FinTech

This study explores the dynamic interaction between financial literacy and higher education, focusing on the critical role of financial education in improving individual financial well-being. Using bibliometric analysis and the VOSviewer software, this research examines thematic clusters in financial literacy, categorized into access, behavior, health, and education. By analyzing 469 articles from the Web of Science database (2020–2024), this study identifies trends and key linkages between financial literacy and societal well-being, highlighting the role of digital innovation. While FinTech is discussed as a facilitator of financial inclusion and education, the primary focus lies in understanding how financial literacy drives behavioral change, capacity building, and economic resilience. This paper provides information for policymakers and educators to design inclusive, behaviorally focused educational programs that address specific demographic needs, ultimately contributing to societal and economic resilience. Full article

Global supply chains, especially in commodity trading, are plagued by fragmentation, lack of transparency, and trust deficits among participants. These issues lead to inefficiencies, increased costs, and an over-reliance on intermediaries. The present Communication describes a blockchain-based platform that leverages Self-Sovereign Identity (SSI) and Verifiable Credentials (VCs) to address these challenges in supply chain management. Developed in collaboration with coffee industry stakeholders, our approach proposes a platform with an integrated marketplace for seller discovery, enables precise order definition with detailed terms and conditions, and actively guides both buyers and sellers throughout the shipping process, managing financial guarantees and ensuring a secure transaction flow. The platform is compatible with both traditional banking infrastructure and modern crypto-based systems, enabling seamless financial transactions. In cases where disputes arise, we empower users to easily collect all communications and documents to present to legal authorities, expediting the resolution process. The platform is implemented using the Internet Computer Protocol (ICP) for secure, on-chain storage and application hosting, and is integrated with the Ethereum blockchain to leverage its extensive decentralized finance (DeFi) ecosystem, significant liquidity, and robust stablecoin infrastructure, thereby facilitating secure financial transactions. Moreover, we introduce an SSI-based authentication and authorization framework that spans across the entire platform, including both the Ethereum Virtual Machine (EVM) and Internet Computer Protocol (ICP), enabling unified role-based access control through verifiable credentials. A value-added of the present Communication, the framework is demonstrated by means of a detailed case study in the coffee industry, highlighting the technical challenges addressed during implementation. While quantitative efficiency metrics will be established through upcoming real-world testing with industry partners, the platform’s design aims to streamline operations by reducing intermediary dependencies and automating key processes. Finally, the Communication provides insights into its adaptability to other industries facing comparable supply chain challenges, presenting an approach focused on enhancing trust and reducing reliance on intermediaries. Full article

In several countries, digital financial advisory services, particularly those supported by robo-advisors, are becoming increasingly popular in retail banking. These tools assist users with financial decisions such as risk assessment, portfolio selection, and rebalancing—all at a reduced cost. Recent studies suggest that, over time, robo-advisors could complement human financial advisors. Building on this research, which evaluates robo-advisors’ effectiveness in asset allocation, this study aims to assess the impact of this strategic shift on retail banks’ profitability. It compares the Canadian and French banking sectors, where robo-advisors were introduced in the 2010s. Results indicate that implementing robo-advisors enhances profitability in non-interest activities, with this effect being more pronounced in France than in Canada. Full article

The shift toward a sustainable future demands substantial investments in green technologies and infrastructure, with green finance emerging as a pivotal driver for mobilizing such investments. This paper introduces a novel framework for green investment models and platforms tailored specifically to Qatar’s unique economic landscape. Through an extensive literature review, we identify essential policy levers and principles that can enhance the effectiveness of green finance initiatives. An experimental assessment utilizing a simulation model evaluates the potential impact of various policy scenarios on key metrics such as green investment volume, job creation, and environmental impact reduction. This study advocates for a comprehensive investment policy framework that includes alignment with Qatar’s national development objectives, targeted incentives for diverse economic sectors, collaborative stakeholder engagement, and robust monitoring and evaluation mechanisms. Our findings demonstrate that implementing these design principles can dramatically accelerate green finance in Qatar, aligning initiatives with the country’s National Vision 2030 and broader sustainability goals. This paper emphasizes the critical role of fiscal incentives tailored to specific sectors, the importance of collaboration among financial institutions and governmental bodies, and the necessity of continuous performance evaluations to inform adaptive policy adjustments. Ultimately, we propose a dynamic platform that not only facilitates green investments but also fosters innovation and mitigates the risks associated with sustainable projects in Qatar. Full article

