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FinTech
Journal Description
FinTechis an international,peer-reviewed, open access journalon a variety of themes connected with financial technology, such as cryptocurrencies, risk management, robo-advising, crowdfunding, blockchain, new payment solutions, machine learning and AI for financial services, digital currencies, etc., published quarterly online by MDPI.
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- Rapid Publication: manuscripts are peer-reviewed and a firstdecision is provided to authors approximately 22.2 days after submission; acceptanceto publication is undertaken in 3.7 days (median values for papers published inthis journal in the second half of 2023).
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subjectImprint Informationget_appJournal Flyer Open Access ISSN: 2674-1032
Latest Articles
Open AccessArticle
Argumentation Schemes for Blockchain Deanonymisation
byDominic Deuber, Jan Gruber, Merlin Humml, Viktoria Ronge and Nicole Scheler
FinTech 2024, 3(2), 236-248; https://doi.org/10.3390/fintech3020014 - 27 Mar 2024
Abstract
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, [...] Read more.
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.Full article
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Open AccessArticle
Comparative Analysis of Linear Models and Artificial Neural Networks for Sugar Price Prediction
byTathiana M. Barchi, João Lucas Ferreira dos Santos, Priscilla Bassetto, Henrique Nazário Rocha, Sergio L. Stevan, Jr., Fernanda Cristina Correa, Yslene Rocha Kachba and Hugo Valadares Siqueira
FinTech 2024, 3(1), 216-235; https://doi.org/10.3390/fintech3010013 - 12 Mar 2024
Abstract
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 [...] Read more.
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
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Open AccessArticle
byLy Nguyen, Mominul Ahsan and Julfikar Haider
FinTech 2024, 3(1), 184-215; https://doi.org/10.3390/fintech3010012 - 05 Mar 2024
Abstract
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 [...] Read more.
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
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Open AccessArticle
byMichał Grabowski
FinTech 2024, 3(1), 173-183; https://doi.org/10.3390/fintech3010011 - 04 Mar 2024
Abstract
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 [...] Read more.
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 article belongs to the Special Issue The Impact of Digitalisation on Financial Services and Financial Literacy)
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Open AccessArticle
byVijaya Krishna Kanaparthi
FinTech 2024, 3(1), 151-172; https://doi.org/10.3390/fintech3010010 - 17 Feb 2024
Abstract
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 [...] Read more.
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
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Open AccessArticle
byMedina Ayta Mohammed, Carmen De-Pablos-Heredero and José Luis Montes Botella
FinTech 2024, 3(1), 135-150; https://doi.org/10.3390/fintech3010009 - 15 Feb 2024
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Financial Technology and Innovation Sustainable Development)
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Open AccessArticle
A Crypto Yield Model for Staking Return
byJulien Riposo and Maneesh Gupta
FinTech 2024, 3(1), 116-134; https://doi.org/10.3390/fintech3010008 - 15 Feb 2024
Abstract
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 [...] Read more.
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
(This article belongs to the Special Issue Advances in Analytics and Intelligent System)
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Open AccessArticle
Robo Advising and Investor Profiling
byRaquel M. Gaspar and Madalena Oliveira
FinTech 2024, 3(1), 102-115; https://doi.org/10.3390/fintech3010007 - 03 Feb 2024
Abstract
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 [...] Read more.
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
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