FinTech doi: 10.3390/fintech3020014
Authors: Dominic Deuber Jan Gruber Merlin Humml Viktoria Ronge Nicole Scheler
Cryptocurrency forensics have become standard tools for law enforcement. Their basic idea is to deanonymise cryptocurrency transactions to identify the people behind them. Cryptocurrency deanonymisation techniques are often based on premises that largely remain implicit, especially in legal practice. On the one hand, this implicitness complicates investigations. On the other hand, it can have far-reaching consequences for the rights of those affected. Argumentation schemes could remedy this untenable situation by rendering the underlying premises more transparent. Additionally, they can aid in critically evaluating the probative value of any results obtained by cryptocurrency deanonymisation techniques. In the argumentation theory and AI community, argumentation schemes are influential as they state the implicit premises for different types of arguments. Through their critical questions, they aid the argumentation participants in critically evaluating arguments. We specialise the notion of argumentation schemes to legal reasoning about cryptocurrency deanonymisation. Furthermore, we demonstrate the applicability of the resulting schemes through an exemplary real-world case. Ultimately, we envision that using our schemes in legal practice can solidify the evidential value of blockchain investigations, as well as uncover and help to address uncertainty in the underlying premises—thus contributing to protecting the rights of those affected by cryptocurrency forensics.
]]>FinTech doi: 10.3390/fintech3010013
Authors: Tathiana M. Barchi João Lucas Ferreira dos Santos Priscilla Bassetto Henrique Nazário Rocha Sergio L. Stevan Fernanda Cristina Correa Yslene Rocha Kachba Hugo Valadares Siqueira
Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners.
]]>FinTech doi: 10.3390/fintech3010012
Authors: Ly Nguyen Mominul Ahsan Julfikar Haider
Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly predict loan defaults to lessen the damage brought on by defaulters. The goal of this study is to fill the gap in the literature by exploring the feasibility of developing prediction models for P2P loan defaults without relying heavily on personal data while also focusing on identifying key variables influencing borrowers’ repayment capacity through systematic feature selection and exploratory data analysis. Given this, this study aims to create a computational model that aids lenders in determining the approval or rejection of a loan application, relying on the financial data provided by applicants. The selected dataset, sourced from an open database, contains 8578 transaction records and includes 14 attributes related to financial information, with no personal data included. A loan dataset is first subjected to an in-depth exploratory data analysis to find behaviors connected to loan defaults. Subsequently, diverse and noteworthy machine learning classification algorithms, including Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naïve Bayes, and XGBoost, were employed to build models capable of discerning borrowers who repay their loans from those who do not. Our findings indicate that borrowers who fail to comply with their lenders’ credit policies, pay elevated interest rates, and possess low FICO ratings are at a higher likelihood of defaulting. Furthermore, elevated risk is observed among clients who obtain loans for small businesses. All classification models, including XGBoost and Random Forest, successfully developed and performed satisfactorily and achieved an accuracy of over 80%. When the decision threshold is set to 0.4, the best performance for predicting loan defaulters is achieved using logistic regression, which accurately identifies 83% of the defaulted loans, with a recall of 83%, precision of 21% and f1 score of 33%.
]]>FinTech doi: 10.3390/fintech3010011
Authors: Michał Grabowski
The Second Payment Services Directive introduced new services into the European Union legal system—Payment Initiation and Account Information Services. These services are based on payment accounts already opened and maintained for customers by the Account Servicing Payment Service Provider (bank, payment institution, electronic money institution). The Account Services Payment Service provider performs AML/CFT verification of the account holder and applies customer due diligence measures to the account holder, such as identifying beneficial owners, obtaining information on the purpose and intended nature of the business relationship, and ongoing monitoring of the business relationship. Payment Initiation and Account Information services are therefore provided to a previously verified client and based on the payment account currently maintained for him. European Union law does not clearly specify whether a Third-Party Service Provider offering Payment Initiation or Account Information Services is obliged to re-apply financial security measures to customers. The aim of this article was to perform a legal analysis of the regulations and soft law acts in force in the European Union and to answer the question. The purposive (teleological) and linguistic–logical (grammatical) methods of interpretation of regulations were used for the analysis. The structure of the legal system of the European Union as a civil law (code law) system was taken into account. This article shows that in the current legal situation, there is no doubt that Third-Party Service Providers are obliged entities in terms of AML/CFT law and are obliged to apply the AML/CFT to customers using Payment Initiation and Account Information services. However, the degree to which customer due diligence measures have to be applied varies depending on the adopted model of providing Payment Initiation and Account Information services. Third-Party Service Providers will be obliged to apply financial security measures in cases where the relationship between the customer and the service providers will have a continuing character. In the case of occasional provision of services, when the transaction value does not exceed a certain threshold, the supplier may only perform simplified customer verification. In particular, this applies to Payment Initiation service models, where the Payment Initiation Service Provider works for merchants, enabling them to accept payments for goods and services sold. In such a model, the Service Provider has a continuous relationship with the merchant but only performs an occasional transaction for the user. The analysis also allowed for the conclusion that European Union law, including that in the draft phase, does not regulate in a sufficiently precise manner when a given model of Account Services and Payment Initiation Services may be treated as based on an occasional transaction. This made it possible to formulate a de lege ferenda request to include this issue in the proposal for an EU Regulation on the prevention of the use of the financial system for the purposes of money laundering or terrorist financing.
]]>FinTech doi: 10.3390/fintech3010010
Authors: Vijaya Krishna Kanaparthi
This research paper explores the complicated connection between uncertainty and the Markowitz asset allocation framework, specifically investigating how mistakes in estimating parameters significantly impact the performance of strategies during out-of-sample evaluations. Drawing on relevant literature, we highlight the importance of our findings. In contrast to common assumptions, our study systematically compares these approaches with alternative allocation strategies, providing insights into their performance in both anticipated and real-world out-of-sample events. The research demonstrates that incorporating methods to address uncertainty enhances the Markowitz framework, challenging the idea that longer sample periods always lead to better outcomes. Notably, imposing a short-sale constraint proves to be a valuable strategy for improving the effectiveness of the initial portfolio. While revealing the complexities of uncertainty, our study also highlights the surprising resilience of basic asset allocation approaches, such as equally weighted allocation, which exhibit commendable performance. Methodologically, we employ a rigorous out-of-sample evaluation, emphasizing the practical implications of parameter uncertainty on asset allocation outcomes. Investors, portfolio managers, and financial practitioners can use these insights to refine their strategies, considering the dynamic nature of markets and the limitations internal to the traditional models. In conclusion, this paper goes beyond the theoretical scope to provide substantial value in enhancing real-world investment decisions.
]]>FinTech doi: 10.3390/fintech3010009
Authors: Medina Ayta Mohammed Carmen De-Pablos-Heredero José Luis Montes Botella
This study investigates the influence of a country’s financial access and stability and the adoption of retail central bank digital currencies (CBDCs) across 71 countries. Using an ordinal logit model, we examine how individual financial access, the ownership of credit cards, financing accessibility by firms, offshore loans, financial sanctions, and the ownership structure of financial institutions influence the probability of CBDC adoption in nations. These findings reveal that nations facing financial sanctions and those with substantial offshore bank loans are more inclined to adopt CBDCs. Furthermore, a significant relationship is observed in countries where many people have restricted financial access, indicating heightened interest in CBDC adoption. Interestingly, no statistically significant relationship was found between the adoption of CBDCs and the percentage of foreign-owned banks in each country. The results show that countries with low financial stability and financial access adopt CBDCs faster. This study expands our knowledge of how a nation’s financial situation influences its adoption of CBDCs. The results provide important and relevant insights into the current discussion of the direction of global finance.
]]>FinTech doi: 10.3390/fintech3010008
Authors: Julien Riposo Maneesh Gupta
We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a forward approach, we prove that the interest is given by the ratio of the average staking gain to the total staked coins. Some additional PoS features are considered in the model, such as slash rate and Maximal Extractable Value (MEV), which marks the originality of this approach. In particular, we prove that slashing diminishes the rewards, reflecting the fact that the blockchain can consider stakers to potentially validate incorrectly. Regarding MEV, the approach we have sheds light on the relation between transaction fees and the average staking gain. We illustrate the developed model with Ethereum 2.0 and apply a similar process in a Proof-of-Work consensus context.
]]>FinTech doi: 10.3390/fintech3010007
Authors: Raquel M. Gaspar Madalena Oliveira
The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo advisors gather information about clients’ preferences, financial situations, and future goals through questionnaires. Subsequently, they recommend ETF-based portfolios tailored to match the investor’s risk profile. However, these questionnaires often appear vague, and robo advisors seldom disclose the methodologies employed for investor profiling or asset allocation. This study aims to contribute by introducing an investor profiling method relying solely on investors’ relative risk aversion (RRA), which, in addition, allows for the determination of optimal allocations. We also show that, for the period under analysis and using the same ETF universe, our RRA portfolios consistently outperform those recommended by the Riskalyze platform, which may suffer from ultraconservadorism in terms of the proposed volatility.
]]>FinTech doi: 10.3390/fintech3010006
Authors: Stefanos Balaskas Maria Koutroumani Kiriakos Komis Maria Rigou
Financial technology or FinTech is a term that has arisen in recent years; it refers to innovative technologies designed to enhance and automate the provision and utilization of financial services. Its solutions aim to simplify conventional financial procedures, boost automation, lower expenses, and deliver personalized and user-friendly experiences for both businesses and consumers. But this question remains: what drives users to adopt such services and how are they perceived by the general public? In our study, a quantitative non-experimental correlational methodology in the form of an online survey was utilized to study the Greek citizens’ behavioral intentions regarding the utilization of FinTech services. Based on the answers of 348 respondents, structural equation modeling was performed to evaluate the theoretical model, which included technology acceptance factors. Unlike conventional models that primarily relate user acceptance to adoption, our research goes beyond these models by expanding on the TAM model via an exploration of the role of trust and the influence of government support on user trust and perceived effort and an examination of how these, in turn, impact the FinTech services adoption. In our context, government support refers to the regulatory frameworks, policies, and endorsements provided by governmental bodies. The results indicated that all the aspects of this study related to trust and user acceptance (effort expectancy and performance expectancy) revealed a significant and positive relationship with FinTech services adoption and can be predictive factors of citizens’ future intentions to use FinTech services. This study also verified that trust in FinTech services mediates the relationship between government support and FinTech services adoption. We place emphasis on the intricate yet complex decision-making process in technology adoption, particularly in the field of FinTech, by exploring the intertwined relationships of trust, government support, and technology acceptance factors; the findings offer valuable insights for policymakers and industry practitioners.