The collapse of Silicon Valley Bank (SVB) on 11 March 2023, and the subsequent depegging of the USDC stablecoin highlighted vulnerabilities in the interconnected financial ecosystem. While prior research has explored the systemic risks of stablecoins and their reliance on traditional banking, there has been limited focus on how banking sector shocks affect digital asset markets. This study addresses this gap by analyzing the impact of SVB’s collapse on the stability of major stablecoins—USDC, DAI, FRAX, and USDD—and their relationships with Bitcoin and Tether. Using daily data from October 2022 to November 2023, we found that the SVB incident triggered a series of depegging events, with varying effects across stablecoins. Our results indicate that USDC, often viewed as one of the safer stablecoins, was particularly vulnerable due to its reliance on SVB reserves. Other stablecoins experienced different impacts based on their collateral structures. These findings challenge the notion of stablecoins as inherently safe assets and underscore the need for improved risk management and regulatory oversight. Additionally, this study illustrates how machine learning models, including gradient boosting and random forests, can enhance our understanding of financial contagion and market stability. Full article

This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization. Full article

FinTech stocks are an important new asset class that reflects the rapidly growing FinTech sector. This paper studies the practical implications of using gold, silver, and basket-of-precious-metals (gold, silver, platinum, palladium) ETFs to diversify risk in FinTech stocks. Downside risk reduction is estimated using relative risk ratios based on CVaR. The analysis shows that gold provides the most downside risk protection. For a 5% CVaR, a 30% portfolio weight for gold reduces the downside risk by about 25%. The minimum variance and minimum correlation three-asset (FinTech, gold, and silver) portfolios (with portfolio weights estimated using a TVP-VAR model) have the highest risk-adjusted returns (Sharpe ratio, Omega ratio) followed by the fixed-weight FinTech and gold portfolio. These results show the benefits of diversifying an investment in FinTech stocks with precious metals. These results are robust to weekly or monthly portfolio rebalancing and reasonable transaction costs. Full article

This study analyses the relationship between non-performing loans (NPLs) and innovation systems at a global level. The data were obtained from the World Bank and the Global Innovation Index over the period 2013–2022 for 149 countries. The k-means algorithm was used to verify the presence of clusters in the data. Since k-means is an unsupervised machine-learning algorithm, we compared the Silhouette coefficient with the Elbow method to find an optimization. The results show that the optimal number of clusters is three, as suggested using the Elbow Method. Furthermore, a panel data analysis was conducted. Results show that the level of NPLs is positively associated with cultural and creative services exports as a percentage of total trade and innovation input sub-index and negatively associated with the Hirsch Index, ICT services exports as a percentage of total trade, ICT services imports as a percentage of total trade, and information and communication technologies. Full article

The integration of Artificial Intelligence (AI) in the FinTech industry has significantly reshaped operational workflows, product innovation, and risk management, all of which are pivotal to company valuation. This study investigates the impact of AI-enhanced multilayer networks on FinTech valuation, introducing a novel, scalable multilayer network model with AI-driven Copula Nodes that serve as connectors across operational layers. By incorporating AI, the research unveils a dynamic and interconnected approach to FinTech valuation, revealing new pathways for value co-creation through real-time adjustments and predictive capabilities. The research reveals that while operational efficiency is a major driver of market value, a balanced integration of AI across risk management, product innovation, and market perception is essential for maximizing value. Additionally, the findings highlight the importance of managing AI-driven risks such as algorithmic bias and regulatory challenges. This comprehensive framework offers critical insights for FinTechs, investors, and regulators seeking to understand the complex role of AI in enhancing valuation within the evolving financial services landscape. Full article

