FinTech

Extreme movements in financial time series pose challenges for risk management and forecasting, particularly when their timing is irregular and difficult to anticipate. This study aims to develop a probabilistic framework for detecting and predicting such events using daily Bitcoin returns as a case study. We first identify extreme positive and negative return events using the Isolation Forest algorithm and estimate their empirical recurrence patterns using a dynamic frequency table to derive baseline parametric probabilities. A 7-day Hawkes excitation kernel is then applied to capture short-run self-exciting dynamics, and both components are integrated using logistic regression to produce real-time probability forecasts. The results show that positive events occur more frequently than negative ones and that prediction accuracy improves over time: Brier scores, which measure the accuracy of probabilistic predictions, decrease as additional event data accumulate, and log loss values exhibit a consistent downward trend. Overall, by combining anomaly detection, empirical inter-arrival estimation, and excitation dynamics into a unified structure, the proposed framework offers a transparent and adaptable tool for forecasting extreme events in the financial market. Full article

We examine whether aggregate “music mood” derived from globally popular songs can help forecast primary equity issuance. We build a Friday-anchored weekly panel that merges SEC EDGAR counts of priced Initial Public Offerings (IPOs) with features from the Spotify Daily Top 200 (audio descriptors such as valence, energy, danceability, tempo, loudness, etc.) and Genius-scraped lyrics. We extract lyric sentiment by tokenizing Genius-scraped lyrics and aggregating lexicon-based affect scores (valence and arousal) into popularity-weighted weekly indices. To address sparsity and regime shifts in issuance, we train a leakage-safe Long Short-Term Memory (LSTM) network on a smoothed target—the forward 4-week sum of IPOs—and obtain next-week forecasts by dividing the predicted sum by 4. On a chronological holdout, a single LSTM with look-back K = 8 outperforms strong baselines—reducing MAE by 13.9%, RMSE by 15.9%, and mean Poisson deviance by 27.6% relative to the best baseline in each metric. Furthermore, we adopt SHapley Additive exPlanations (SHAP) to explain our LSTM model, showing that IPO persistence remains the dominant driver, but music and lyrics covariates contribute incremental and robust signal. These results suggest that aggregate music sentiment contains economically meaningful information about near-term IPO activity. Full article

Robo-advisors are evolving fintech solutions that ask potential clients about their investment purpose and time horizon and then offer investment strategies to reach different goals. This study aims to build on prior research and gain insights into the influence of innovation attributes (relative advantage, complexity, compatibility, and observability), perceived trust, and image regarding robo-advisor adoption by applying and extending the Diffusion of Innovation (DOI) theory. Data were collected using a cross-sectional survey approach. A total of 187 valid responses were obtained from an online participant recruitment website based in the United States and analysed using the partial least squares approach. The findings indicate that relative advantage and attitude influence an individual’s intention to adopt a robo-advisor, while all innovation attributes, perceived trust, and image of a robo-advisor influence an individual’s attitude towards it. By extending the DOI framework, this research advances understanding of its applicability to robo-advisor adoption. This study contributes to the literature by clarifying the influences on robo-advisor adoption and their relationships. From a practical standpoint, the findings and measures could help wealth management companies improve their promotional campaigns and technical design. Full article

Road infrastructure underpins Nigeria’s economic competitiveness, yet Public–Private Partnership (PPP) performance is constrained not by inadequate legislation but by persistent weaknesses in enforcement and governance. Transparency deficits across procurement, design management, certification, and toll-revenue reporting have produced chronic delays, cost overruns, and declining public trust. This study offers the first empirical investigation of blockchain–Building Information Modelling (BIM) integration as a transparency-enhancing mechanism within Nigeria’s PPP road sector, focusing on Lagos State. Using a qualitative design, ten semi-structured interviews with stakeholders across the PPP lifecycle were thematically analysed to diagnose systemic governance weaknesses and assess the contextual feasibility of digital innovations. Findings reveal entrenched opacity rooted in weak enforcement, discretionary decision-making, and informal communication practices—including biased bidder evaluations, undocumented design alterations, manipulated certifications, and toll-revenue inconsistencies. While respondents recognised BIM’s potential to centralise project information and blockchain’s capacity for immutable records and smart-contract automation, they consistently emphasised that technological benefits cannot be realised absent credible institutional foundations. The study advances an original theoretical contribution: the Codify–Condition–Capacitate framework, which explains the institutional preconditions under which digital governance tools can improve transparency. This framework argues that effectiveness depends on: codifying digital standards and legal recognition; conditioning enforcement mechanisms to reduce discretionary authority; and capacitating institutions through targeted training and phased pilots. The research generates significant practical implications for policymakers in Nigeria and comparable developing contexts seeking institution-aligned digital transformation. Methodological rigour was ensured through purposive sampling, thematic saturation assessment, and documented analytical trails. Full article

