Explainable AI in Financial Institutions for Fraud and Risk Mitigation

The increasing complexity of AI models used in financial institutions has raised significant concerns regarding transparency, compliance, and trust. Explainable Artificial Intelligence (XAI) addresses these challenges by offering insights into how and why AI systems arrive at specific decisions—especially in high-stakes domains like fraud detection, risk scoring, and regulatory compliance. This paper explores the role of XAI in detecting financial fraud and insider trading, enhancing Anti-Money Laundering (AML) systems through interpretable risk scoring mechanisms, and ensuring legal adherence to data protection laws such as the GDPR, particularly the "Right to Explanation." Through real-world applications, architectural strategies, and regulatory case studies, the paper underscores the necessity of embedding explainability into the AI life cycle for ethical and secure financial operations.