]]>FinTech doi: 10.3390/fintech3010005
Authors: Peterson K. Ozili David Mhlanga Rym Ammar Marwa Fersi
The lockdown restrictions during the COVID-19 pandemic led to increased interest in Fintech and digital finance solutions, and it gave people an incentive to join the formal financial sector by owning a formal account. People became interested in information about Fintech and digital finance solutions, and it led them to search the Internet to obtain information about Fintech, digital finance, and financial inclusion. In this study, we investigate whether interest in Internet information about Fintech and digital finance led to interest in Internet information about financial inclusion during the COVID-19 pandemic. Using global data that capture interest over time, we found that interest in information about Fintech was greater in developed countries while interest in information about financial inclusion was greater in developing countries during the pandemic. Interest in Fintech information was strongly correlated with interest in financial inclusion information during the pandemic. Interest in Fintech information had a significant positive effect on interest in financial inclusion information during the pandemic. There is a unidirectional causality between interest in Fintech information and interest in financial inclusion information during the pandemic. The implication of these findings is that interest in Fintech information is an important determinant of interest in financial inclusion information.
]]>FinTech doi: 10.3390/fintech3010004
Authors: Jan René Judek
The process of decision-making is increasingly supported by algorithms in a wide variety of contexts. However, the phenomenon of algorithm aversion conflicts with the development of the technological potential that algorithms bring with them. Economic agents tend to base their decisions on those of other economic agents. Therefore, this experimental approach examines the willingness to use an algorithm when making stock price forecasts when information about the prior adoption of an algorithm is provided. It is found that decision makers are more likely to use an algorithm if the majority of preceding economic agents have also used it. Willingness to use an algorithm varies with social information about prior weak or strong adoption. In addition, the affinity for technological interaction of the economic agents shows an effect on decision behavior.
]]>FinTech doi: 10.3390/fintech3010003
Authors: Ata Larijani Farbod Dehghani
Many intrusion detection algorithms that use optimization have been developed and are commonly used to detect intrusions. The process of selecting features and the parameters of the classifier are essential parts of how well an intrusion detection system works. This paper provides a detailed explanation and discussion of an improved intrusion detection method for multiclass classification. The proposed solution uses a combination of the modified teaching–learning-based optimization (MTLBO) algorithm, the modified JAYA (MJAYA) algorithm, and a support vector machine (SVM). MTLBO is used with supervised machine learning (ML) to select subsets of features. Selection of the fewest features possible without impairing the accuracy of the results in feature subset selection (FSS) is a multiobjective optimization issue. This paper presents MTLBO as a mechanism and investigates its algorithm-specific, parameter-free idea. This study used the modified JAYA (MJAYA) algorithm to optimize the C and gamma parameters of the support vector machine (SVM) classifier. When the proposed MTLBO-MJAYA-SVM algorithm was compared with the original TLBO and JAYA algorithms on a well-known intrusion detection dataset, it was found to outperform them significantly.
]]>FinTech doi: 10.3390/fintech3010002
Authors: Anton Miglo
This paper offers a game-theoretic model of a firm that raises funds for financing an innovative business project and chooses between ICO (initial coin offering) and equity financing. The model is based on information problems associated with both ICO and equity financing well-documented in the literature. Several new features are introduced, for example, information complexity, which is analyzed along with a more traditional imperfect information and an asymmetric information approach. The model provides several implications that have not yet been tested. For example, we find that the message complexity can be beneficial for firms conducting ICOs. Also, high-quality projects can use ICO as a signal of quality. Thirdly, the average size of projects undertaking equity financing is larger than that of firms conducting ICO.
]]>FinTech doi: 10.3390/fintech3010001
Authors: Sungida Rashid
According to the National Financial Inclusion Strategy (NFIS), Bangladesh aims to achieve a 100% financial inclusion target by 2026 through mobile financing services (MFSs). However, despite several efforts, the financial inclusion score remained only 53% at the end of 2021, compared to 50% in 2017. A substantial proportion of this growth came through MFSs during the COVID-19 pandemic. This article investigates the short-run and long-run influence of COVID-19 movement restriction orders on MFSs. An autoregressive distributed lag model (ARDL) is applied to the monthly transaction data over the period of December 2016 to May 2022 of the three most popular MFSs. Movement restriction orders are associated with a significant increase in person-to-person transactions (P2P) and person-to-business transactions (P2B) in the long run, but the effect is positive and statistically insignificant for remittance transfer. Furthermore, using the volume of ATM transactions as a measure of financial inclusion, this study confirms the crucial role of movement restriction orders in intensifying the financial inclusion of Bangladesh through MFSs. The coefficients of error correction models (ECM) indicate that policymakers must act promptly to develop actionable strategies to maintain the short run momentum of the demand for MFSs to achieve the national target.
]]>FinTech doi: 10.3390/fintech2040040
Authors: Lea Kocjancic Sergej Gricar
Successful organisations prioritise product quality and customer satisfaction. Non-financial indicators are crucial for measuring performance, requiring specific financial and technology management knowledge. Effective knowledge management and entrepreneurial activity significantly impact performance, vital to the country’s economic factors. Electricity is crucial to society’s development. Renewable energy sources such as solar, wind, hydropower, and biomass can generate sustainable electricity. Managing environmental, social, and economic aspects is essential for sustainable societal and virtual development. In this study, the central element of novelty is associated with the dependent variable Nominal Labour Productivity per Employee. This research shows that effective knowledge management impacts a company’s business performance. Based on secondary data from various sources, we have used factor analysis to assess the interrelationship between the factors and econometric dimensionalities. Accompanied by this econometric approach, the research methodology aims to present hybrid models based on econometric techniques and artificial intelligence (AI) networks. Based on the principal component method analysis results, we show the interdependence of 30 variables in the micro and macro environment. The new components of the correlated variables show how knowledge and innovation are related to the economic performance of society, and nominal employee productivity is a valuable indicator for measuring economic efficiency. Nevertheless, AI, a knowledge management product, provides helpful comments on the econometric results.
]]>FinTech doi: 10.3390/fintech2040039
Authors: Progress Choongo Mungu Chileshe Christine Nakamba Lesa Bruce Mwiya Thomas Kweku Taylor
The purpose of this study is to determine the relationship between the leadership styles of leaders of financial technology (Fintech) start-ups and firm growth. A quantitative design employing a cross-sectional survey with the use of a Likert questionnaire was conducted on the leaders of top-performing Fintech firms in Zambia, as recognized by Tracxn in its May 2020 report. This study focuses on three leadership styles: transformational leadership, transactional leadership, and laissez-faire leadership. The most significant result is that transformational leadership is strongly associated with the growth of Fintech start-ups in Zambia, while transactional leadership plays a limited role. The association between laissez-faire leadership and firm growth is positive but weak. The research makes two main contributions to the literature in the field of Fintech. First, the findings can help researchers explain leadership styles that predict the growth of Fintech start-ups. Second, founders of Fintech firms will understand the most important leadership styles that can lead to the growth of start-ups. The limitations of this study relate to the sample size, the need to consider other readership styles, and the use of qualitative and longitudinal designs that would provide more insights and validation.
]]>FinTech doi: 10.3390/fintech2040038
Authors: Kazi Abdul Mannan Khandaker Mursheda Farhana
Globally, large numbers of adults remain unbanked, and most of them live in rural areas of the Third World. The recent outbreak of the COVID-19 pandemic has shown us how inequalities in accessing financial services continue to affect us. However, digital financial inclusion has emerged as an effective tool used to tackle socioeconomic ills and drive economic development. In fact, due to these modern technological developments, the number of studies in this area is very limited, especially in the context of developing economies. This study examines the impacts of migrant remittances on digital financial inclusion within households in Bangladesh by using the Migration and Remittance Household Survey. To meet the research objectives of this study, a household survey was conducted and 2165 households interviewed in 2022–2023 in Bangladesh. The survey data collected was tested using univariate and multivariate estimations. This study finds that the coefficient of remittance has positive relationships with the probability of e-bank accounts and the use of mobile banking for a household’s financial transactions. However, the use of ATM cards by households for financial transactions has not been significantly affected. The article concludes that remittance flows may enhance access to and use of means of digital financial inclusion by reducing some of the barriers and costs in Bangladesh, which could greatly contribute to the country’s economic growth by creating and increasing a strong fund for investment. The findings of this study can help in taking various steps to facilitate the most powerful financial sector of Bangladesh, namely, remittance management.
]]>FinTech doi: 10.3390/fintech2040037
Authors: Nai Chiek Aik Qiurui Zhang
This study uses panel data from 2016 to 2020 to examine the impact of digital financial inclusion on income inequality in the urban-rural divide of Chongqing, China. The results suggest that increasing access to digital financial services could help narrow the income gap between urban and rural areas. However, the impact becomes significantly positive when controlling for other variables with the Random Effects regression model. Among the control variables, the urbanization rate and government expenditure are found to be significant determinants of income inequality in Chongqing. These findings offer insights for policymakers on the potential benefits of targeted interventions to promote financial inclusion and sustainable urbanization, while ensuring effective allocation of government spending to reduce income inequality.
]]>FinTech doi: 10.3390/fintech2030036
Authors: Md. Abdul Bashir Md. Alaul Haque Aidin Salamzadeh Md. Mizanur Rahman
The banking sectors are optimistic that electronic banking (E-banking) will help them provide better customer service and strengthen customer relationships. Despite this, a relatively low priority has been given to the level of satisfaction that E-banking users in Bangladesh have regarding the quality of the services they receive and their overall experiences. Consequently, this study aims to determine the effect of service quality and customer experiences on the level of satisfaction perceived by E-banking customers in Bangladesh. Using a convenience sampling technique and a self-administered questionnaire, we gathered data from 315 E-banking customers. The independent variable (service quality and customer experience) and dependent variable (customer satisfaction) on a five-point “Likert-Type Scale” explain the degree to which participants agree or disagree with the questionnaire’s statements. Covariance-based structural equation modeling (CB-SEM) was utilised to analyse the gathered data. The findings of this study indicate that service quality and customer experience significantly positively affect E-banking customer satisfaction in Bangladesh. The outcomes of this study will urge the banking authorities to prioritize service quality to boost customer satisfaction by suggesting several steps to improve the efficiency, effectiveness, and security of the E-banking system.
]]>FinTech doi: 10.3390/fintech2030035
Authors: Mohammad Rakibul Islam Bhuiyan K. M. Salah Uddin Md Noor Uddin Milon
The objective of this study is to assess the current and future potential of the digital economy in Bangladesh, with the goal of fostering national development and prosperity by the year 2041. Concurrently, this study examines the various aspects of the digital economy through the lens of the Fourth Industrial Revolution and emerging technologies, specifically focusing on the utilization of information and communication technology (ICT) in Bangladesh. The methodology section employs a qualitative approach to ascertain the research objectives, utilizing secondary data. The purpose of this study is to provide an overview of the contemporary status of the digital economy, focusing on emerging trends that have a significant impact on the national gross domestic product (GDP). Companies and individuals possess an understanding of the digital economy, which has the potential to mitigate the digital divide and establish a robust connection between technology and the economy. The research contributes to a more thorough understanding that Bangladesh is ranked 40th out of 193 nations at present; with the advancement of the digital economy, it will move up to 24th place in 2034. Future research can perhaps be expanded by adopting a qualitative methodology to explore the concept of a smart Bangladesh.
