FinTech

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. Full article

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. Full article

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. Full article

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. Full article

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?”. Full article

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. Full article

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

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. Full article