AI-Driven Fraud Detection in Financial Markets: Predictive Modeling for Risk Mitigation and Compliance Enhancement

The complexity and velocity of financial market activities have heightened the risk of sophisticated fraudulent practices. Traditional rule-based surveillance systems often struggle to adapt to evolving threat patterns, resulting in delayed detection and increased financial and reputational risks. With the advent of artificial intelligence (AI) and machine learning (ML), financial institutions and regulators are now positioned to proactively identify anomalies and mitigate risks through predictive modeling approaches. This paper investigates the transformative role of AI and predictive modeling in modern fraud detection within financial markets. The research evaluates the effectiveness of supervised and unsupervised learning models for dynamic fraud detection and risk scoring. Furthermore, the paper proposes a predictive fraud detection framework designed to provide real-time risk assessments, enhance regulatory compliance, and enable faster investigative actions. Ultimately, this study advocates for the strategic adoption of AI technologies to fortify financial market integrity against current and future fraud threats.