Model Documentation

A concise articulation of the model's intended use, business context, and scope. This section defines the specific financial product, decision point, or risk being addressed, and explicitly states the model's materiality classification (high, medium, low risk) per the institution's model risk tiering framework. It must clearly delineate what the model does and does not cover, preventing scope creep during validation. The summary also identifies the accountable model owner and the business unit responsible for its performance.

A rigorous justification of the chosen modeling methodology, grounded in financial theory or statistical learning principles. This section must explain why a specific algorithm—such as a gradient-boosted tree for tabular fraud data or a graph neural network for collusion detection—is appropriate for the problem. It includes a review of relevant academic literature, industry practice, and the key assumptions underpinning the model. For machine learning models, this covers the loss function, optimization algorithm, and regularization strategy.

A complete inventory of all input data, including source systems, extraction logic, and refresh frequency. This section documents the data lineage from origin to feature, listing every transformation, join, and aggregation. It must specify the observation window (training period) and the performance window (outcome period), along with the rationale for any exclusion rules. Data quality metrics—completeness, accuracy, and timeliness—are reported here, along with the treatment of missing values and outliers.

A detailed technical specification of the model's mathematical structure. For a neural network, this includes the number of layers, activation functions, dropout rates, and hyperparameter configurations. For tree-based models, it documents maximum depth, learning rate, and subsampling ratios. This section must provide the final fitted parameters or weights, the feature engineering pipeline, and any interaction terms or embeddings. It serves as the definitive blueprint for reproducing the model.

A comprehensive report of the model's predictive power, discrimination, and calibration. Key metrics include:

• AUROC and AUPRC for ranking quality
• Precision-Recall curves at operational thresholds
• Population Stability Index (PSI) for distributional shift
• Backtesting results comparing predictions to actual outcomes
• Segment-level performance across customer demographics, transaction types, and geographies Benchmarking against a simpler challenger model or heuristic is mandatory to demonstrate added value.

An honest, unvarnished disclosure of the model's known weaknesses and boundary conditions. This includes:

• Extrapolation risk outside the training distribution
• Sensitivity to specific features or data quality issues
• Edge cases where the model is known to fail
• Ethical considerations and fairness testing results The section also documents the human override protocol, defining the circumstances under which an operator may reverse a model decision and the governance around such overrides.