Automated Model-Risk Monitoring for Adaptive Credit, Fraud, and Early-Warning Models

Abstract

Financial institutions typically operate several families of machine-learning models side by side credit-scoring models, fraud-detection models, and early-warning models for portfolio or counterparty distress each evaluated on its own native performance metric: discriminatory power for credit scoring, precision at a fixed alert budget for fraud detection, and lead time before a confirmed distress event for early warning. Because these native metrics are not measured on a common scale, an institution’s model-risk function is typically forced to build and maintain separate monitoring logic, separate thresholds, and separate alerting rules for each model family, an arrangement that scales poorly as the number of adaptive models grows and that gives senior model-risk officers no single view of which models, across the entire portfolio, are most in need of attention at a given moment. This paper develops Cross-model Health and Risk Tracking (CHART), a framework that applies established statistical process control methods the cumulative sum (CUSUM) control chart and the exponentially weighted moving average (EWMA) control chart not to any single model’s native metric directly, but to a standardized model health index constructed by expressing each model family’s native metric as a deviation from its own established baseline, so that credit-scoring, fraud-detection, and early-warning models can be monitored on one common statistical scale, alerted through one shared thresholding logic, and displayed on one unified dashboard, without requiring that their underlying metrics be comparable in the first place. Following a design-science research approach, the framework is illustrated, not validated, through two comparative tables, a hypothetical case scenario, and four illustrative figures, with every quantitative value explicitly and repeatedly labeled as an illustrative estimate constructed from the cited literature’s directional findings and from established statistical process control theory rather than as measured data from a deployed monitoring system. The paper concludes with a discussion of CHART’s practical implications for financial institutions operating heterogeneous portfolios of adaptive models, its assumptions and limitations, and directions for future empirical research.