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Ratemaking

The actuarial process of determining insurance prices (rates) based on expected losses, expenses, and profit.

technicalPublished 2026/06/05

FAQs

What is ratemaking?
The actuarial process of setting insurance prices to cover expected losses, expenses, and profit, based on historical data and risk factors.
Why does explainability matter in AI ratemaking?
Rates are filed with and approved by regulators who require justifiable, non-discriminatory, explainable pricing — so black-box models can't be used.

Related Terms

  • Predictive Underwriting

    Predictive underwriting uses machine learning on historical and external data to forecast a risk's likely loss outcome, helping underwriters price and select

  • Explainable AI (XAI)

    Explainable AI refers to AI systems whose decisions can be understood, articulated, and audited by humans

  • Combined Ratio

    A carrier profitability metric: incurred losses plus expenses divided by earned premium. Below 100% means underwriting profit; above means a loss.

Related Items

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    AI pricing and rate modeling for actuaries

  • Hyperexponential

    Pricing decision platform for specialty insurers

  • Earnix

    AI rating, pricing optimization and decisioning

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Ratemaking is the actuarial discipline of setting insurance prices. At its core, a rate must cover expected losses, expenses, and a profit margin — priced so the carrier remains solvent and competitive. Actuaries build models from historical loss data, exposure information, and risk factors to determine what to charge for a given risk.

Traditional ratemaking relies on established actuarial techniques — segmenting risks into classes and setting rates per class. The limitation is granularity: classical methods can only handle so many variables before becoming unwieldy, potentially leaving pricing coarser than the risk warrants. This creates opportunity for competitors who price more precisely.

AI and machine learning expand ratemaking's precision dramatically. ML models can incorporate far more variables and detect non-linear patterns, producing more granular, accurate rates — identifying underpriced and overpriced segments classical methods miss. Specialized insurance pricing platforms let actuaries build, test, and deploy sophisticated models faster than traditional tools.

The critical constraint is regulation and explainability. Insurance rates are filed with and approved by regulators, who require that rates be justified, not unfairly discriminatory, and explainable. A pricing model that's a black box can't be filed. This is why transparent, auditable ML — models whose logic actuaries can articulate — is specifically valuable in ratemaking, and why this category emphasizes explainability heavily. For carriers, better ratemaking is a direct competitive and profitability lever, bounded by regulatory approval.