magnus919_agent-skills/actuarial-risk-modeling/references/applications-and-governance.md

Applications and Governance

Insurance pricing and risk classification

Define the target, exposure, rating period, permitted variables, and intended use before optimizing fit. Separate frequency, severity, and aggregate or pure-premium decisions. Check relativities on the scale used for the decision, portfolio mix, credibility of sparse segments, monotonicity or business constraints where required, and stability under likely mix changes. Predictive association does not settle fairness, causality, legality, or permitted classification.

Reserving and claims development

Record whether the data are reported, incurred, paid, or developed and which development information was available at each valuation date. Treat claims triangles as indexed observations with calendar, accident, and development structure, not ordinary IID rows. Compare methods against historical vintages where possible, quantify process and parameter uncertainty, and test sensitivity to changing settlement speed, inflation, case reserve practice, and large claims.

Credibility and sparse experience

Credibility is a partial-pooling problem: experience varies in reliability with volume, variance, homogeneity, and external information. State what is being pooled, the level of hierarchy, the prior or complement of credibility, and how uncertainty changes with exposure. Do not call a weighted blend “credibility” without defining the weights and estimand. Validate shrinkage decisions against out-of-sample or historical behavior and check sparse-group stability.

Solvency, capital, and financial risk

Define the loss horizon, confidence or tail functional, aggregation unit, dependence assumption, market or underwriting components, and action threshold. Distinguish VaR-like quantiles from expected shortfall or tail means. Backtest only what is observable at the relevant horizon and disclose regime limitations. Scenario and stress analysis should cover dependence, concentration, liquidity, model error, and data revisions where they matter to the decision.

Model risk controls

Assign an owner and independent reviewer. Record intended use, prohibited extrapolations, input lineage, assumptions, validation population, known blind spots, overrides, monitoring, incident handling, change approval, review cadence, and retirement criteria. Treat a model as a controlled decision component, not a permanent oracle.

Professional boundary

This skill can help structure analysis and surface questions. It does not confer actuarial credentials or authorize regulated practice. Apply the relevant professional standards, organizational policy, jurisdictional requirements, privacy rules, and qualified review. When those requirements are unknown, say so and stop short of a deployment or pricing recommendation.