Model to Rating Structure Distillation: Turning Complex ML Models into Simple Tables
Yuval Ben Dror, Data Science Researcher & Yitzhak Yahalom, Senior Data Science Researcher
Earnix
June 25, 2026
The most accurate predictive model isn't always the one insurers can put into production.
Machine learning models excel at identifying complex relationships in data, often delivering significantly better predictive performance than traditional approaches. But in insurance pricing, accuracy is only part of the equation. Pricing models must also be transparent, easy to review, and suitable for regulatory filing.
So how can insurers preserve the predictive power of machine learning while producing rating structures that are practical to govern, explain, and implement?
Earnix Analytical and Technical Blog Series
How can insurers and banks tackle today's toughest analytical challenges?
At Earnix, we believe it starts with asking the right questions, challenging assumptions, and finding better ways forward.
In this blog series, we explore key challenges in financial analytics—from improving predictive models to simplifying operational workflows and helping organizations stay competitive. Previous posts explored topics including Model Analysis, Auto-XGBoost, Smart Grouping (Auto-GLM), Hierarchical Level Selector, and KPI-focused Data Monitoring.
In this post, we introduce a new capability available as part of the Model to Rating Structure Distillation lab: automatically translating machine learning models into production-ready rating structures for Price-It.
When Predictive Performance Meets Operational Reality
Modern machine learning models capture non-linear relationships, complex interactions, and subtle predictive signals that simpler models often miss. This makes them powerful tools for improving pricing accuracy.
However, insurers rarely deploy these models directly into production.
Pricing structures must satisfy a broader set of business requirements. Internal stakeholders need to understand how pricing decisions are made, governance teams need models that can be reviewed, and regulators often require pricing logic to be expressed as transparent rating tables.
Consider an actuary building a highly accurate pricing model using advanced tree-based algorithms. While the model may deliver excellent predictive performance, the final pricing structure may still need to be represented as a straightforward set of rating tables.
Creating those tables manually is possible, but it's also time-consuming, subjective, and often requires sacrificing predictive accuracy in favor of simplicity.
This reflects a common challenge across insurance pricing: the models that perform best are rarely the easiest to operationalize.
From Machine Learning to Rating Structures
To help insurers bridge this gap, Earnix developed the Model-to-Rating Structure Distillation lab.
Rather than forcing pricing teams to choose between predictive accuracy and practical implementation, the lab creates a structured path between the two.
Starting with an existing machine learning model, it automatically generates candidate rating structures that closely approximate the model's behavior. These structures can then be reviewed, compared, and exported directly into Price-It for implementation.
The process is guided by business constraints rather than pure automation. Users can define parameters such as monotonicity, offsets, weights, and interaction limits to ensure the resulting rating structures align with organizational and regulatory requirements.
Instead of producing a single answer, the lab generates multiple candidate structures that can be evaluated from different perspectives, including how closely they reproduce the original model and how well they predict real-world outcomes.
A Flexible Approach to Complexity
There is no universal answer to the trade-off between predictive accuracy and structural simplicity.
Some organizations prioritize compact rating structures that are easy to explain, govern, and maintain. Others are willing to accept greater complexity if it better preserves the predictive signal of the original model.
Rather than assuming one approach fits every use case, the Model-to-Rating Structure Distillation lab enables pricing teams to explore multiple alternatives and select the one that best aligns with their business objectives.
Some candidate models are intentionally simple from the outset, using interpretable additive approaches such as Earnix AGLM and Explainable Boosting Machines (EBMs). Regularization techniques help discourage unnecessary complexity while preserving predictive performance.
Other candidates take a more expressive approach by using CatBoost as a residual learner on top of a simpler base model. The resulting tree structure naturally controls the dimensionality of interaction tables, while a post-processing LASSO step removes splits and tables that contribute little additional value.
The result is not a single automated recommendation, but a practical set of options that allow pricing teams to balance interpretability, governance, and predictive performance according to their own priorities.
From Prediction to Production
Machine learning continues to raise the bar for predictive performance, but production pricing models must satisfy much more than accuracy alone. They must also be explainable, governable, reviewable, and practical to deploy.
Model to Rating Structure Distillation helps bridge that gap by translating sophisticated machine learning models into transparent, production-ready rating structures.
By enabling insurers to preserve predictive performance while meeting operational and regulatory requirements, the lab helps pricing teams move more confidently from advanced analytics to real-world implementation.
Have questions? Get in touch with an expert.