Explainable AI Framework Boosts Auditor Confidence in Banking Anomaly Detection

Jul 16, 2026

A new preprint introduces an explainable AI (XAI) framework for detecting anomalies in banking transactions, combining Isolation Forest with SHAP explanations. Tested on synthetic data, the system achieved 0.91 precision and 0.88 recall, outperforming other unsupervised methods. A Streamlit dashboard delivers feature-level explanations, and expert feedback indicates these explanations improve auditor confidence and decision quality.

Why it matters: This work shows that explainable AI can enhance trust and effectiveness in automated fraud detection for financial audits.

Full story at: arXiv Machine Learning ↗