RISK SCORING IN THE SECTOR MACHINE LEARNING APPLICATIONS FOR FRAUD DETECTION AND BFSI
Keywords:
Machine Learning, Fraud Detection, Artificial Intelligence, Financial Technology, Anomaly DetectionAbstract
As digital transactions continue to expand rapidly in the Banking, Financial Services, and Insurance (BFSI) sector, traditional rule-based fraud detection mechanisms struggle to keep pace with evolving fraud tactics. Machine Learning (ML) has emerged as a transformative tool, offering adaptive, scalable, and predictive capabilities. This paper explores ML's growing role in fraud detection and risk scoring in the BFSI industry, with emphasis on recent developments up to existing. We review key algorithms, data processing strategies, and deployment models, and discuss challenges including explainability, data imbalance, and regulatory compliance. Finally, we present flow charts, tables, and visual illustrations to synthesize insights for practitioners and researchers.
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