Transforming Fraud Detection in Banking with Explainable AI : Enhancing Transparency and Trust - Sivaranjani, S.; Hassan S. , Noorul (University of Science and Computer Technology, 2025)
As financial fraud grows in complexity, banks are turning to artificial intelligence (AI) to identify and avert fraudulent actions. Nevertheless, the conventional black-box AI models often lack clarity, leading to issues related to trust, adherence to regulations, and customer assurance. This paper investigates the function of Explainable AI (XAI) in revolutionizing fraud detection in the banking industry by connecting algorithmic clarity with stakeholder confidence. We analyze how XAI improves fraud detection systems by making AI-generated decisions more understandable for regulators, auditors, and customers while preserving high levels of detection accuracy. By promoting transparency, accountability, and trust, XAI is transforming the financial sector’s strategy for addressing fraud. Traditional rule-based systems are no longer sufficient due to the growing complexity of hackers, which is why banks are adopting AI-driven and machine learning solutions. However, many sophisticated models' opacity presents serious difficulties for risk management and regulatory compliance. This gap is filled by explainable AI, which offers insights into decision-making processes and produces outputs that are easy to understand without sacrificing predictive ability. Integrating XAI into fraud detection systems becomes crucial as customers want more comfort regarding the handling of their financial data and regulatory bodies demand greater responsibility. This report emphasizes how important XAI is to improving operational resilience and bolstering consumer and bank trust.