]]>FinTech doi: 10.3390/fintech2030034
Authors: Alexandra Lefevre Agnes Tourin
This paper examines the integration of climate risks into structural credit risk models. We focus on applications in housing finance and argue that mortgage defaults due to climate disasters have different statistical features than default due to household-specific reasons. We propose two models incorporating climate risk based on two separate default definitions. The first focuses on default as a response to a decrease in home value, and the second defines default as a consequence of missed mortgage payments. Using mortgage performance data during Hurricane Harvey, we conduct an empirical study whose results suggest that climate events are potentially another source of undiversifiable credit risk affecting homeowners’ ability to make contractual monthly payments. We also show that incorporating this climate-specific default process may capture additional uncertainty in default probability assessments.
]]>FinTech doi: 10.3390/fintech2030033
Authors: Aurel Burciu Rozalia Kicsi Simona Buta Mihaela State Iulia Burlac Denisa Alexandra Chifan Beatrice Ipsalat
This study aims to assess and identify the role of disruptive/digital technologies in financial innovation strategies as part of social innovations at both the firm and country level. The analysis proposed by the present study brings useful theoretical/pragmatic insights on the application of financial technologies in the context of the “fintech” revolution, as a disruptive innovation. There are few studies of this type that “cross-examine” technical/social innovative capacity at the firm level vs. the same innovative capacity at the level of the world’s major countries. Our proposed study brings some novel elements to the literature on this topic. First, the study synthesizes the factors/variables explaining technical/social innovative capacity as ranked by the GCI (Global Competitiveness Index) and GII (Global Innovation Index) at the country level and then correlates informal/empirical variables with the factors explaining innovative capacity for the 50 companies in the BCG (Boston Consulting Group) ranking. Second, the study identifies three “driving forces” (digital technologies, managers, and the market) as the main variables determining financial innovativeness (fintech revolution) at the firm level. Third, based on the “over-cross assessment” (non- statistical) of the information/data provided by the BCG study vs. the GII and GCI studies, the study suggests some ways to delineate and quantify financial innovation as part of social innovation (e.g., it is argued that up to 80% of the social innovation achieved annually by a firm relates to the financial relationships engaged by the firm with various categories of stakeholders). Finally, the study is also important from a pragmatic point of view as it suggests/proposes a number of principles that can be considered by managers for building a KM (knowledge management) and continuous financial innovation strategy. From a theoretical perspective, the study provides a starting point for further research aimed at explaining firm-level financial innovation (fintech as a disruptor) through the massive use of disruptive technologies.
]]>FinTech doi: 10.3390/fintech2030032
Authors: Ana Carla Magalhães Nascimento Nathália de Kassia Galdino Oliveira Verônica de Menezes Nascimento Nagata Reimison Moreira Fernandes Vitor William Batista Martins
Background: The post-COVID-19 scenario has demonstrated the increasing importance of marketing for organizations, as retailers and entrepreneurs have had to adapt to new ways of selling their products and services. In this regard, this research aimed to identify challenges for developing the marketing plan of startups and validate them from the perspective of managers in the field, considering the market characteristics inherent to the post-COVID-19 era; Methods: To achieve this, a literature review and a survey were conducted among professionals in the field. The collected data were analyzed using the quantitative Lawshe method. Results: The results indicate that, for the development of the marketing plan of startups considering the post-COVID-19 reality, it is important to prioritize overcoming the challenges of “Consumer behavior pattern change”, “Differentiation from the competition”, “Digital expansion”, “Innovation capacity of companies”, “Creation of transformative marketing”, and “Reevaluation of marketing channels in the post-pandemic period”; Conclusions: Therefore, it can be concluded that these challenges reflect the main concerns and obstacles faced by startups in building effective marketing strategies and striving for a competitive position in the market. By recognizing and understanding these challenges, startups will be better prepared to face adversity and seize opportunities in this new market context.
]]>FinTech doi: 10.3390/fintech2030031
Authors: Voicu D. Dragomir Valentin Florentin Dumitru
The Markets in Crypto-Assets (MiCa) Regulation of the European Union is the first comprehensive piece of legislation that seeks to protect the interests of investors in the crypto-assets sector. Although the market value of crypto-assets is significant at world level, there is a lack of clear regulatory guidelines regarding the recognition, measurement, and presentation of crypto-assets in the financial statements of investors. Considering that not all digital assets are the same, retail holders need to take into account the characteristics, rights, and obligations associated with the crypto-assets they purchase to determine the appropriate accounting method. Therefore, the research question of the present article is: Which are the main types of crypto-assets and how should they be recognized and measured in the financial statements of investors and holders? We perform a review of the accounting policies and options, relying on relevant regulations, standards, regulatory drafts, legal and academic papers, recommendations of market regulators, crypto-asset white papers, industry opinions, and media articles. There are different accounting treatments that can be applied, depending on the legal and technological aspects of each class of crypto-assets. Based on a critical discussion of accounting policies and options, our research has implications for accounting professionals, but also for standard setters, who are urged to provide clear guidelines. Identifying the key economic characteristics of each asset and determining the most appropriate way to recognize these characteristics in the financial statements are crucial for the development of a functional and trustworthy market in crypto-assets.
]]>FinTech doi: 10.3390/fintech2030030
Authors: Hiranya Dissanayake Catalin Popescu Anuradha Iddagoda
This study presents a comprehensive bibliometric analysis of research on financial technology (FinTech) as a methodology. The aim is to unveil the research landscape, trends, and influential factors within this rapidly evolving field. By examining publication records, citation patterns, and thematic maps, valuable insights into the intellectual structure and impact of FinTech research are provided. The analysis highlights the increasing research output and global interest in FinTech, identifies key contributors and knowledge hubs driving the field, and uncovers emerging research themes such as blockchain technology, digital payments, robo-advisors, peer-to-peer lending, and regulatory frameworks. This analysis serves as a roadmap for researchers, industry professionals, and policymakers, offering guidance for navigating the vast body of FinTech research, identifying research gaps, and fostering collaborations to drive innovation in the financial industry. Overall, this bibliometric analysis contributes to a better understanding of the current state of FinTech research and provides valuable insights for future research endeavors and decision-making in the field.
]]>FinTech doi: 10.3390/fintech2030029
Authors: Bernhard Koelmel Max Borsch Rebecca Bulander Lukas Waidelich Tanja Brugger Ansgar Kuehn Matthias Weyer Luc Schmerber Michael Krutwig
This paper focuses on quantifying the economic and financial viability of NB-IoT and LoRaWAN technologies, two low-power wide-area network (LPWAN) technologies with unique characteristics that make them suitable for IoT applications. The purpose of this study is to propose a “pragmatic” artifact for performing life cycle cost analysis and demonstrate its application to these technologies. The methodology uses pragmatic computational tools to facilitate the analysis and considers all relevant economic and financial factors, such as operating costs, equipment costs, and revenue potential. The main finding of this study is that Narrow Band-Internet of Things (NB-IoT) and Long Range Wide Area Network (LoRaWAN) technologies have different cost structures and revenue potentials, which may affect their economic and financial viability for different IoT applications. Ultimately, the study concludes that a comprehensive life cycle cost analysis is critical to making informed decisions about technology adoption, and that the proposed methodology can be applied to other IoT technologies to gain insight into their economic and financial viability.
]]>FinTech doi: 10.3390/fintech2030028
Authors: Morshadul Hasan Ariful Hoque Thi Le
At present, with the rise of information technology revolution, such as mobile internet, cloud computing, big data, machine learning, artificial intelligence, and the Internet of Things, the banking industry is ushering in new opportunities and encountering severe challenges. This inspired us to develop the following research concepts to study how data innovation impacts banking. We used qualitative research methods (systematic and bibliometric reviews) to examine research articles obtained from the Web of Science and SCOPUS databases to achieve our research goals. The findings show that data innovation creates opportunities for a well-developed banking supply chain, effective risk management and financial fraud detection, banking customer analytics, and bank decision-making. Also, data-driven banking faces some challenges, such as the availability of more data increasing the complexity of service management and creating fierce competition, the lack of professional data analysts, and data costs. This study also finds that banking security is one of the most important issues; thus, banks need to respond to external and internal cyberattacks and manage vulnerabilities.
]]>FinTech doi: 10.3390/fintech2030027
Authors: Sonia Kherbachi
The digital economy has revolutionized industries worldwide, prompting companies to invest in digital technologies to enhance productivity and profitability. However, the successful implementation of these technologies hinges on employees’ perceptions and satisfaction with the digital infrastructure. This paper aims to explore the impact of digital technology satisfaction on overall job satisfaction within the fintech domain. Drawing from the User-Task-Technology fit framework, it investigates the interplay between digital technology satisfaction, job satisfaction, and work-life balance. By aligning technology with task requirements and individual user needs, organizations can foster a positive work environment and improve firm performance. The study employs Principal Component Analysis (PCA) to identify key requirements for the digital economy in a digital environment. Furthermore, it addresses two research questions related to the selection of variables representing sustainability dimensions and evaluating dependency in digital economy projects under a fintech scope. The findings highlight the importance of digital technology satisfaction in driving employee job satisfaction and overall work experience. Ultimately, this research contributes to a deeper understanding of the factors influencing the digital economy and offers insights for managers and organizations seeking to optimize their digital transformation strategies. The study concludes by exploring the digital economy in the context of healthcare services in Africa, specifically focusing on the initiatives led by the World Bank.
]]>FinTech doi: 10.3390/fintech2030026
Authors: Vijaya Krishna Kanaparthi
Accounts Payable (AP) is a time-consuming and labor-intensive process used by large corporations to compensate vendors on time for goods and services received. A comprehensive verification procedure is executed before disbursing funds to a supplier or vendor. After the successful conclusion of these validations, the invoice undergoes further processing by traversing multiple stages, including vendor identification; line-item matching; accounting code identification; tax code identification, ensuring proper calculation and remittance of taxes, verifying payment terms, approval routing, and compliance with internal control policies and procedures, for a comprehensive approach to invoice processing. At the moment, each of these processes is almost entirely manual and laborious, which makes the process time-consuming and prone to mistakes in the ongoing education of agents. It is difficult to accomplish the task of automatically processing these invoices for payment without any human involvement. To provide a solution, we implemented an automated invoicing system with modules based on artificial intelligence. This system processes invoices from beginning to finish. It takes very little work to configure it to meet the specific needs of each unique customer. Currently, the system has been put into production use for two customers. It has handled roughly 80 thousand invoices, of which 76 percent were automatically processed with little or no human interaction.
]]>FinTech doi: 10.3390/fintech2030025
Authors: Edgar Cambaza
This narrative review explores the potential of FinTech in promoting sustainable healthcare development in Sub-Saharan Africa (SSA), focusing on the role of blockchain, crowdfunding, digital payments, and machine learning. The review also highlights the potential barriers to FinTech adoption in SSA, including limited access to technology, regulatory challenges, and cultural factors, and proposes potential solutions, such as capacity building and increased financial investment. Additionally, the review discusses the ethical and social implications of FinTech in healthcare development, including privacy, data security, equity, and accessibility. The main findings suggest that FinTech has the potential to significantly improve healthcare delivery and financing in SSA, particularly in the areas of information sharing, healthcare financing, and healthcare delivery models. However, addressing the barriers to FinTech adoption and mitigating the ethical and social implications will be essential to realizing the full potential of FinTech in healthcare development in the region. The review recommends future research and development in this area, and highlights the potential for FinTech to promote sustainable and equitable healthcare development in SSA.
