FinTech, Volume 3, Issue 1 (March 2024) – 13 articles

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. Full article

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%. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article