Explainable AI in Financial Fraud Detection Systems
Abstract
This paper addresses the integration of explainable AI (XAI) techniques in financial fraud detection systems to enhance transparency and trust. A hybrid model combining machine learning algorithms with SHAP (SHapley Additive exPlanations) is proposed to detect anomalies in transactional data. The approach provides interpretable insights into model predictions, aiding financial institutions in decision-making and regulatory compliance. Experimental results demonstrate improved fraud detection rates with actionable explanations for end-users.
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