This document summarizes practices and financial evaluation approaches that are applied before deployment of AI models in the financial sector. The focus is on validation during training, testing, or pre-release stages.
| Validation / Pre-release Activity | What’s Done / Measured | Why It Matters in Finance Context |
|---|---|---|
| Data quality & integrity checks | Detect missing data, outliers, and feature consistency; stress-test slices. | Financial models are very sensitive to distribution shifts; bad data leads to wrong risk/credit/fraud predictions milliman. |
| Back-testing / Out-of-sample performance | Historical simulation; compare predictions vs. actual outcomes. | Ensures models aren’t just overfitting; essential for risk and portfolio models cfa. |
| Cross-validation / Time-aware splits | Use purged cross-validation, walk-forward testing to avoid look-ahead bias. | Prevents overly optimistic results in time-series financial data wiki-pcv. |
| Hyperparameter tuning & model specification reviews | Explore architectures, parameters, feature sets. | Balances bias/variance, stability, and risk of extreme errors google-ml. |
| Stress testing / Scenario analysis | Evaluate under adverse conditions (e.g., downturns, shocks). | Core requirement for credit and market risk models milliman. |
| Fairness, bias, regulatory compliance checks | Check group fairness, regulatory adherence. | Prevents legal/regulatory exposure in lending, underwriting, etc. empowered. |
| Model explainability / interpretability | Use explainability tools (feature attribution, local explanations). | Required for auditability and trust in regulated financial contexts fiddler. |
| Offline metrics linked to business KPIs | Validate that accuracy, AUC, precision, etc. correlate with expected business outcomes. | Avoids models that look “good” technically but fail financially google-ml. |
| Gate / Release sign-off criteria | Require thresholds across categories (edge cases, rare events, slices). | Provides a governance checkpoint before production indium. |
| Synthetic / simulation data evaluation | Generate artificial or simulated market/fraud data to test rare events. | Helps evaluate resilience under tail risks jpm-synth. |
- AI Validator roles in banks: Dedicated teams perform adversarial testing, subgroup fairness checks, and interpretability validation before release fiddler.
- Finance Agent Benchmark: Evaluates foundation models on finance-analyst tasks (retrieval, Q&A) as a pre-use benchmark vals.
- CFA Investment Model Validation: Professional guidance for validation in investment management (back-testing, hold-out analysis, governance) cfa.
- Empowered Systems: Outlines validation strategies—documentation, input validation, governance—tailored to financial services empowered.
- JP Morgan synthetic data: Uses synthetic equity market data for safe, repeatable pre-deployment model testing jpm-synth.