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“Model risk” books examine the uncertainty and potential losses that arise when organizations rely on quantitative, statistical, or AI/ML models to make decisions, especially in finance and other regulated industries. This category covers sources of error (poor data, flawed assumptions, overfitting, misuse, drift), consequences (mispricing, mis‑hedging, capital and compliance failures), and the full model lifecycle: design and conceptual soundness, development and calibration, backtesting and benchmarking, sensitivity and stress testing, independent validation and challenge, ongoing monitoring, documentation, and change control. Titles often frame governance and regulation (e.g., supervisory expectations like SR 11‑7 and Basel), present case studies of model failures, and offer practical mitigation techniques such as uncertainty quantification, robustness checks, challenger models, and explainability. With the rise of AI, they also address fairness, interpretability, data lineage, and MLOps. Intended readers include risk managers, quants, data scientists, auditors, and executives.

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