Advancing fraud detection and regulatory transparency in Fintech through data-mining frameworks

FinTech fraud detection and regulatory transparency rely on hybrid data?mining frameworks that integrate supervised classifiers, anomaly detectors, rule?based filters, and explainable AI techniques. These architectures balance precision with adaptability, enabling detection of known?pattern attacks and novel outliers while providing human?readable rationales for alerts. Real?time streaming supports immediate intervention, whereas batch?processing enables deeper cross-transaction analysis, requiring trade?offs between latency and depth. Explainability layers; featuring feature-attribution and counterfactual scenarios; facilitate stakeholder understanding and regulatory auditability. Persistent challenges include standardizing explanation formats, ensuring scalability under peak loads, and addressing cross-jurisdictional differences in transparency requirements. Future research should focus on longitudinal validation of explanation methods, integration of blockchain?based audit logs for tamper-evident records, and hybrid human–AI oversight models that enhance both accuracy and accountability. Rigorous governance, methodological transparency, scalable infrastructure, and industry collaboration for continuous improvement globally. Keywords: Fraud Detection, Fintech, Data, Mining.