]]>FinTech doi: 10.3390/fintech2030024
Authors: Haris Alibašić
The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.
]]>FinTech doi: 10.3390/fintech2030023
Authors: Woo Jae Byun Bumkyu Choi Seongmin Kim Joohyun Jo
Although deep reinforcement learning (DRL) has recently emerged as a promising technique for optimal trade execution, two problems still remain unsolved: (1) the lack of a generalized model for a large collection of stocks and execution time horizons; and (2) the inability to accurately train algorithms due to the discrepancy between the simulation environment and real market. In this article, we address the two issues by utilizing a widely used reinforcement learning (RL) algorithm called proximal policy optimization (PPO) with a long short-term memory (LSTM) network and by building our proprietary order execution simulation environment based on historical level 3 market data of the Korea Stock Exchange (KRX). This paper, to the best of our knowledge, is the first to achieve generalization across 50 stocks and across an execution time horizon ranging from 165 to 380 min along with dynamic target volume. The experimental results demonstrate that the proposed algorithm outperforms the popular benchmark, the volume-weighted average price (VWAP), highlighting the potential use of DRL for optimal trade execution in real-world financial markets. Furthermore, our algorithm is the first commercialized DRL-based optimal trade execution algorithm in the South Korea stock market.
]]>FinTech doi: 10.3390/fintech2030022
Authors: Dian Palupi Restuputri Figo Bimaraka Refoera Ilyas Masudin
In recent years, cryptocurrency has increased in popularity in Indonesia. In Indonesia, based on data from the Ministry of Trade (Kemendag), until the end of May 2021, the number of investors in cryptocurrency assets or crypto money was 6.5 million people. This number has increased by more than 50 percent when compared to 2020 when there were 4 million people. The Pintu application is the first crypto mobile application in Indonesia that is committed to solving crypto investment problems, especially for beginners and ordinary people. Even though it provides benefits, investing in cryptocurrency can provide high profits. In an instant, it can also make a profit. The motion, which is like a roller coaster, requires strong mental readiness to invest in cryptocurrencies. This should also be a critical consideration for investors, especially young investors. Therefore, it is necessary to understand what factors contribute to building stronger attitudes and behavioral intentions toward the PINTU application. This research analyzes the data using the use of technology 2 method with the partial least square (PLS) analysis technique method, which will later be processed in the form of data results in the form of responses of the user when using the application. Facilitating conditions and social influence are the most influential indicators. The results of the study show that behavioral intention to adopt has a relationship with behavioral intention to recommend, and behavioral intention to adopt positively and significantly influences the intention to recommend.
]]>FinTech doi: 10.3390/fintech2030021
Authors: Luiz Antonio Bueno Tiago F. A. C. Sigahi Rosley Anholon
Digital banks have profoundly changed the financial industry’s operations. In this scenario, the study of digital banks has gained increasing attention in the academic community. However, there is still a lot of room to understand how this type of organization functions and impacts different contexts. Considering the information collected, partial findings, and the professional experience of those involved in a larger research project, the main objective of this study is to present the Brazilian scenario related to digital banks from the analytical perspective of the research group. The methodological approach included analysis of partial results of a larger research project, bibliographic research, analysis of public data about digital banks in Brazil, and multidisciplinary discursive approach to conduct debates with the support of academic literature and experience from top managers working in major Brazilian financial institutions. Data on key performance indicators (KPIs), including cost breakdown, net revenue, return on equity (ROE), and cost-to-income ratio, are presented and analyzed for both traditional and digital banks. Furthermore, this study puts forward potential avenues for future research within three main research domains: digital operational efficiency for banks, customer attraction strategies employed by digital banks, and the utilization of digital financial services in the retail industry.
]]>FinTech doi: 10.3390/fintech2020020
Authors: Lance Decker Ben Zoghi
Cash movements between banks and customers are often conducted through armored courier services. These armored couriers are hesitant to adopt new technologies because the business’s nature requires well-documented custody transfers of cash bags. Often, these transfers are still based on paper receipts. The researchers believe that using radio frequency identification (RFID) and an application programming interface (API) between all parties in the cash management ecosystem reduces cost, improves efficiency, and increases capacity. To alleviate the hesitancy of armored couriers, a simulation model is made that operates much like an existing 45-vehicle branch. Once the model was validated, changes were made to the model to adopt the API interfaces and RFID systems required. In addition, an RFID-based sorting robot was implemented. A comparison focused on the workforce utilization of armored vehicle crews and branch tellers. As expected, the resulting model significantly reduces staffing requirements, improves efficiency, and increases capacity. The operational behaviors of tellers were reduced by 79%, and truck route durations were reduced by 43%. The expectation is that this research will help armored couriers see the advantages of adopting such a system and spur additional investigation of the solution. Finally, the cost of the system and operational savings were put into a return on investment/payback period calculation, revealing an annual savings of approximately USD 2.2 million after a one-year payback period.
]]>FinTech doi: 10.3390/fintech2020019
Authors: Godfred Anakpo Zizipho Xhate Syden Mishi
Globally, over 1.4 billion adult people remain unbanked. This worrisome phenomenon was exacerbated by the outbreak of the COVID-19 pandemic, which further created a new dimension of inequality in accessing financial services. Digital financial inclusion promises to be an effective tool for addressing this socioeconomic ill and propelling economic development. Given the limited studies on the subject in the context of developing economies, it is imperative to understand the existing policies, practices, and barriers to digital financial inclusion in developing economies so as to provide cutting-edge interventions for redress. It is against this background that this study seeks to address the following research questions: (1) What is the state of digital financial inclusion in the developing economy? (2) What are the policies and practices regarding digital financial inclusion in the developing economy? (3) What are the barriers to digital financial inclusion and innovative interventions for redress? Findings reveal that about 44% of the adult population in developing countries does not have access to financial services, with only a few countries that have made significant progress and gains through policy and practice, such as mobile financial services, mobile money interoperability, native connectivity, human capital development, and the digitalization of public services for digital financial inclusion. Our findings also identify challenges and implications with recommendations, which are discussed in detail in this paper.
]]>FinTech doi: 10.3390/fintech2020018
Authors: Sunhilde Cuc
The textile and fashion industry is on the brink of a major disruption, and blockchain technology (BT) presents a promising solution that could transform the industry by facilitating supply chain transparency, traceability, and sustainability. This article explores the potential of BT in the textile and fashion industry, with a focus on its current applications and potential impact. Using case studies and analyzing all announced blockchain projects from January 2017 to January 2023, we examine the diversity of blockchain applications across different aspects of the textile and fashion industry, including smart contracts and payment processing, supply chain tracking, sustainability applications, and customer engagement. The findings suggest an increasing number of companies are adopting BT, and that BT has the potential to revolutionize the T and F industry by creating a more transparent and efficient supply chain, reducing fraud and counterfeiting, and increasing customer confidence in products. We also identified the challenges and difficulties that may arise during the implementation of BT. This article contributes to the literature on BT in the textile and fashion industry, providing critical insights into its potential impact.
]]>FinTech doi: 10.3390/fintech2020017
Authors: Dora Almeida Andreia Dionísio Paulo Ferreira Isabel Vieira
Extraordinary events, regardless of their financial or non-financial nature, are a great challenge for financial stability. This study examines the impact of one such occurrence—the COVID-19 pandemic—on cryptocurrency markets. A detrended cross-correlation analysis was performed to evaluate how the links between 16 cryptocurrencies were changed by this event. Cross-correlation coefficients that were calculated before and after the onset of the pandemic were compared, and the statistical significance of their variation was assessed. The analysis results show that the markets of the assessed cryptocurrencies became more integrated. There is also evidence to suggest that the pandemic crisis promoted contagion, mainly across short timescales (with a few exceptions of non-contagion across long timescales). We conclude that, in spite of the distinct characteristics of cryptocurrencies, those in our sample offered no protection against the financial turbulence provoked by the COVID-19 pandemic, and thus, our study provided yet another example of ‘correlations breakdown’ in times of crisis.
]]>FinTech doi: 10.3390/fintech2020016
Authors: Tao Zhu Xinyu Sun
In recent years, the growth rate of China’s real industry has slowed down while the financial industry has entered a phase of rapid development. Driven by the profit-seeking motive of capital, real enterprises tend to carry out financial investments, and the degree of corporate financialization has been rising. This paper selects A-share listed enterprises in Shanghai and Shenzhen from 2009 to 2020 as research samples to study the impact of corporate financialization on technological innovation and the mediating effect of financing constraints from the perspective of financial asset holding. The study found that the financialization of enterprises’ crowding out effect on technological innovation has led to the phenomenon of “turning from real to virtual”. We also found that the crowding-out effect had experienced lag. This conclusion still held when we controlled for endogeneity. The heterogeneity analysis showed that the financialization of non-state-owned enterprises had an excessive inhibitory effect on technological innovation, and the financialization of enterprises in eastern China has had a remarkable inhibitory effect on technological innovation. The influence mechanism analysis showed how financing constraints played a crucial mediating role in corporate financialization inhibiting technological innovation, and corporate financialization has inhibited technological innovation by exacerbating financing constraints. Based on this research, we propose targeted suggestions to prevent the excessive financialization of enterprises on both government and enterprise levels.
]]>FinTech doi: 10.3390/fintech2020015
Authors: Fábio Albuquerque Paula Gomes Dos Santos
Accounting has been evolving to follow the latest economic, political, social, and technological developments. Therefore, there is a need for researchers to also include in their research agenda the emerging topics in the accounting area. This exploratory paper selects technological matters in accounting as its research object, proposing a literature review that uses archival research as a method and content analysis as a technique. Using different tools for the assessment of qualitative data, this content analysis provides a summary of those papers, such as their main topics, most frequent words, and cluster analysis. A top journal was used as the source of information, namely The International Journal of Accounting Information Systems, given its scope, which links accounting and technological matters. Data from 2000 to 2022 was selected to provide an evolutive analysis since the beginning of this century, with a particular focus on the latest period. The findings indicate that the recent discussions and trending topics in accounting, including matters such as international regulation, the sustainable perspective in accounting, as well as new methods, channels, and processes for improving the entities’ auditing and reporting, have increased their relevance and influence, enriching the debate and future perspectives in combination with the use of new technologies. Therefore, this seems to be a path to follow as an avenue for future research. Notwithstanding, emerging technologies as a research topic seem to be slower or less evident than their apparent development in the accounting area. The findings from this paper are limited to a single journal and, therefore, this limitation must be considered in the context of those conclusions. Notwithstanding, its proposed analysis may contribute to the profession, academia, and the scientific community overall, enabling the identification of the state of the art of literature in the technological area of accounting.
