Intelligent Fraud Prevention Information Banking: A Data Governance- Centric Approach Using Behavioural Biometrics

This study investigates the integration of behavioural biometrics and data governance in enhancing fraud prevention systems within the banking sector. Behavioural biometrics refers to the analysis of unique patterns in user interactions—such as typing rhythm, mouse movement, and touchscreen gestures—to authenticate identity continuously and unobtrusively. As financial fraud becomes more sophisticated, traditional rule-based systems are proving insufficient. This research addresses that gap by evaluating the performance and ethical deployment of intelligent fraud detection systems using real-world data. Four open-source datasets were employed—IEEE-CIS Fraud Detection Dataset, Open Data Barometer, BioCatch case studies, and Stanford AI Index Reports. Statistical techniques including logistic regression, Wilcoxon signed-rank tests, and multivariate regression were used to evaluate system effectiveness and governance impact. Results show that behavioural biometric systems achieved an accuracy of 89.9% and a ROC-AUC of 0.849, indicating strong classification performance. Post-implementation data from fifteen institutions revealed an average fraud reduction of 35.5%, with statistically significant improvement (p < 0.001). Moreover, data governance maturity was found to explain 79.3% of national fraud rate variance and 86% of system performance variability. The findings highlight the critical role of ethical data governance in enabling the secure and responsible use of behavioural biometrics. Key recommendations include enforcing robust data privacy laws, investing in biometric infrastructure, and aligning AI governance frameworks across borders. This research provides empirical evidence and practical insights for banks, technology vendors, and policymakers seeking to modernize fraud prevention in a digitally complex financial environment.