Earnix bridges the gap between ML accuracy and regulation

According to insurtech pricing specialist Arnix, the most predictive models that insurers build are often the ones that can’t actually be operationalized.

Machine learning models excel at uncovering complex nonlinear relationships in data, often outperforming traditional actuarial approaches. However, raw accuracy is only half the battle when it comes to insurance pricing. Models must also be transparent, available for review by governance teams, and suitable for regulatory submissions.

Earnix explored this tension in the latest installment of our analysis and technology series. This series has previously covered topics such as model analysis, Auto-XGBoost, smart grouping (Auto-GLM), hierarchy level selectors, and data monitoring with a focus on KPIs.

The problem, Arnix explained, is that regulators and internal stakeholders often require pricing logic expressed as a simple rating sheet. Actuaries may use sophisticated algorithms to build highly accurate models, but manually converting them into these tables is time-consuming, subjective, and usually means sacrificing predictive accuracy for simplicity. In other words, the best performing models are rarely the easiest to operate.

To fill this gap, Earnix has launched a new capability within the Model-to-Evaluation Structure Distillation Lab. It automatically transforms machine learning models into production-ready evaluation structures on the Price-It platform. In the lab, we start with an existing model and generate candidate evaluation structures that closely approximate the behavior of the original model. Evaluation structures can be exported directly for review, comparison, and implementation.

Importantly, processes are controlled by business constraints rather than pure automation. Pricing teams can set parameters such as monotonicity, offsets, weights, and interaction limits to ensure outputs match organizational and regulatory requirements. Rather than providing a single answer, the lab generates multiple candidates that can be judged by how closely they reflect the source model and how accurately they predict real-world outcomes.

Earnix emphasized that there is no universal answer to the trade-off between accuracy and simplicity. Some candidates are intentionally simple, built on interpretable additive approaches such as Earnix AGLM and Explainable Boosting Machines, and regularization avoids unnecessary complexity. Others are more expressive and use CatBoost as a residual learner on a simpler base model, removing folds and tables of little value in a post-processing LASSO step.

The result is a set of practical options that enable insurers to balance interpretability, governance, and predictive performance according to their own priorities, enabling pricing teams to confidently move from advanced analytics to real-world deployment.

For more information, read the full article here.

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