A Study on Financial Fraud Detection using Explainable AI

With the rapid growth of digital banking, online payments, and cashless transactions, financial fraud has become a major challenge for banks and financial institutions. Fraudsters continuously develop new techniques, making traditional rulebased fraud detection systems ineffective and outdated. In recent years, machine learning and deep learning models have shown strong potential in identifying fraudulent transactions by learning complex patterns from large volumes of financial data. However, most of these models function as black-box systems, meaning their decisions are difficult to understand and explain. This lack of transparency creates trust issues for users and raises concerns regarding regulatory compliance in the financial sector. This study focuses on financial fraud detection using a combination of stacking ensemble learning and Explainable Artificial Intelligence (XAI) techniques. By integrating multiple high-performing machine learning models, the stacking approach improves detection accuracy and handles challenges such as class imbalance and evolving fraud patterns. At the same time, explainability methods such as SHAP, LIME, and feature importance analysis are used to provide clear insights into how and why a transaction is classified as fraudulent or legitimate. The study highlights that combining high accuracy with interpretability is both achievable and necessary for modern fraud detection systems. By improving transparency and trust, explainable fraud detection models can better meet real-world operational and regulatory requirements