]]>FinTech doi: 10.3390/fintech2020014
Authors: Simon Gluzman
The general framework for quantitative technical analysis of market prices is revisited and extended. The concept of a global time-translation invariance and its spontaneous violation and restoration is introduced and discussed. We find that different temporal patterns leading to some famous crashes (e.g., bubbles, hockey sticks, etc.) exhibit analogous probabilistic distributions found only in the time series for the stock market indices. A number of examples of crashes are presented. We stress that our goal here is to study the crash as a particular phenomenon created by spontaneous time-translation symmetry breaking/restoration. We ask only “how to calculate and interpret the probabilistic pattern which we encounter in the day preceding crash, and how to calculate the typical market reactions to shock?”.
]]>FinTech doi: 10.3390/fintech2020013
Authors: Faten Aisyah Ahmad Ramli Muhammad Iskandar Hamzah Siti Norida Wahab Rishabh Shekhar
The proliferation of digital payments has paved the way for the greater use of E-wallets or mobile payments in over-the-counter (OTC) retail transactions. Nevertheless, given its economic and accessibility benefits over NFC forms of mobile payment, relatively little is known about QR-code E-wallet (QREW) adoption from the consumer–brand relationship perspective. The study aims to address this knowledge void by augmenting brand equity elements (perceived value, brand image, and brand awareness) to comprehensively analyze consumers’ QREW usage intention in the OTC retail environment. A structural equation modeling analysis was performed on 305 consumers in the greater Klang Valley, Malaysia. The empirical findings suggest that brand awareness positively affects QREW usage intention and mediates the effects of both perceived quality and brand image on the outcome. Moreover, the results reveal a serial mediation effect involving all of the examined factors. Theoretically, this study supplements the literature on mobile payments from the consumer–brand relationship view, in which the predictive nature of brand equity factors is examined separately. In practical terms, considering that the Malaysian market QREW is in a relatively early growth stage, the findings should offer QREW providers insights into how to capitalize on brand equity mechanisms for attracting consumers to utilize their offerings.
]]>FinTech doi: 10.3390/fintech2010012
Authors: Che-Pin Chen Kai-Wen Huang Yung-Chi Kuo
This paper defines Conditional Token (CT) as the token with specific conditions and proposes the use functions for its operations in smart contract so that it can be deployed at the public blockchain. If CTs were exchanged to/equivalent to fiat currency once then all conditions are realized, that is, the required performances and obligations/rights are agreed upon. In use, the obligation-type CT can be used as a divisible mortgage or be used as a representation of accounts receivable, accounts payable and vouchers as it is used in accounting. While the rights-type CT can be used as divisible fixed-income bonds or as an investment vehicle. Integrate both types of CTs with a matching methodology can thus be used in any kind of peer-to-peer (P2P) system of the decentralized finance, such as crowdfunding and P2P lending. This paper thus applying this new model to solve the complex issues of supply chain finance. For feasibility, this study concludes CT is the “Verdinglichung Obligatorischer Rechte”, and CTs are better than the current corporate loans in terms of cost and benefits. In addition, it is capable of transferring risk to other investors. In terms of implementation, this paper proposes a system framework and has completed a proof of concept of the system.
]]>FinTech doi: 10.3390/fintech2010011
Authors: Man-Fai Leung Lewis Chan Wai-Chak Hung Siu-Fung Tsoi Chun-Hin Lam Yiu-Hang Cheng
The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visualization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals.
]]>FinTech doi: 10.3390/fintech2010010
Authors: Ioannis Kosmas Theofanis Papadopoulos Georgia Dede Christos Michalakelis
Artificial intelligence (AI) is an extensive scientific field, part of which is the concept of deep learning, belonging to broader family of machine learning (ML) methods, based on artificial neural networks (ANNs). ANNs are active since the 1940s and are applied in many fields. There have been actions around the world for the digital transformation of the public sector and the use of new innovative technologies, but the trajectory and degree of adoption of artificial intelligence technologies in the public sector have been unsatisfactory. Similar issues must be handled, and these problems must be classified. In the present work, preparatory searches were made on Scopus and IEEE bibliographic databases in order to obtain information for the progress of the adoption of ANNs in the public sector starting from the year 2019. Then, a systematic review of published scientific articles was conducted using keywords. Among the 2412 results returned by the search and the application of the selection/rejection criteria, 10 articles were chosen for analysis. The conclusion that emerged after reading the articles was that while the scientific community has a lot of suggestions and ideas for the implementation of ANNs and their financial effects, in practice, there is no appropriate use of them in the public sector. Occasionally, there are cases of implementation funded by state or non-state bodies without a systematic application and utilization of these technologies. The ways and methods of practical application are not further specified, so there are no indications for the systematic application of specialized deep learning techniques and ANNs. The legal framework for the development of artificial intelligence applications, at least in the European Union (EU), is under design, like the necessary ISO standards from an international perspective, and the economic impact of the most recent AI-based technologies has not been fully assessed.
]]>FinTech doi: 10.3390/fintech2010009
Authors: Matheus Camilo da Silva Gabriel Marques Tavares Marcos Cesar Gritti Paolo Ceravolo Sylvio Barbon Junior
In the context of online banking, new users have to register their information to become clients through mobile applications; this process is called digital onboarding. Fraudsters often commit identity fraud by impersonating other people to obtain access to banking services by using personal data obtained illegally and causing damage to the organisation’s reputation and resources. Detecting fraudulent users by their onboarding process is not a trivial task, as it is difficult to identify possible vulnerabilities in the process to be exploited. Furthermore, the modus operandi for differentiating the behaviour of fraudulent actors and legitimate users is unclear. In this work, we propose the usage of a process mining (PM) approach to detect identity fraud in digital onboarding using a real fintech event log. The proposed PM approach is capable of modelling the behaviour of users as they go through a digital onboarding process, while also providing insight into the process itself. The results of PM techniques and the machine learning classifiers showed a promising 80% accuracy rate in classifying users as fraudulent or legitimate. Furthermore, the application of process discovery in the event log dataset produced an insightful visual model of the onboarding process.
]]>FinTech doi: 10.3390/fintech2010008
Authors: Md. Mominur Rahman
The purpose of the study is to examine the effects of business intelligence on the bank’s operational efficiency and perceptions of profitability. The study is based on 259 responses from 27 branches of a commercial bank, employing a simple random sampling technique. This research uses the partial least square- structural equation method (PLS-SEM) method to test the hypotheses. The study verifies construct’s reliability and construct’s validity of the measurement model, and tests the fitness of the structural model. The study finds that business intelligence is positively associated with operational efficiency and profitability. Further, the study reveals that operational efficiency through business intelligence positively affects bank’s profitability. Based on competitive theory, this research states that business intelligence allows the productive entity to generate superior margins compared to its market rivals. Thus, banks can offer better options more cheaply than their rivals and thereby ensure competitive advantage. Further, based on resource-based view theory, the study argues that business intelligence as a strategic resource can provide the foundation to develop bank capabilities that can lead to superior performance over time. Therefore, the study implies business intelligence application in the banking companies and helps decision-making effectiveness for the management body of banks, academics, and policymakers.
]]>FinTech doi: 10.3390/fintech2010007
Authors: Chung-Yim Yiu Ka-Shing Cheung
Conventional real estate education emphasises the application of knowledge from various disciplines. While this approach has its merits, its efficacy is affected by the stage of development of the discipline referenced. A notable case in point is the adoption of financial technologies (or FinTech) in real estate. How we prepare our next generation with creative thinking skills, an innovation mindset, and a risk-taking attitude to embrace the rapid transformation to an innovation-based economy is therefore critical. In this study, we advocate that the case study method is an effective teaching pedagogy that enables students to learn from analysing real cases and applying knowledge from a complex discipline in real estate. The method motivates students to acquire new knowledge to establish new practices and theories in innovative applications, such as FinTech, in real estate. This study provides a teaching reflection on adopting the case study method in an undergraduate Property Technology (PropTech) course. Students are required to use real business cases to analyse how FinTech is solving real estate problems. Discussions with lecturers and peer reviews in the online discussion forum enable students to wrestle with the knowledge they learn and encourage an atmosphere of knowledge co-creation.
]]>FinTech doi: 10.3390/fintech2010006
Authors: Mohan Khanal Sudip Raj Khadka Harendra Subedi Indra Prasad Chaulagain Lok Nath Regmi Mohan Bhandari
The most significant and rapidly expanding fintech services in Nepal are provided by several fintech firms. Customer satisfaction must be compared side by side even if every organization has made an effort to expand the usage of services. Many studies have concentrated on evaluating the impact of various factors on customer satisfaction, but significantly fewer studies have been conducted to explore the factors and focus of machine learning. Based on the planned behavioural theory (TPB), the study is concentrated on exploring and evaluating customer satisfaction on a different stimulus offered by F1 Soft (a fintech firm in nepal), customers’ loyalty and the compatibility they gain through the company’s services. By exploring various factors affecting customer satisfaction by using principal component analysis (PCA) and explainable AI (XAI), the study explored the eight factors (customer service, compatibility, ease of use, assurance, loyalty intention, technology perception, speed and firm’s innovativeness) which affect customer satisfaction individually. Furthermore, by using support vector machine (SVM) and logistic regression (LR), the major contributing factors are explained with local interpretable model-agnostic explanation (LIME) and Shapley additive explanations (SHAP). SVM holds the training accuracy of 89.13% whereas LR achieves 87.88%, and both algorithms show that compatibilty issues consider the major contributing factor for customer satisfaction. Contributing toward different dimensions, determinants, and the results of customer satisfaction in fintech, the study suggests how fintech companies must integrate factors affecting customer satisfaction in their system for further process development.
]]>FinTech doi: 10.3390/fintech2010005
Authors: <i>FinTech</i> Editorial Office <i>FinTech</i> Editorial Office
High-quality academic publishing is built on rigorous peer review [...]
]]>FinTech doi: 10.3390/fintech2010004
Authors: Samuel Asante Gyamerah Janet Arthur Saviour Worlanyo Akuamoah Yethu Sithole
Longevity is without a doubt on the rise throughout the world due to advances in technology and health. Since 1960, Ghana&rsquo;s average annual mortality improvement has been about 1.236%. This poses serious longevity risks to numerous longevity-bearing assets and liabilities. As a result, this research investigates the effect of mortality improvement on pension annuities related to a particular pension scheme in Ghana. Different stochastic mortality models (Lee&ndash;Carter, Renshaw&ndash;Haberman, Cairns&ndash;Blake&ndash;Dowd, and Quadratic Cairns&ndash;Blake&ndash;Dowd) are used to forecast mortality improvements between 2021 and 2030. The results from accuracy metrics indicate that the quadratic Cairns&ndash;Blake&ndash;Dowd model exhibits the best fit to the mortality data. The findings from the study demonstrate that mortality for increasing ages within the retirement period was declining, with increasing improvement associated with increasing ages. Furthermore, the forecasts were used to estimate the associated single benefit annuity for a GHS 1 per annum payment to pensioners, and it was discovered that the annuity value expected to be paid to such people was not significantly different regardless of the pensioner&rsquo;s current age.
