Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces an Explainable Artificial Intelligence (XAI) framework specifically designed for anomaly detection in banking transactions, addressing the limitations of traditional rule-based systems that suffer from high false-positive rates and lack of interpretability. Utilizing an Isolation Forest model for unsupervised anomaly scoring combined with SHAP for feature-attributed explanations, the framework enhances the decision-making process for internal audit teams. Evaluation results show a precision of 0.91 and recall of 0.88 on a synthetic banking dataset, with expert feedback indicating that the feature-level explanations significantly boost auditor confidence and decision quality.
Feature-level explanations in anomaly detection can transform auditor confidence and decision-making in banking compliance.
The banking sector increasingly relies on automated systems to monitor electronic transactions for signs of fraud, yet conventional rule-based approaches struggle with high false-positive rates and offer no justification for their outputs, limiting their utility for compliance teams. This paper introduces an Explainable Artificial Intelligence (XAI) framework tailored for banking transaction anomaly detection within internal audit workflows. An Isolation Forest (iForest) model performs unsupervised anomaly scoring, while a SHAP (SHapley Additive exPlanations) layer provides transaction-level, feature-attributed explanations grounded in cooperative game theory [8]. A lightweight Streamlit dashboard renders these outputs in a form accessible to audit professionals without machine learning expertise. Evaluation on a synthetic banking dataset yields 0.91 precision and 0.88 recall, outperforming three unsupervised baselines. Expert feedback confirms that feature-level explanations measurably improve auditor confidence and decision quality. The framework advances the practical deployment of accountable, transparent AI in regulated financial environments.