This study examines the theoretical, structural, and empirical applications of Artificial Intelligence (AI) and Machine Learning (ML) architectures within the domain of regulatory compliance (RegTech) and supervisory technology (SupTech) for cross-border digital transactions. The exponential expansion of cross-border financial flows, real-time payment systems, and decentralized financial instruments has amplified regulatory fragmentation, multi-jurisdictional compliance friction, and sophisticated financial crime typologies. Utilizing institutional economics, information asymmetry theory, and computational compliance modeling, this paper analyzes how advanced algorithmic architectures—specifically Graph Neural Networks (GNNs), Natural Language Processing (NLP), and Federated Learning—optimize anti-money laundering (AML), counter-terrorist financing (CFT), and real-time sanctions screening. The findings demonstrate that shifting from legacy rule-based heuristics to adaptive, privacy-preserving AI frameworks significantly compresses false-positive rates, bridges cross-jurisdictional regulatory disparities, and establishes a dynamic, mathematically rigorous paradigm for global financial integrity.
The increasing reliance on digital banking solutions has significantly transformed financial services, with Automated1Teller1Machine (ATM) transactions playing a critical role in banking operations. This study examines the impact of ATM transactions on the1 financial performance of Deposit Money Banks (DMBs) in Nigeria, utilizing a Robust Least Squares (RLS) estimation technique to analyze quarterly data from 2009 to 2023. The study employs Return on Assets (ROA), Return on Equity (ROE), and Capital Adequacy Ratio1 (CAR) as proxies for financial performance. The findings reveal that while ATM transactions exhibit a statistically insignificant effect on ROA and ROE, they have a significant positive relationship with CAR, suggesting that ATM services contribute more to the financial stability of banks than to their profitability. The study also highlights key challenges associated with ATM usage, including network failures, fraud risks, and high maintenance costs, which may limit its full potential in enhancing bank performance. Given these findings, the study recommends that Nigerian banks strengthen ATM infrastructure, enhance cybersecurity measures, integrate emerging technologies such as blockchain, and implement customer education programs to optimize ATM efficiency and mitigate associated risks. These measures will enhance financial inclusion, improve customer satisfaction, and sustain the overall financial health of deposit money banks in Nigeria.
Pre-analysis commitment for a study of deposit rate sensitivity across U.S. bank size classes over the 2021 to 2024 tightening cycle, using FDIC Call Report data. The plan fixes the estimator, sample, comparison groups, controls, reported statistics, robustness variants, and the threshold for what counts as a finding. The file was written on August 25, 2026, before any data was retrieved. It was deposited here on August 27, 2026, after estimation had been carried out. This deposit therefore establishes the content and the deposit date. It does not independently verify that the file predates the estimation, and no claim to that effect is made. Departures from the plan are recorded in a deviation log accompanying the analysis. The work is funded by the Blockchain Association. The author retains the right to publish the findings regardless of what they show.
Omar A. Esqueda, Mohammad Sharif Karimi, Daniel P. Liston, Saleh Ghavidel Doostkouei
This paper investigates the dynamic relationship between macroeconomic factors—particularly Bitcoin pricing—and the equity returns of firms in the financial technology (FinTech) sector. Using a Structural Vector Autoregression (SVAR) framework with daily data from July 2013 to March 2025, the analysis examines how shocks in major financial variables affect FinTech equity performance. The results indicate that positive shocks to the S&P 500 index are associated with a significant increase in the FinTech sector indicator, underscoring the sector’s close linkage with overall equity market performance. Shocks to the 10-year U.S. Treasury bond yield also generate a positive but comparatively weaker and delayed response, suggesting a secondary influence of interest rate dynamics. In contrast, Bitcoin price shocks do not produce a statistically significant effect on FinTech returns, implying limited spillovers from cryptocurrency markets to traditional FinTech equities. Robustness checks using PARCH and TARCH models confirm the stability of these findings. Overall, the evidence suggests that FinTech firms remain more sensitive to developments in conventional financial markets than to movements in digital asset prices, highlighting the sector’s growing integration with institutional finance rather than speculative crypto-based activity.
