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.