Credit card fraud detection remains a critical challenge for financial institutions, particularly due to extreme class imbalance and the continuously evolving nature of fraudulent behavior. This study investigates two complementary approaches: anomaly detection based on multivariate normal distribution and deep reinforcement learning using a Deep Q-Network. While anomaly detection effectively identifies deviations from normal transaction patterns, its static nature limits adaptability in real-time systems. In contrast, the DQN reinforcement learning model continuously learns from every transaction, autonomously adapting to emerging fraud strategies. Experimental results demonstrate that, although initial performance metrics of the DQN are modest compared to anomaly detection, its capacity for online learning and policy refinement enables long-term improvement and operational scalability. This work highlights reinforcement learning as a highly promising paradigm for dynamic, high-volume fraud detection, capable of evolving with the environment and achieving near-optimal detection rates over time. Full article

This study investigates how strategic foresight can enhance FinTech governance and policy resilience in emerging economies, using Kuwait as an illustrative case. It aims to identify which foresight interventions should be prioritized across alternative futures to strengthen innovation, security, and institutional adaptability within the digital finance ecosystem. A scenario-based Multi-Criteria Decision Analysis (MCDA) framework is applied, combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Expert evaluations were conducted to assess five foresight interventions against eight policy and performance criteria across three plausible scenarios: Optimistic Growth, Status Quo, and Crisis and Contraction. Sensitivity analyses were performed to validate the stability of intervention rankings. The results reveal distinct priorities under each scenario: SME-oriented digital finance platforms and talent development dominate under growth and stability, while cybersecurity investment becomes paramount during crisis conditions. Regulatory fast-tracking maintains a consistent, moderate influence across all contexts. These outcomes underscore the need for adaptive, context-sensitive policy design that accommodates uncertainty. The framework provides policymakers with a structured approach to align FinTech strategies with long-term national visions such as Kuwait’s Vision 2035, while offering transferable insights for other emerging economies. The study’s originality lies in integrating strategic foresight and MCDA for FinTech governance—a methodological and practical contribution to foresight-informed policymaking. Full article

Research on users’ switching intentions in peer-to-peer (P2P) mobile payment systems, particularly in developing markets, remains limited. This study examines how two satisfaction dimensions, transaction-based satisfaction and experience-based satisfaction, influence switching intentions through two layers of trust: institution-based trust and disposition to trust. Grounded in Expectancy-Disconfirmation Theory, data from 529 users of Haiti’s leading P2P mobile payment platform were analyzed using structural equation modeling. Results show that while transaction-based satisfaction has minimal impact on switching intentions, experience-based satisfaction strengthens institution-based trust, which in turn significantly reduces switching intentions. These findings highlight the central role of institutional reliability in shaping post-adoption behavior in duopolistic and resource-constrained markets. The study extends satisfaction-trust theory to digital financial ecosystems and offers practical insights for improving user retention through sustained institutional credibility and long-term service reliability. Full article

This study investigates the influence of access to mobile cashless technology on enterprise continuity intention and cash flow management skills. It also explores the influence of cashless technology, knowledge, and experience on enterprise continuity intention and cash flow management skills, and examines the direct relationship between cash flow management skills and enterprise continuity intention among micro and small enterprises during crises and in an unstable context. The 259 responses collected from micro and small entrepreneurs were analyzed by Partial Least Squares Structural Equation Modeling. The hypotheses tested reported a positive and significant relationship between access to mobile cashless technology and enterprise continuity intention and cash flow management skills. Furthermore, it was found that cashless technology knowledge and experience have a positive and significant relationship with enterprise continuity intention, as well as cash flow management skills. Finally, cash flow management skills were found to positively influence enterprise continuity intention. The study offers theoretical and practical implications for policymakers and other stakeholders to improve cashless transactions in the context of the study. Full article

