Explainable Artificial Intelligence for Anomaly Detection in Banking Transactions: An Internal Audit Perspective

arXiv cs.AI · 18 hours ago

A framework combining Isolation Forest anomaly detection with SHAP explanations was developed to identify fraudulent banking transactions while providing auditors with feature-level justifications for flagged transactions. The model achieved 0.91 precision and 0.88 recall on synthetic banking data, outperforming three unsupervised baselines. The explainability layer measurably improved auditor confidence and decision quality in compliance workflows, enabling practical deployment of transparent AI in regulated financial environments.