This study examines whether major cryptocurrency returns respond systematically to scheduled Federal Reserve (Fed) interest rate announcements and whether FOMC-window movements are explained more by realised policy decisions or by broader risk-sentiment conditions. Using daily data for Bitcoin, Ethereum, XRP, Dogecoin, Solana, the U.S. Dollar Index, and VIX, the analysis covers 43 scheduled FOMC announcements between 2021 and 2026. Six cumulative event-window returns are evaluated through parametric mean tests, Wilcoxon signed-rank tests, and panel event-study regressions with crypto fixed effects and FOMC-event-clustered standard errors. Because the available surprise measure contains only two nonzero observations, the study focuses on realised rate changes, hike/cut/hold categories, asymmetric rate-change magnitudes, VIX changes, and DXY returns rather than formal monetary policy shocks. The results provide little evidence that cryptocurrency returns differ systematically from zero around FOMC announcements. Actual rate changes, policy-direction categories, and asymmetric hike/cut magnitudes do not robustly explain event-window returns, and crypto-specific interaction models provide no stable evidence of heterogeneous sensitivity across assets. By contrast, VIX changes are negatively and significantly associated with cryptocurrency returns in several windows, while DXY effects are weak and unstable. The study contributes by showing that FOMC-window cryptocurrency performance is better explained by risk-sentiment conditions than by the realised size or direction of Fed rate decisions.
The rapid development of cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs) has transformed the global monetary landscape and accelerated the transition toward a cashless society. While critics argue that digital currencies threaten financial stability due to volatility, disintermediation, energy consumption, and regulatory concerns, this paper contends that the increasing competition among digital and fiat currencies can generate significant economic benefits. By examining the evolution of cryptocurrencies, the emergence of stablecoins, the global adoption of CBDCs, and the case of Zimbabwe's hyperinflation, this study argues that currency competition encourages governments to pursue more disciplined fiscal and monetary policies, strengthens policy credibility, and helps anchor inflation expectations. Greater monetary credibility also expands policymakers' ability to respond effectively to future economic downturns. Although digital currencies present important risks, many of these challenges can be mitigated through technological innovation, appropriate regulation, and institutional development. Overall, this paper concludes that a wellmanaged transition toward a cashless society can promote competition, innovation, and long-term economic resilience rather than undermine financial stability.
The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets. We test it on five markets spanning 2006-2026 (equities including banking crises and the AI sector, cryptocurrencies, commodities, foreign exchange and sovereign debt), against three independent definitions of a crisis episode, at a fixed alarm budget, out of sample, with block-bootstrap intervals and a Holm correction across the family of tests. The benchmarks are the Absorption Ratio, the industry standard used by MSCI and central banks; the effective rank and the Vendi score, the sharpest spectral measures available; Ollivier-Ricci curvature; and the global and local balance indices of signed correlation networks. Three comparisons favour the index. It carries a per-node decomposition, diag(A^3), naming which asset is carrying the concentration with no parameter to select, and scores 0.97-0.99 against 0.33-0.84 for the only published per-node alternative, whereas spectral attribution must first choose how many components to read and collapses under a standard but wrong choice. Its alarms are the cleanest of anything tested, 4.0% of them with no matching episode against 14.7% for the effective rank and roughly 59% for the Absorption Ratio. And it beats the Absorption Ratio on detection by 0.273 in F1 out of sample, p<0.0005. The remaining comparisons are ties. Against the effective rank and the Vendi score the index ties in every scheme and both samples, and the margin over the Absorption Ratio narrows under the strictest labelling. On real matrices the far simpler node degree reproduces the attribution. A lead-lag analysis puts the peak cross-correlation at zero lag: this is a coincident state index, not a forecast.
Decentralized Finance (DeFi) refers to an open financial ecosystem built on blockchain technology that does not require the participation of centralized institutions. The technology and operational mechanisms it employs represent a significant "paradigm mismatch" with the current financial regulatory framework. This paper examines the comprehensive impact of DeFi on existing financial regulation from multiple perspectives, including the blurring of regulatory authority and a lack of accountability; the difficulty in identifying regulatory targets and the ambiguity in determining their nature; the ineffectiveness of regulatory rules and the absence of relevant provisions; overlapping jurisdictions, and difficulties in enforcement. Through a comparative study of regulatory experiences in the United States, Europe, and other regions, this paper proposes solutions such as shifting the existing regulatory philosophy toward functional regulation, embedding compliance requirements into the underlying technology at the institutional level, and strengthening international cooperation at the operational level, while also discussing the specific context in China. This paper identifies a threefold paradigm mismatch between decentralized finance and traditional financial regulation, giving rise to multiple regulatory challenges such as difficulties in holding entities accountable, ambiguity in defining regulatory targets, ineffective regulatory rules, and obstacles to cross-border enforcement. A comparison of regulatory practices in the U.S. and Europe reveals that it is difficult for any single country to independently manage the risks associated with globalized DeFi.