This study evaluates the early impact of Central Bank Digital Currencies (CBDCs) on key financial indicators in The Bahamas, focusing on the introduction of the SandDollar—the world’s first fully implemented retail CBDC. Using the Synthetic Control Method (SCM), the analysis constructs counterfactual scenarios to assess the effects of CBDCs on three dependent variables: outstanding loans from commercial banks as a percentage of GDP, outstanding deposits as a percentage of GDP, and the number of deposit accounts per 1000 adults. Three separate SCM models were estimated for the period 2014–2024, incorporating a broad set of control variables reflecting financial infrastructure, economic performance, demographic characteristics, and digital readiness. The findings consistently show that the SandDollar’s implementation is associated with reductions in loan issuance, deposit levels, and deposit account ownership compared to their synthetic counterparts. These results support the hypothesis that direct CBDC models may amplify “deposit substitution” and increase liquidity risks by shifting financial activity away from commercial banks. Although the SCM provides a structured causal framework, the short post-treatment period and potential pandemic-related disruptions limit the scope of a long-term understanding. The study underscores the importance of careful CBDC design, particularly the role of intermediated models in mitigating unintended financial stability risks. Full article

Prediction of stock closing price plays a critical role in financial planning, risk management, and informed investment decision-making. In this study, we propose a novel model that synergistically amalgamates Bidirectional GRU (BiGRU) with three complementary attention techniques—Top-k Sparse, Global, and Bahdanau Attention—to tackle the complex, intricate, and non-linear temporal dependencies in financial time series. The proposed Fused Attention Model is validated on two highly volatile, non-linear, and complex- patterned stock indices: NIFTY 50 and S&P 500, with 80% of the historical price data used for model learning and the remaining 20% for testing. A comprehensive analysis of the results, benchmarked against various baseline and hybrid deep learning architectures across multiple regression performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R² Score, demonstrates the superiority and noteworthiness of our proposed Fused Attention Model. Most significantly, the proposed model yields the highest prediction accuracy and generalization capability, with R² scores of 0.9955 on NIFTY 50 and 0.9961 on S&P 500. Additionally, to mitigate the issues of interpretability and transparency of the deep learning model for financial forecasting, we utilized three different Explainable Artificial Intelligence (XAI) techniques, namely Integrated Gradients, SHapley Additive exPlanations (SHAP), and Attention Weight Analysis. The results of these three XAI techniques validated the utilization of three attention techniques along with the BiGRU model. The explainability of the proposed model named as BiGRU based Fused Attention (BiG-FA), in addition to its superior performance, thus offers a robust and interpretable deep learning model for time-series prediction, making it applicable beyond the financial domain. Full article

Background: Fintech companies have revolutionized the financial services industry in India in recent years. This is especially true for the growth of digital payment methods. India’s unbanked are being introduced to banking by fintech companies. Despite the country’s strong banking system, many residents find it difficult to get government financial services. This is particularly true for rural or low-income people. This vacuum has been addressed by fintech solutions including digital banking, micro-lending applications, mobile wallets, and UPI platforms. Objectives: to study the impact of financial technology businesses on increasing financial inclusion for India’s underbanked and unbanked population and Challenges encountered by financial technology enterprises in their endeavors to access unbanked populations, encompassing concerns of infrastructure with special reference to western Uttar Pradesh. Method: This mixed-methods study examines how FinTech is narrowing the financial gap for unbanked people using quantitative econometric analysis and qualitative case study assessments. Results: Digital financial innovation and regulatory support encourage inclusive growth in underdeveloped economies, whereas rich nations benefit from sophisticated banking institutions. This is indicated by the small influence of GDP per capita (β = 0.22–0.32, p < 0.05). Findings: The study found that inclusive finance is revolutionized when FinTech is used with the help of robust regulatory frameworks and digital infrastructure. Policymakers should prioritize cybersecurity, public-private partnerships to improve digital literacy, and rural connection if they want more people to take part in the digital financial ecosystem. Implications: FinTech can remove obstacles to accessing financing. The proper coordinated improvements in regulatory frameworks, digital infrastructure and financial literacy among the people are necessary to achieve full financial inclusion. Full article