]]>FinTech doi: 10.3390/fintech2010003
Authors: Lennart Ante Ingo Fiedler Jan Marius Willruth Fred Steinmetz
This study reviews the current state of empirical literature on stablecoins. Based on a sample of 22 peer-reviewed articles, we analyze statistical approaches, data sources, variables, and metrics, as well as stablecoin types investigated and future research avenues. The analysis reveals three major clusters: (1) studies on the stability or volatility of different stablecoins, their designs, and safe-haven-properties, (2) the interrelations of stablecoins with other crypto assets and markets, specifically Bitcoin, and (3) the relationship of stablecoins with (non-crypto) macroeconomic factors. Based on our analysis, we note future research should explore diverse methodological approaches, data sources, different stablecoins, or more granular datasets and identify five topics we consider most significant and promising: (1) the use of stablecoins in emerging markets, (2) the effect of stablecoins on the stability of currencies, (3) analyses of stablecoin users, (4) adoption and use cases of stablecoins outside of crypto markets, and (5) algorithmic stablecoins.
]]>FinTech doi: 10.3390/fintech2010002
Authors: Egi Arvian Firmansyah Masairol Masri Muhammad Anshari Mohd Hairul Azrin Besar
The rise of financial technology (fintech) has been one of the substantial changes in the financial landscape driven by technological advancements and the global financial crisis. This paper employs the systematic literature review (SLR) technique to review recent literature on fintech adoption or acceptance employing the Scopus database (2019&ndash;2022). The final reviewed documents are sixteen journal articles published by various journals from different country contexts and theoretical backgrounds. Several inclusion criteria were used to filter those selected documents. One crucial criterion is the journal continuity in the Scopus index, which assures the quality of the published scholarly works. This criterion selection is expected to represent this paper&rsquo;s novelty. The study reveals various determinants derived from the theories used by the fintech researchers. However, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are the most used theoretical foundations. Additionally, trust, financial literacy, and safety are other factors developed by previous researchers and are significant determinants of fintech adoption. Besides, these results suggest that future studies on fintech adoption develop a genuine construct since fintech keeps progressing, and so does the customers&rsquo; behavior.
]]>FinTech doi: 10.3390/fintech2010001
Authors: Monday Osagie Adenomon Richard Adekola Idowu
This study provides evidence of the impact of COVID-19 on five (5) Nigerian Stock Exchange (NSE) sectorial stocks (NSE Insurance, NSE Banking, NSE Oil and Gas, NSE Food and Beverages, and NSE Consumer Goods). To achieve the goal of this paper, daily stock prices were obtained from a secondary source ranging from 2 January 2020 to 25 March 2021. Because of the importance of incorporating structural breaks in modelling stock returns, the Zivot&ndash;Andrews unit root test revealed 20 January 2021, 26 March 2020, 27 July 2020, 23 March 2020 and 23 March 2020 as potential break points for NSE Insurance, NSE Food, Beverages and Tobacco, NSE Oil and Gas, NSE Banking, and NSE Consumer Goods, respectively. This study investigates the volatility in daily stock returns for the five (5) Nigerian Stock Exchange (NSE) sectorial stocks using nine versions of GARCH models (sGARCH, girGARCH, eGARCH, iGARCH, aPARCH, TGARCH, NGARCH, NAGARCH, and AVGARCH); in addition, the half-life and persistence values were obtained. The study used the Student t- and skewed Student t-distributions. The results from the GARCH models revealed a negative impact of COVID-19 on the NSE Insurance, NSE Food, Beverages and Tobacco, NSE Banking, and NSE Consumer Goods stock returns; however, the NSE Oil and Gas returns showed a positive correlation with the COVID-19 pandemic. This study recommends that the shareholders, investors, and policy players in the Nigerian Stock Exchange markets should be adequately prepared in the form of diversification of investment in stocks that can withstand future possible crises in the market.
]]>FinTech doi: 10.3390/fintech1040031
Authors: Ahmet F. Aysan Zhamal Nanaeva
The present-day financial system is being influenced by the rapid development of Fintech (financial technology), which comprises technologies created to improve and automate traditional forms of finance for businesses and consumers. The topic of Fintech as a financial disruptor is gaining popularity in line with the swift spread of digitalization across the banking industry, whereby this paper contributes to the field by presenting a novel bibliometric analysis of the academic literature related to Fintech as a financial disruptor. The analysis is based on metadata extracted from the Scopus database through the VOSviewer and Biblioshiny software. The bibliometric analysis of 363 documents identifies the most impactful sources of publication, keywords, authors, and most cited documents on the topic of Fintech as a financial disruptor. As our analysis demonstrates, the number of publications on the given topic is increasing, indicating both interest among academia and potential for future research.
]]>FinTech doi: 10.3390/fintech1040030
Authors: Yuechu Hu Jong-Min Kim
The COVID-19 pandemic ravaged the world, not only threatening people&rsquo;s health but also impacting various industries. This paper will focus on the impact of the pandemic on the music industry, specifically on live and recorded music. To help determine how the COVID-19 pandemic has impacted both live and recorded music, we will analyze the log-returns of stock data of three companies representative of the music industry: Live Nation Entertainment, Tencent Music Entertainment, and Warner Music Group. We also provide descriptive statistics related to the log-returns of stock data of the three companies and calculate the correlation coefficients of the log returns for these companies using three correlation methods (Pearson correlation test, Kendall correlation test, and Spearman correlation) before and after the pandemic. From stock price charts, we observed a negative relationship between the stock indices of both live and recorded music during the early pandemic period. However, we found that there was no correlation in the log-returns of both live and recorded music company stocks after the COVID-19 vaccination became widely available, despite their being a slight positive correlation from the results.
]]>FinTech doi: 10.3390/fintech1040029
Authors: Ibrahim Musa Unal Ahmet Faruk Aysan
The increasing interest in Fintech, Blockchain, and Digitalization in Islamic Finance created a new area in the literature, requiring a systematic review of these academic publications. The scope of the analysis is limited to journal articles to understand the trends in the indexed journals. Results are categorized into three sections, Islamic banks&rsquo; digitalization, Blockchain and Crypto Assets research, and Islamic non-bank financial institutions&rsquo; digitalization. Islamic fintech has great potential mainly because of the overlapping norms of Shariah and fintech, making it easier to implement technological disruption into Islamic finance. Moreover, the trust shift to Islamic finance could be merged with the opportunities of fintech and increase the potential of Islamic fintech even more.
]]>FinTech doi: 10.3390/fintech1040028
Authors: Biruk Birhanu Ashenafi Yan Dong
Financial inclusion and Fintech have revolutionized the financial sector and fundamentally changed how we store, save, borrow, transfer, and invest money. This paper investigates the impact of financial inclusion and Fintech on income inequality using waves of survey data for 2011, 2014, and 2017 across 39 African countries. By using pooled ordinary least square and two-stage least square (2sls) estimation methods, we obtain three key findings. First, institutional factors such as political stability, control of corruption, and government effectiveness determine Fintech and financial inclusion. Second, Fintech encourages individuals to have a formal bank account, thereby promoting financial inclusion. Third, financial inclusion and Fintech exacerbate income inequality. The direct implication of our findings is that policymakers make tradeoffs whether they seek to achieve higher inclusion and Fintech or to reduce income inequality. We highlight that a pro-poor financial sector development is vital. Easing the bottleneck in obtaining loans, offering agriculture-based Fintech services, and improving digital literacy are important steps to gain the most out of inclusion and Fintech in reducing income inequality.
]]>FinTech doi: 10.3390/fintech1040027
Authors: Ahmed Antwi-Boampong David Boison Musah Doumbia Afia Boakye Linda Osei-Fosua Kwame Owiredu Sarbeng
The study evaluated factors influencing port users’ intentions to participate in Financial Technology (Fintech) in the ports of Ghana. The study used non-experimental quantitative correlational design and the Extended Unified Theory of the Acceptance and Use of Technology (UTAUT2) as the theoretical foundation to assess whether performance expectancy (PE), behavioral intention (BI), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), price value (PV), and habit (HT) were predictors of the intention of port users to participate in a Fintech program with age as a moderating factor. The sample comprised 407 individuals who work in the port industry and are between 18 and 64 years old; these were randomly selected through the SurveyMonkey platform. The study used principal component analysis (PCA), confirmatory factor analysis, and structural equation modeling to analyze and report the results. Findings show that PE, EE, and HT were predictors of the behavioral intention of port users to participate in a Fintech in the maritime and ports in Ghana. FC, SI, HM, and PV values could not predict BI for port users to enroll on a Fintech program. Neither did age have a moderating effect on the predictors variable influence on behavioral intention. This study offers a deeper insight into the adoption of Fintech in the port industry and sub-Saharan Africa. The findings can help researchers explain the variations in the UTAUT2 theoretical framework predictions relative to different sectors and disciplines. Researchers who intend to use the UTAUT2 theoretical framework to influence port users BI to enroll in the Fintech program will now consider PE, EE, and HT the most effective adoption factors. From a practical perspective, the study will help managers and stakeholders in ports in Ghana and sub-Saharan Africa focus on the critical constructs as the first steps to implementing a Fintech program. On the other side, port users will also understand their role relative to performance expectancy, effort expectancy, and the habit to cultivate toward Fintech.
]]>FinTech doi: 10.3390/fintech1040026
Authors: Kilian Wenker
The fast-growing, market-driven demand for cryptocurrencies worries central banks, as their monetary policy could be completely undermined. Central bank digital currencies (CBDCs) could offer a solution, yet our understanding of their design and consequences is in its infancy. This non-technical paper examines how The Bahamas has designed the Sand Dollar, the first real-world instance of a retail CBDC. It contrasts the Sand Dollar with definition-based specifications. The author then develops a scenario analysis to illustrate commercial bank risks. In this process, the central bank becomes a deposit monopolist, leading to high funding risks, disintermediation risks, and solvency risks for the commercial banking sector. This paper argues that restrictions and caps will be the new specifications of a regulatory framework for CBDCs if disintermediation in the banking sector is to be prevented. The anonymity of CBDCs is identified as a comparative disadvantage that will affect their adoption. These findings provide insight into governance problems facing central banks and coherently lead to the design of the Sand Dollar. This paper concludes by suggesting that combating cryptocurrencies is a task that cannot be solved by a CBDC.
]]>FinTech doi: 10.3390/fintech1040025
Authors: David Cerezo Sánchez
The latest developments in blockchain technology have conceptualised very efficient consensus protocols that have not yet been able to overcome older technologies. This paper presents Pravuil, a robust, secure, and scalable consensus protocol for a permissionless blockchain suitable for deployment in an adversarial environment such as the Internet. Using zero-knowledge authentication techniques, Pravuil circumvents previous shortcomings of other blockchains: Bitcoin&rsquo;s limited adoption problem (as transaction demand grows, payment confirmation times grow much less than that of other PoW blockchains); higher transaction security at a lower cost; more decentralisation than other permissionless blockchains; impossibility of full decentralisation; the blockchain scalability trilemma (decentralisation, scalability, and security can be achieved simultaneously); and Sybil resistance for free implementation of the social optimum. Pravuil goes beyond the economic limits of Bitcoin and other PoW/PoS blockchains, leading to a more valuable and stable cryptocurrency.
