FinTech, Volume 2, Issue 1 (March 2023) – 12 articles

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

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. Full article

Longevity is without a doubt on the rise throughout the world due to advances in technology and health. Since 1960, Ghana’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–Carter, Renshaw–Haberman, Cairns–Blake–Dowd, and Quadratic Cairns–Blake–Dowd) are used to forecast mortality improvements between 2021 and 2030. The results from accuracy metrics indicate that the quadratic Cairns–Blake–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’s current age. Full article

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. Full article

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 (2019–2022). 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’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’ behavior. Full article

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–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. Full article