AI-Based Fraud Detection And Financial Risk Management

The rapid digitalization of banking, payments, and capital markets has increased both the scale of financial transactions and the sophistication of fraudulent activity, making traditional rule-based fraud detection and risk assessment systems increasingly inadequate. Artificial Intelligence (AI) offers a powerful alternative by enabling systems that can learn from historical transaction patterns, detect anomalies in real time, and continuously adapt to emerging fraud typologies. This paper proposes a framework for applying AI and Machine Learning (ML) techniques to fraud detection and financial risk management, covering data collection from transactional, behavioral, and market sources, feature engineering, model selection, and impact assessment. By analyzing large volumes of structured and unstructured financial data, AI algorithms can identify suspicious patterns, predict the probability of default and credit risk, and quantify the effectiveness of fraud-control interventions. The proposed approach is designed to help banks, NBFCs, fintech companies, and regulators make faster, more accurate, and more transparent risk decisions, while reducing financial losses and compliance costs. The paper outlines the methodology, presents a modular framework architecture, and discusses a pilot case study along with the scope for future research.