]]>FinTech doi: 10.3390/fintech1040024
Authors: Pritham Pattamatta Shaunak S. Dabadghao
Point of Sale (PoS) or Buy-Now-Pay-Later (BNPL) type financing has observed a strong rise in the last decade, and accounts for about 10% of the unsecured lending market in the USA. This market is largely covered by FinTechs, and traditional financial institutions are beginning to demonstrate interest in entering this market. In this short communication, we identify why the POS market is attractive and what drives its growth, examine the market participants, and illustrate the various ways in which financial institutions can enter this market.
]]>FinTech doi: 10.3390/fintech1040023
Authors: Éder Pereira Paulo Ferreira Derick Quintino
Non-fungible tokens (NFTs) are a type of digital record of ownership used in a unique way: ensuring authenticity and uniqueness. Due to these characteristics, NFTs have been used in several markets: games, arts, and sports, among others. In 2020, the volume of negotiations of the NFTs was about USD 200 million. Despite the strong interest of economic agents in operating with NFTs, there are still gaps in the literature, regarding their dynamics and price interrelation with other potentially related assets, which deserve to be studied. In this sense, the main purpose in this paper is to analyze the cross-correlation between NFTs and larger cryptocurrencies. To this end, our methodological approach is based on a Detrended Cross-Correlation Analysis correlation coefficient, with a sliding windows approach. Our main finding is that the cross-correlations are not significant, except for a few cryptocurrencies, with weak significance at some moments of time. We also carried out an analysis of the long-term memory of NFTs, which demonstrated the antipersistence of these assets, with results seemingly corroborating the market inefficiency hypothesis. Our results are particularly important for different classes of investors, due to the analysis on different time scales.
]]>FinTech doi: 10.3390/fintech1040022
Authors: Nada Mallah Boustani Magnaghi Elisabetta
As the Fourth Industrial Revolution gains momentum and involves a plethora of disruptive technology concepts, such as blockchain, they have infiltrated economies that have only experienced a small portion of their scope, consequences, and applications in their different branches. This research aims to examine the potential uses of blockchain technology within the framework of smart contracts in the insurance sector, notably in the event of a pandemic that results in business interruption. Businesses hardly ever take business interruption insurance into account, particularly in a country similar to Lebanon, where natural disasters and pandemics are scarce. Due to the complexity of the task and the numerous requirements for trust in terms of risk consistency, traditional insurance companies are not interested in offering these kinds of insurance contracts. In this current study, a quantitative study was conducted over 213 businesses in various fields and revealed acceptance and socio-demographic differences in the activity sectors of this potentially ground-breaking solution for a developing country that is undergoing a sanitary and economic crisis. As a result, smart contracts and decentralized finance (DeFi) were proposed in the current research as potential solutions to overcome the Lebanese currency devaluation and high insurance costs.
]]>FinTech doi: 10.3390/fintech1030021
Authors: Anton Miglo
This paper analyzes a financing problem for an innovative firm that is considering launching a web-based platform. The model developed in the paper is the first one that analyzes an entrepreneur&rsquo;s choice between initial exchange offering (IEO) and initial coin offering (ICO). Compared to ICO, under IEO the firm is subject to screening by an exchange that reduces the risk of investment in tokens; also the firm receives access to a larger set of potential investors; finally tokens become listed on an exchange faster. The paper argues that IEO is a better option for the firm if: (1) the investment size is relatively large; (2) the extent of moral hazard problems faced by the firm is relatively large; (3) the degree of investors&rsquo; impatience is relatively small. Furthermore, a non-linear relationship between firm quality and its financing choice is found. Most of these predictions are new and have not been tested so far.
]]>FinTech doi: 10.3390/fintech1030020
Authors: Po-Han Ko Yu-Ling Hsueh Chih-Wen Hsueh
Nowadays, blockchain bloat is an endangering issue caused by inefficient transaction storage mechanisms. Based on the Distributed File System (DFS), the blockchain network can reduce the local storage to solve the blockchain bloat problem. However, storing all blocks on DFS is not durable or scalable. Hence, classifying blocks into hot and cold was adopted in previous works. The blockchain nodes can reduce the time consumption and storage consumption by storing hot blocks locally. However, the previous works are not able to periodically check block integrity and do not provide a reward mechanism to encourage DFS system nodes to store blocks. We extend previous works based on the InterPlanetary File System (IPFS) and design an innovative scheme to incentivize IPFS nodes. The IPFS nodes are regulated with smart contracts and behave under the pricing strategy controls to increase profit. By adopting proof of retrievability, we guarantee the integrity of the blocks. Further, the redundant scheme extends our pricing strategy to improve the durability of our proposed framework. A load-balancing pricing strategy and a general pricing strategy are provided in the framework to reward the DFS nodes. Extensive experiments are presented to demonstrate that the latency and throughputs of our model are competitive, while still maintaining data integrity in the system. The additional increased throughput takes only 0.167% of that produced by the original Bitcoin and the upload latency takes only 6.67% of the mining time of the Bitcoin Mainnet. Furthermore, our load-balancing pricing strategy achieves the effectiveness to ensure the redundancy of blocks and reduces the overall storage consumption up to 97% using the load-balancing pricing strategy, compared to the non-load-balancing pricing strategy.
]]>FinTech doi: 10.3390/fintech1030019
Authors: Kwami Ahiabenu
This paper discusses critical considerations in the design of central bank digital currency (CBDC) in West Africa through a comparative case study of Ghana&rsquo;s (eCedi) and Nigeria&rsquo;s (eNaira) design frameworks. This paper analyses CBDC design options framed through context (digital payment landscape and CBDC objectives), technical aspects (design principles, architecture, risks), use cases, and deployment plans. This study conducted a thematic analysis of official CBDC design documents to identify similarities, differences, and patterns. The results indicate more similarities between the eCedi and eNaira designs than differences. Differences were observed in the CBDC deployment context, risk profiles, and plans. Surprisingly, neither country has articulated the detailed legal and regulatory environments for CBDC. This paper highlights the use of CBDC designs to promote citizens&rsquo; welfare by using financially inclusive policy goals within central banking&rsquo;s welfare functions, thereby extending their traditional role. Policymakers should focus on adaptive legal and policy design outlooks to address uncertainties associated with CBDC. This paper is important because it is one of the first to contribute to a detailed comparison of Ghana and Nigeria&rsquo;s CBDC design frameworks.
]]>FinTech doi: 10.3390/fintech1030018
Authors: Paul P. Momtaz
The Metaverse refers to a shared vision among technology entrepreneurs of a three-dimensional virtual world, an embodied internet with humans and the physical world in it. As such, the Metaverse is thought to expand the domain of human activity by overcoming spatial, temporal, and resource-related constraints imposed by nature. The technological infrastructure of the Metaverse, i.e., Web3, consists of blockchain technology, smart contracts, and Non-Fungible Tokens (NFTs), which reduce transaction and agency costs, and enable trustless social and economic interactions thanks to decentralized consensus mechanisms. The emerging Metaverse may give rise to new products and services, new job profiles, and new business models. In this brief note, I assess the promises and challenges of the Metaverse, offer a first empirical glimpse at the emerging Metaverse economy, and discuss some simple Metaverse economics that revolve around building and operating the Metaverse.
]]>FinTech doi: 10.3390/fintech1030017
Authors: Lennart Ante
Non-fungible tokens (NFTs) are transferrable rights to digital assets, such as art, in-game items, collectables, or music. The phenomenon and its markets have grown significantly since early 2021. We investigate the interrelationships between NFT sales, NFT users (unique active blockchain wallets), and the pricing of Bitcoin (BTC) and Ether (ETH). Using daily data between January 2018 and April 2021, we show that a Bitcoin price shock triggers an increase in NFT sales. Also, Ether price shocks reduce the number of active NFT wallets. The results suggest that (larger) cryptocurrency markets affect the growth and development of the (smaller) NFT market, but there is no reverse effect.
]]>FinTech doi: 10.3390/fintech1020016
Authors: Ahmet Faruk Aysan Abdelilah Belatik Ibrahim Musa Unal Rachid Ettaai
As new digitalization strategies storm the banking industry, banks which are behind the technological curve may struggle to keep pace. This is a well-known challenge in the Islamic banking sector in particular; however, this research shows that little is being done in order to achieve unified digitalization in operations. The 2020 Global Islamic Bankers Survey (GIBS) from CIBAFI sought opinions and data from 101 Islamic banks, which outlined both their institutions&rsquo; adoption of financial technology and their awareness of existing technologies. In addition, several technology trends&mdash;such as AI, machine learning, DLTs, and P2P lending&mdash;were analyzed separately in order to understand how they may be implemented within Islamic banking. This paper performed different statistical procedures to answer these research questions via correlation analysis and one-way ANOVA. The data were compiled and analyzed using SPSS software. In doing so, this study clarified the perspective of Islamic banks on digital transformation and answered whether Islamic banks are taking the right direction in terms of their digitalization strategies. Interestingly, most newly developing technologies have a low implementation level in Islamic banking operations globally, with the exception of mobile banking, which already has a vast global infrastructure. The results may serve as a warning to Islamic banks to invest more capital and energy in the developing fields of financial technologies in order to keep abreast of their conventional banking counterparts.
]]>FinTech doi: 10.3390/fintech1020015
Authors: Aichih Chang Nesreen El-Rayes Jim Shi
Firms are eager to adopt new technologies, such as Artificial Intelligence (A.I.), Cloud Computing, Big Data, etc., as they witness successful business applications. As one of the disruptive technologies, Blockchain technology (BCT) has been drawing attention stemming from cryptocurrency proliferation (e.g., Bitcoin and Ethereum), for which Blockchain serves as the backbone. However, the public is haunted by the bewilderment between cryptocurrencies and BCT. Furthermore, the burgeoning of Metaverse and non-fungible tokens (NFT) has raised BCT to another notch. This study conducts a holistic literature review on BCT features, implementations, and business implications. In particular, by reviewing and analyzing 2265 up-to-date articles that reveal BCT’s applications across various fields, this Blockchain-centered study reveals the research status and delineates future research directions. It is shown that, among various characteristics of BCT, traceability is the main characteristic fueling BCT’s application in supply chain management (SCM). We further find that the BCT-related research has been extremely growing in SCM, healthcare, and government, while declining in the areas of banking and cyber security. Geographically, the top countries with BCT-related publications are China, U.S., and India. Finally, it is emphasized that BCT-related research in environmental sciences and agriculture have potential to be explored.