The article presents a blockchain-based architecture for decentralized electricity trading that tokenizes energy delivery rights and cash-flows. Energy Attribute Certificates (EACs) are implemented as NFTs, while buy/sell orders are encoded as ERC-1155 tokens whose tokenId packs a time slot and price, enabling precise matching across hours. A clearing smart contract (Matcher) burns filled orders, mints an NFT option, and issues two ERC-20 assets: PT, the right to consume kWh within a specified interval, and YT, the producer’s claim on revenue. We propose a simple, linearly increasing discounted buyback for YT within the slot and introduce an aggregating token, IndexYT, which accumulates YTs across slots, redeems them at par at maturity, and gradually builds on-chain reserves—turning IndexYT into a liquid, yield-bearing instrument. We outline the PT/YY lifecycle, oracle-driven policy controls for DSO (e.g., transfer/splitting constraints), and discuss transparency, resilience, and capital efficiency. The contribution is a Pendle-inspired split of electricity into Principal/Yield tokens combined with a time-stamped on-chain order book and IndexYT, forming a programmable market for short-term delivery rights and yield derivatives with deterministic settlement. Full article

This paper examines the combination of traditional banking credit assessment techniques with contemporary internal sales accounting systems in Kazakhstan, aiming to augment the precision and resilience of financial assessments pertaining to SMEs. The proposed model consists of two discrete components: a traditional credit scoring module that employs logistic regression and a supplementary sales analytics module that leverages ensemble machine learning methodologies — random forests and gradient boosting algorithms. The outputs generated by these components are amalgamated through an ensemble strategy, where optimal weighting coefficients are ascertained via cross-validation. An empirical analysis was conducted on a dataset encompassing 41,000 SME records from a prominent Kazakhstan bank alongside daily transactional sales data from 150 SMEs gathered between the years 2021 and 2024. The integrated hybrid model demonstrated a statistically meaningful enhancement in predictive efficacy, as evidenced by an increase in the area under the ROC curve from 0.76 to 0.87 and a decrease in mean squared error from 0.12 to 0.08 relative to the traditional methodology. The investigation delves into the transformative influence of digitalization on innovation within SMEs, elucidating that improved real-time data integration not only sharpens risk assessment processes but also promotes adaptive lending strategies and operational efficiencies. Full article

The rapid evolution of digital assets transforms cryptocurrencies into one of the most volatile and data-rich financial markets. Their nonlinear and unpredictable nature limits the effectiveness of traditional forecasting models, motivating the use of machine learning methods to identify hidden patterns and short-term price movements. This study compares the performance of Logistic Regression (LR), Random Forest (RF), XGBoost, Support Vector Classifier (SVC), K-Nearest Neighbors (KNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models in predicting the daily price directions of Bitcoin (BTC), Ethereum (ETH), and Ripple (XRP). Extensive data preprocessing and feature engineering are performed, integrating a broad set of technical indicators to enhance model generalization and capture temporal market dynamics. The results show that XGBoost achieves the highest classification accuracy of 55.9% for BTC and 53.8% for XRP, while LR provides the best result for Ethereum with an accuracy of 54.4%. In trading simulations, XGBoost achieves the strongest performance, generating a cumulative return of 141.4% with a Sharpe ratio of 1.78 for Bitcoin and 246.6% with a Sharpe ratio of 1.59 for Ripple, whereas LSTM delivers the best results for Ethereum with a 138.2% return and a Sharpe ratio of 1.05. Compared to recent studies, the proposed approach attains slightly higher accuracy, while demonstrating stronger robustness and profitability in practical backtesting. Overall, the findings confirm that through rigorous preprocessing machine learning-based strategies can effectively capture short-term price movements and outperform the conventional buy-and-hold benchmark, even under a simple rule-based trading framework. Full article

