The most predictive model is not always the one an insurer can actually use, according to a new analytical report from InsurTech firm Earnix.
Machine learning models are adept at uncovering complex relationships in data, frequently outperforming traditional approaches on accuracy. Yet in insurance pricing, Earnix argues, accuracy is only one piece of the puzzle. Pricing models must also be transparent, straightforward to review and appropriate for regulatory filing.
The challenge, as Earnix frames it, is preserving the predictive power of machine learning while producing rating structures that are practical to govern, explain and implement.
The post forms part of the Earnix analytical and technical series, which examines pressing analytical challenges facing insurers and banks.
Previous instalments have covered model analysis, Auto-XGBoost, smart grouping, hierarchical level selection and KPI-focused data monitoring.
This latest entry introduces a new capability within the firm’s Model to Rating Structure Distillation lab, which automatically translates machine learning models into production-ready rating structures for Price-It.
According to Earnix, modern ML models capture non-linear relationships and subtle predictive signals that simpler models miss, but insurers rarely deploy them directly. Internal stakeholders must understand how pricing decisions are made, governance teams need reviewable models, and regulators often demand pricing logic expressed as transparent rating tables. An actuary might build a highly accurate model using advanced tree-based algorithms, only to find the final structure must still be rendered as straightforward rating tables. Building those manually is time-consuming, subjective and often sacrifices accuracy for simplicity.
The Earnix lab addresses this by generating candidate rating structures that closely approximate a machine learning model’s behaviour, which can then be reviewed, compared and exported into Price-It. Crucially, the process is guided by business constraints rather than pure automation, with users able to define parameters such as monotonicity, offsets, weights and interaction limits to ensure alignment with organisational and regulatory requirements.
Rather than delivering a single answer, the lab produces multiple candidates evaluated from different perspectives. Some are intentionally simple, using interpretable additive approaches such as Earnix AGLM and Explainable Boosting Machines, with regularisation discouraging unnecessary complexity. Others are more expressive, deploying CatBoost as a residual learner atop a simpler base model, with a post-processing LASSO step stripping out splits and tables that add little value.
Earnix stresses there is no universal answer to the trade-off between accuracy and simplicity. Some organisations favour compact structures that are easy to explain and maintain, while others accept greater complexity to retain predictive signal. The lab, the firm says, lets pricing teams weigh interpretability, governance and performance against their own priorities, helping them move confidently from advanced analytics to real-world implementation.
For more, read the full story here.
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