]]>FinTech doi: 10.3390/fintech1020014
Authors: David Liu An Wei
This research aims to study the pricing risks of options by using improved LSTM artificial neural network models and make direct comparisons with the Black&ndash;Scholes option pricing model based upon the option prices of 50 ETFs of the Shanghai Securities Exchange from 1 January 2018 to 31 December 2019. We study an LSTM model, a mathematical option pricing model (BS model), and an improved artificial neural network model&mdash;the regulated LSTM model. The method we adopted is first to price the options using the mathematical model&mdash;i.e., the BS model&mdash;and then to construct the LSTM neural network for training and predicting the option prices. We further form the regulated LSTM network with optimally selected key technical indicators using Python programming aiming at improving the network&rsquo;s predicting ability. Risks of option pricing are measured by MSE, RMSE, MAE and MAPE, respectively, for all the models used. The results of this paper show that both the ordinary LSTM and the traditional BS option pricing model have lower predictive ability than the regulated LSTM model. The prediction ability of the regulated LSTM model with the optimal technical indicators is superior, and the approach adopted is effective.
]]>FinTech doi: 10.3390/fintech1020013
Authors: Nicola Dimitri
In the paper we investigate consensus formation, from an economic perspective, in a Proof-of-Stake (PoS) based platform inspired by the Algorand blockchain. In particular, we consider PoS in relation to governance, focusing on two main issues. First we discuss alternative sampling schemes, which can be adopted to select voting committees and to define the number of votes of committee members. The selection probability is proportional to one&rsquo;s stake and increases with it. Participation in governance allows users to affect the platform&rsquo;s decisions as well as to obtain a reward. Then, based on such preliminary analysis, we introduce a microeconomic model to investigate the optimal stake size for a generic user. In the model we conceptualize an optimal stake, for a user, as striking the balance between having Algos immediately available for transactions and setting aside currency units to increase the probability of becoming a committee member. Our main findings suggest that the optimal stake can be quite sensitive to the user&rsquo;s preferences and to the rules for selecting committees. We believe the findings may support policy decisions in PoS based platforms.
]]>FinTech doi: 10.3390/fintech1020012
Authors: Shuli Lv Yangran Du Yong Liu
The rapid development of Fintechs has brought opportunities and challenges to the profitability of banks. In this paper, we theoretically expound how Fintechs impact on banks&rsquo; profitability, then we establish the Error Correction Model (ECM) and combine this with the Granger causal relation test based on the data of the Industrial and Commercial Bank of China (ICBC) in 2011&ndash;2020. The research results show the following findings: (1) banks&rsquo; profitability (ROE) has a cooperative relationship with the development of Fintechs (FTI), banks&rsquo; assets (TA), the profitability of interest-bearing assets (NIM), credit risks (NPL) and cost control (CTI). (2) Fintechs have a &ldquo;U&rdquo;-shaped impact on the banks&rsquo; profitability. In the initial stages, Fintechs impact the business of banks, which reduces the profitability of banks; the advantages of Fintechs gradually increase in the middle and later stages, and the profitability gradually increases. (3) The assets of banks (TA) and the profitability of interest-bearing assets (NIM) change in the same direction as banks&rsquo; profitability (ROE), while credit risks (NPL) and cost control (CTI) change in the opposite direction from ROE. (4) The level of bank profitability and the development of Fintechs are Granger causes of each other, the size of the bank&rsquo;s assets is the Grange reason for the increase in profitability and the increase in profitability is the Granger cause for the improvement of NIM and the decline in NPL.
]]>FinTech doi: 10.3390/fintech1020011
Authors: Luckshay Batra Harish Chander Taneja
Information theoretic measures were applied to the study of the randomness associations of different financial time series. We studied the level of similarities between information theoretic measures and the various tools of regression analysis, i.e., between Shannon entropy and the total sum of squares of the dependent variable, relative mutual information and coefficients of correlation, conditional entropy and residual sum of squares, etc. We observed that mutual information and its dynamical extensions provide an alternative approach with some advantages to study the association between several international stock indices. Furthermore, mutual information and conditional entropy are relatively efficient compared to the measures of statistical dependence.
]]>FinTech doi: 10.3390/fintech1020010
Authors: David Roubaud
On behalf of the editorial board, reviewers, and authors of the journal, I am very much looking forward to interacting with the FinTech research and practice communities to share their latest research results through this new platform [...]
]]>FinTech doi: 10.3390/fintech1020009
Authors: Daniel Felix Ahelegbey
We study the sensitivity of stock returns to the tail risk of major equity market indices, including the G10 countries. We model the sensitivity relationship via extreme downside hedging and estimate the parameters via a Bayesian graph structural learning method. The empirical application examines whether downside risk connections among the major stock markets are merely anecdotal or provide a signal of contagion and the nature of sensitivity among major equity markets during the global financial crisis and the coronavirus pandemic. The result showed that the COVID-19 crisis recorded the historically highest spike in the downside risk interconnectedness among the major equity market indices, suggesting higher financial market vulnerability in the coronavirus pandemic than during the global financial crisis.
]]>FinTech doi: 10.3390/fintech1020008
Authors: Leonard Loh Hee Kueh Nirav Parikh Harry Chan Nicholas Ho Matthew Chua
Algorithmic trading has become the standard in the financial market. Traditionally, most algorithms have relied on rule-based expert systems which are a set of complex if/then rules that need to be updated manually to changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading because it can directly learn market patterns and behaviors from historical trading data and factor this into trading decisions. In this paper, a complete end-to-end system is proposed for automated low-frequency quantitative trading in the foreign exchange (Forex) markets. The system utilizes several State of the Art (SOTA) machine learning strategies that are combined under an ensemble model to derive the market signal for trading. Genetic Algorithm (GA) is used to optimize the strategies for maximizing profits. The system also includes a money management strategy to mitigate risk and a back-testing framework to evaluate system performance. The models were trained on EUR–USD pair Forex data from Jan 2006 to Dec 2019, and subsequently evaluated on unseen samples from Jan 2020 to Dec 2020. The system performance is promising under ideal conditions. The ensemble model achieved about 10% nett P&amp;L with −0.7% drawdown level based on 2020 trading data. Further work is required to calibrate trading costs &amp; execution slippage in real market conditions. It is concluded that with the increased market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to a changing market environment will become even stronger.
]]>FinTech doi: 10.3390/fintech1010007
Authors: Chih-Wen Hsueh Chi-Ting Chin
Since FinTech was stimulated by the invention of blockchain, without the full realization of blockchain technologies in the following years, FinTech has not been fully realized. We discuss some myths and reasons for why blockchain technologies were not fully realized. The lack of distributed synchronization might be the most difficult challenge such that the trust provided by blockchain is not good enough for public use. We propose a mathematical solution with a new consensus mechanism based on general Proof-of-Work mining, called Proof-of-PowerTimestamp, to reach distributed synchronization and reduce power consumption to less than one billionth of Bitcoin. We also discuss related issues toward blockchain realization once the distributed synchronization and energy consumption problems are solved. Since the issues are mostly interdisciplinary or multidisciplinary, researchers are invited to cooperate to help blockchain realization as soon as possible.
]]>FinTech doi: 10.3390/fintech1010006
Authors: Alex Gramegna Paolo Giudici
Feature selection is a popular topic. The main approaches to deal with it fall into the three main categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose to integrate an explainable AI approach, based on Shapley values, to provide more accurate information for feature selection. We test our proposal in a real setting, which concerns the prediction of the probability of default of Small and Medium Enterprises. Our results show that the integrated approach may indeed prove fruitful to some feature selection methods, in particular more parsimonious ones like LASSO. In general the combination of approaches seems to provide useful information which feature selection algorithm can improve their performance with.
]]>FinTech doi: 10.3390/fintech1010005
Authors: Daniel Felix Ahelegbey Paolo Giudici Fatemeh Mojtahedi
The aim of this paper is to propose a portfolio selection methodology capable to take into account asset tail co-movements as additional constraints in Markowitz model. We apply the methodology to the observed time series of the 10 largest crypto assets, in terms of market capitalization, over the period 20 September 2017&ndash;31 December 2020 (1200 daily observations). The results indicate that the portfolios selected considering tail risk are more diversified and, therefore, more resilient to financial shocks.
]]>FinTech doi: 10.3390/fintech1010004
Authors: Sanjib Kumar Nayak Sarat Chandra Nayak Subhranginee Das
Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.
]]>FinTech doi: 10.3390/fintech1010003
Authors: Hong Bao David Roubaud
Non-Fungible Token (NFT) has risen rapidly since 2020 and has become one of the most popular applications in the Fintech field [...]
]]>FinTech doi: 10.3390/fintech1010002
Authors: Adebayo Felix Adekoya Isaac Kofi Nti Benjamin Asubam Weyori
An accurate prediction of the Exchange Rate (ER) serves as the basis for effective financial management, monetary policies, and long-term strategic decision making worldwide. A stable and competitive ER enables economic diversification. Economists, researchers, and investors have conducted several studies to predict trends and facts that influence the ER&rsquo;s rise or fall. This paper used the Long Short-Term Memory Networks (LSTM) framework to predict the weekly exchange rate of one Ghanaian Cedis (GH&#8373;) to three different currencies (United States Dollar, British Pound, and Euro), using Google Trends and historical macroeconomic data. We fused past exchange rates, fundamental macroeconomic variables, commodity prices (cocoa, gold, and crude oil) and public search queries (Google Trends) as input parameters. An empirical analysis using publicly available ER data from the Bank of Ghana (BoG) from January 2004 to October 2019 showed satisfactory results. We observed that the proposed LSTM model outperformed the Support Vector Regressor (SVR) and Back-propagation Neural Network (BPNN) models in accuracy and closeness metrics. That is, our LSTM model obtained (MAE = 0.033, MSE = 0.0035, RMSE = 0.0551, R2 = 0.9983, RMSLE = 0.0129 and MAPE = 0.0121) compared with SVR (MAE = 0.05, MAE = 0.005, RMSE = 0.0683, R2 = 0.9973, RMSLE = 0.0191 and MAPE = 0.0241) and BPNN (MAE = 0.04, MAE = 0.0056, RMSE = 0.0688, R2 = 0.9974, RMSLE = 0.0172 and MAPE = 0.0168). Moreover, we observed a strong positive correction (0.98&ndash;0.99) between Google Trends on the currency of focus and its exchange rate to the Ghanaian cedis. The study results show the importance of incorporating public search queries from search engines to predict the ER accurately.
]]>FinTech doi: 10.3390/fintech1010001
Authors: Junzo Watada Nureize Binti Arbaiy Qiuhong Chen
Goal programming (GP) can be thought of as an extension or generalization of linear programming to handle multiple, normally conflicting objective measures. Each of these measures is given a goal or target value to be achieved. Unwanted deviations from this set of target values are then minimized in an achievement function. Production planning is an important process that aims to leverage the resources available in industry to achieve one or more business goals. However, the production planning that typically uses mathematical models has its own challenges where parameter models are sometimes difficult to find easily and accurately. Data collected with various data collection methods and human experts&rsquo; judgments are often prone to uncertainties that can affect the information presented by quantitative results. This study focuses on resolving data uncertainties as well as multi-objective optimization using fuzzy random methods and GP in production planning problems. GP was enhanced with fuzzy random features. Scalable approaches and maximum minimum operators were then used to solve multi-object optimization problems. Scaled indices were also introduced to resolve fuzzy symbols containing unspecified relationships. The application results indicate that the proposed approach can mitigate the characteristics of uncertainty in the analysis and achieve a satisfactory optimized solution.
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