This study develops a comprehensive competitiveness model for Indonesia’s WealthTech ecosystem by integrating Interpretive Structural Modeling (ISM) and MICMAC analysis. The research identifies and classifies 23 interrelated variables derived from SEM-PLS and NVivo analysis, of which 17 passed expert validation and were subsequently retained in the ISM–MICMAC structural model, including innovation capabilities, regulatory support, digital infrastructure, capital readiness, and customer trust, to evaluate their systemic roles in shaping competitive advantage. Through expert interviews, bibliometric analysis, and a structured modeling process, key independent drivers such as innovation capabilities, geopolitical events, and economic shocks were identified as foundational enablers. Linkage variables including digital transformation, strategic alliances, and cost leadership connect these enablers to dependent outcomes such as customer satisfaction and platform personalization. The resulting hierarchical framework and strategic roadmap offer actionable insights for policymakers, fintech stakeholders, and investors to enhance resilience, regulatory alignment, and ecosystem integration. This research not only fills a critical gap in the digital finance literature but also provides a strategic tool for advancing Indonesia’s WealthTech sector within the global financial landscape. Full article

Purpose: The central research question of the study is how objective financial knowledge and subjective financial confidence interact and relate to digital financial behavior and the use of FinTech tools. By examining both objective knowledge refers to measured, test-based financial competence and subjective confidence denote self-assessed financial understanding, the research offers insight into the psychological and demographic drivers of FinTech use and perceived financial well-being. Design/methodology/approach: Based on the OECD’s 2023 international financial literacy survey, the study uses a nationally representative Hungarian sample. It employs non-parametric statistical methods, linear regression, and two-step cluster analysis. Three composite indicators, general digital activity, digital financial engagement frequency, perceived financial security were developed to measure general digital activity, frequency of digital financial engagement, and perceived financial security. Findings: Results reveal a moderate but significant correlation between actual and self-assessed financial knowledge. Men score higher on both measures, though self-assessment bias does not significantly differ by gender. Higher education and income levels are associated with stronger financial literacy and more frequent use of FinTech tools, while age correlates negatively. However, the accuracy of self-perception is not explained by these demographic factors. Cluster analysis identifies four distinct financial knowledge profiles and five consumer digital behavior types, revealing disparities in digital financial inclusion and confidence. Originality: This research contributes a multidimensional perspective on how consumer capabilities, attitudes, and digital behavior influence FinTech adoption. By integrating behavioral, demographic, and psychological factors, the study offers practical implications for targeted financial education and the design of inclusive, human-centered digital financial services—especially relevant for emerging European markets. Full article

Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks. Full article

Non-Fungible Tokens (NFTs) have transformed digital ownership, offering unique representations of assets such as art, collectibles, and virtual property. However, pricing NFTs remains a complex and underexplored issue. This study addresses two core questions: what determines NFT prices? And how are prices set in NFT markets? We conduct a comprehensive literature review and market analysis to identify both endogenous and exogenous price determinants. Trait rarity emerges as the most influential intrinsic factor, while cryptocurrency value stands out as a major external influence, albeit with ambiguous effects. Other factors include visual aesthetics, scarcity, utility in games, social media engagement, and broader market sentiment. As to pricing mechanisms, aside from fixed pricing (which is accepted in all marketplaces), NFT marketplaces primarily utilise auctions for art pieces and collectibles— especially English and Dutch formats—which are effective at capturing the buyer’s willingness-to-pay. Full article

The rapid growth of financial technology (FinTech) has created new opportunities to promote environmentally responsible consumption. Yet, little is known about the factors that shape consumer trust in green FinTech platforms, which is crucial for their adoption and long-term impact. This study develops and tests a partial least squares structural equation model (PLS-SEM) integrating sustainability and technology determinants of trust. Survey data from 240 consumers were analyzed. Results show that green transparency, platform security and privacy, and ease of use significantly enhance perceived credibility, while social influence and perceived environmental responsibility increase green perceived value. In turn, perceived credibility reduces perceived risk and promotes trust. Trust is also strengthened by environmental responsibility, green perceived value, and platform innovativeness, but weakened by perceived risk. All hypothesized relationships were statistically significant. The findings highlight the importance of credible sustainability communication, high level security, and social endorsement in building trust for green FinTech services. Full article