The integration of generative AI (GAI) into the financial sector has brought about significant advancements, offering new solutions for various financial tasks. This review paper provides a comprehensive examination of recent trends and developments at the intersection of GAI and finance. By utilizing an advanced topic modeling method, BERTopic, we systematically categorize and analyze existing research to uncover predominant themes and emerging areas of interest. Our findings reveal the transformative impact of finance-specific large language models (LLMs), the innovative use of generative adversarial networks (GANs) in synthetic financial data generation, and the pressing necessity of a new regulatory framework to govern the use of GAI in the finance sector. This paper aims to provide researchers and practitioners with a structured overview of the current landscape of GAI in finance, offering insights into both the opportunities and challenges presented by these advanced technologies. Full article

Technical analysis, reliant on statistics and charting tools, is a predominant method for predicting stock prices. However, given the impact of the joint effect of stock price and trading volume, analyses focusing solely on single factors at isolated time points often yield partial or inaccurate results. This study introduces the application of Cycle Generative Adversarial Network (CycleGAN) alongside Deep Learning (DL) models, such as Residual Neural Network (ResNet) and Long Short-Term Memory (LSTM), to assess the joint effects of stock price and trading volume on prediction accuracy. By incorporating these models into system engineering (SE), the research aims to decode short-term stock market trends and improve investment decisions through the integration of predicted stock prices with Bollinger Bands. Thereby, active learning (AL) is employed to avoid over-and under-fitting and find the hyperparameters for the overall system model. Focusing on TSMC’s stock price prediction, the use of CycleGAN for analyzing 30-day stock data showcases the capability of ResNet and LSTM models in achieving high accuracy and F-1 scores for a five-day prediction period. Further analysis reveals that combining DL predictions with SE principles leads to more precise short-term forecasts. Additionally, integrating these predictions with Bollinger Bands demonstrates a decrease in trading frequency and a significant 30% increase in average Return on Investment (ROI). This innovative approach marks a first in the field of stock market prediction, offering a comprehensive framework for enhancing predictive accuracy and investment outcomes. Full article

The existing global multi-crises have generated significant transformations in the architecture of financial systems, impacting local communities. Furthermore, the digital era has created a conducive environment for the development of financial innovations that can generate financial instruments supporting financial inclusion. Our research aims to identify and develop innovative financial instruments that foster closer collaboration within communities and promote a more equitable distribution of wealth and resources, directly impacting financial inclusion and well-being. The methodology used in our study is based on existing empirical research in the specialized scientific literature, as well as on identifying variables within existing models. Additionally, the use of bibliometric analyses and research tools based on artificial intelligence allows us to structure the innovative financial instruments found in the scientific databases. Building on the existence of innovative financial instruments, our paper specifically explores the concept of time banking as an innovative financial instrument, offering a new approach to economic exchange and the construction of financial mechanisms at the local community level. By using technology, especially in digital and ecological eras, time banks can be efficiently managed through online platforms where individuals can register their contributed hours and access the services they need. This study’s conclusions emphasize that time banks have the potential to serve as innovative financial instruments. Furthermore, through the analysis conducted in this study and the identified models, this study contributes to redefining the concept of time banking as an innovative financial instrument. Time banks focus on the productivity and efficiency of local community activities, with direct implications for reducing dependence on traditional currency and promoting an equitable distribution of labor. This innovative approach is promising, especially in an increasingly digitized financial landscape. Our paper seeks to capture this transformative potential and highlight our personal contributions to redefining the time bank as an innovative financial instrument. Full article

Credit rationing, especially prevalent for smaller firms, impedes economic growth. A central bank-aligned not-for-profit managed business-to-business “stablecoin” (“synthetic central bank digital currency”) providing trade credit liquidity can provide additional monetary mass to mitigate small firm credit rationing. This raises growth by reducing monetary transmission imperfections consequent upon asymmetric information, commercial bank underwriting restrictions, market power dynamics, and regulatory distortion. A simple framework is developed to contextualise small firm credit rationing and associated monetary transmission imperfections with broader credit flows into both the real and monetary sectors. Evidence is presented regarding monetary transmission efficacy to firms, paving the way to proposing a business-to-business central bank-mediated “trade credit stablecoin” to improve business credit supply. In addition to providing additional (estimated at more than 10%) industrial and commercial (including smaller) firm financing, the envisaged trade credit stablecoin provides an additional monetary transmission channel for central banks to manage credit supply to the real economy to support economic activity and raise growth. Available to all firms, the trade credit stablecoin offers additional low-cost liquidity to firms, thereby offering policymakers an additional contra-cyclical monetary transmission instrument to support growth and, where necessary, reduce real economic disruption consequent upon financial system crises and liquidity events. Full article

