The most predictive model an insurer builds is often the one it can never put into production, according to InsurTech pricing specialist Earnix.
Machine learning models are adept at uncovering complex, non-linear relationships in data, frequently outperforming traditional actuarial approaches. Yet in insurance pricing, raw accuracy is only half the battle. Models must also be transparent, reviewable by governance teams and suitable for regulatory filing.
Earnix explored this tension in the latest instalment of its analytical and technical series, which has previously covered topics such as Model Analysis, Auto-XGBoost, Smart Grouping (Auto-GLM), the Hierarchical Level Selector and KPI-focused data monitoring.
The problem, Earnix explained, is that regulators and internal stakeholders often demand pricing logic expressed as straightforward rating tables. An actuary may build a highly accurate model using advanced algorithms, but converting it into those tables manually is slow, subjective, and typically means sacrificing predictive accuracy for the sake of simplicity. The models that perform best, in other words, are rarely the easiest to operationalise.
To close that gap, Earnix has launched a new capability within its Model to Rating Structure Distillation lab, which automatically translates machine learning models into production-ready rating structures for its Price-It platform. Starting from an existing model, the lab generates candidate rating structures that closely approximate the original model’s behaviour, which can then be reviewed, compared and exported directly for implementation.
Crucially, the process is steered by business constraints rather than pure automation. Pricing teams can set parameters such as monotonicity, offsets, weights and interaction limits to ensure outputs align with organisational and regulatory demands. Rather than delivering a single answer, the lab produces multiple candidates that can be judged on how faithfully they mirror the source model and how well they predict real-world outcomes.
Earnix stressed there is no universal answer to the trade-off between accuracy and simplicity. Some candidates are deliberately simple, built on interpretable additive approaches such as Earnix AGLM and Explainable Boosting Machines, with regularisation discouraging unnecessary complexity. Others are more expressive, using CatBoost as a residual learner on top of a simpler base model, with a post-processing LASSO step stripping out splits and tables that add little value.
The outcome is a practical set of options that lets insurers balance interpretability, governance and predictive performance according to their own priorities, helping pricing teams move confidently from advanced analytics to real-world deployment.
For more, read the full story here.
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