مهدی آزادی مطلق

The application of machine learning has become indispensable in the critical domain of financial fraud detection. However, a major limitation of traditional models is their "black box" nature, which obscures the reasoning behind a flagged transaction. This lack of transparency often leads to many false positives, which can undermine customer trust and incur substantial operational expenses. To address this challenge, this paper proposes a novel framework for Explainable Anomaly Detection in financial fraud, using advanced Explainable AI (XAI) techniques to provide clear insights into the model's predictive processes. Our approach is designed to move beyond a simplistic binary output of "fraud/no fraud." Our framework combines advanced anomaly detection models (e.g., Isolation Forests and Deep Autoencoders) with model-agnostic explanation methods such as SHAP and LIME, to clearly show which features contribute to a transaction’s anomaly score. The efficacy of our framework has been evaluated using a financial transaction benchmark dataset. The results show that integrating XAI not only makes the system more transparent and trustworthy, but also improves the efficiency of fraud investigations. Based on these results, our method reduces the time and resources needed for manual reviews, while still maintaining high accuracy in detecting fraudulent activities.