Mitigating Explanation Leakage in Financial Fraud Detection Systems
Researchers examine how explainability mandates for financial fraud models can leak sensitive data in federated-learning setups — a friction point for EU-regulated institutions balancing transparency with privacy.
Summary written by editorial AI · Source link below
arXiv:2608.22607v1 Announce Type: cross Abstract: Financial fraud detection relies heavily on centralized machine learning models. This creates serious data privacy risks. Federated Learning (FL) decentralizes data processing, but financial regulations still require models to be transparent. This means using Explainable AI (XAI) tools such as TreeSHAP. Recent cybersecurity research shows a problem with this approach. Sharing high-fidelity SHAP explanations exposes the federated network to Membe
Editorial Analysis
Financial institutions deploying federated learning must reconcile DORA's transparency expectations with the risk that model explanations leak training data, creating a compliance tightrope.
Audit explainability outputs of federated-learning fraud-detection systems for information leakage before regulatory deadlines.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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