How to spot outliers: an Ensemble Anomaly Detection Framework

Errors in risk valuation outputs arising from data-feed failures, model misconfiguration, or system malfunctions can propagate undetected through an investment bank's risk infrastructure and generate material operational losses. Using proprietary daily credit-derivatives data from a major global investment bank covering 183 trades across 129 trading days, we design, implement, and empirically evaluate the Ensemble Quality Assessment Framework (EQAF), a layered unsupervised architecture that combines complementary outlier-detection methods to monitor risk calculation integrity in real time. Using a controlled anomaly-injection protocol with eight operationally realistic scenarios, we show that the calibrated ensemble achieves F1 scores of 61-79%, substantially outperforming the best individual method (6-66%) across four distinct risk-measure datasets. Improvements of 4-6 percentage points in AUC-ROC confirm that this advantage is robust to threshold selection. We further demonstrate that purely statistical detection methods systematically fail to identify stale-value anomalies, a class of frozen-feed errors in which valuation outputs are identical to prior observations and therefore indistinguishable from normal data, and that domain-specific deterministic rules are architecturally indispensable. These findings have direct implications for model risk management under Basel III and the Fundamental Review of the Trading Book (FRTB), where automated and auditable quality controls for internal risk models are increasingly required.