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation. Full article

Commercial banks constitute a crucial segment of China’s financial system, and their efficient operation is directly linked to the development of other sectors within the national economy. The sustainable profitability of these banks is vital for maintaining the stability of China’s financial system. In the context of the current digital economy, it is of great theoretical and practical significance to conduct an in-depth analysis of the impact of financial technology (fintech) development on the sustainable profitability of commercial banks and its underlying mechanisms. Such research can promote the digital transformation of commercial banks, enhance risk supervision policies, and mitigate systemic financial risks. This study utilizes EViews software Version 13 to analyze annual data from 13 listed commercial banks in China over the period from 2011 to 2021. It examines the influence of fintech on the profitability of these banks, considering their unique characteristics and drawing insights from the existing literature on the mechanisms through which fintech affects bank profitability. Employing both a static panel fixed effects variable-intercept model and a dynamic panel generalized method of moments (GMM) model, the empirical findings indicate that fintech development significantly impacts the profitability of listed commercial banks. Full article

This paper examines the correlation between three prospective investing options: the Bitcoin cryptocurrency price, gold, and the Dow Jones stock index. The main research question is whether there is a causal effect of gold and the DWJ on Bitcoin and how this effect varies on time. The study begins with a background analysis that explains the definitions and operation of cryptocurrencies, followed by a brief overview of gold and its derivatives. In addition, a historical review of stock markets is provided, with a focus on the Dow Jones index. Then, a literature review follows. Daily data from three separate periods are used, each spanning four years. The first period, running from October 2014 to September 2018, provides an overview of the introduction of official cryptocurrency price data. The second period, running from Oct 2018 to Sept 2022, captures more recent trends preceding COVID-19. The third period, from January 2020 to December 2023, is the whole COVID-19 period with the initiation, embedded, and terminal phases. Classical inductive statistical methods (descriptive, correlations, multiple linear regression) as well as time series analysis methods (autocorrelation, cross-correlation, Granger causality tests, and ARIMA modeling) are used to analyze the data. Rigorous testing for autocorrelation, multicollinearity, and homoskedasticity is performed on the estimated models. The results show a correlation of Bitcoin with gold and the DWJ. This correlation varies over time, as in the first period the correlation mainly concerns the DWJ and in the second it mainly concerns gold. By using ARIMA models, it was possible to make a forecast in a time horizon of a few days. In addition, the structure of the forecasting mechanism of gold and DWJ on Bitcoin seems to have changed during the COVID-19 crisis. The findings suggest that future research should encompass a broader dataset, facilitating comprehensive comparisons and enhancing the reliability of the conclusions drawn. Full article

This paper explores the economic trends and identifies speculative bubbles within the emerging metaverse, based on the specific example of Decentraland, which is represented by its underlying native token MANA.For comparability, we consider three further tokens: SAND, ETH, and BTC.The study shows price prediction and provides further insight into bubble behavior to provide a deeper insight into the real trend and situation of the metaverse. When comparing all considered tokens, evidence of comovement and positive as well as negative bubbles is identified. This paper makes use of proven modeling techniques, such as SARIMA, for price prediction and LPPLS for financial bubble identification, visualization, and time stamping. Full article

In this paper, we study the networks of financial institutions using textual data (i.e., news). We draw knowledge graphs after the textual data has been processed via various natural language processing and embedding methods, including use of the most recent version of ChatGPT (via OpenAI api). Our final graphs represent bank networks and further shed light on the systemic risk of the financial institutions. Financial news reflects live how financial institutions are connected, via graphs which provide information on conditional dependencies among the financial institutions. Our results show that in the year 2016, the chosen 22 top U.S. financial firms are not closely connected and, hence, present no systemic risk. Full article