Article 244 : Machine Learning Elastic Net Regression in Python for Actuarial Science: End-to-End Case Studies

This article shows how Elastic Net Regression can be applied in actuarial science to improve prediction accuracy, manage correlated predictors, and deliver interpretable models across life, health, and general insurance case studies.

Article Outline

1. Introduction to Elastic Net Regression in Actuarial Science

• Importance of predictive modeling in actuarial tasks such as pricing, reserving, and risk classification.

• The problem of multicollinearity among actuarial predictors (e.g., age, policy duration, claims frequency, exposure).

• Why Elastic Net is valuable: combining Ridge (stability) and Lasso (feature selection).

2. Applications of Elastic Net in Actuarial Science

• Life Insurance: modeling mortality risk using demographic and lifestyle factors.

• Health Insurance: predicting medical claim severity from age, comorbidities, and policy features.

• General Insurance (P&C): estimating motor insurance claim frequency and cost based on driver and vehicle data.

• Pension and Retirement: forecasting annuity payouts with correlated economic and demographic variables.

3. Mathematical Foundation of Elastic Net Regression

• Review of the Elastic Net objective function.

• Explanation of tuning parameters:

  • λ (alpha in sklearn) controls regularization strength.

  • l1_ratio determines balance between Ridge and Lasso.

• Why this hybrid is ideal for actuarial datasets with overlapping explanatory factors.

4. Setting Up the Python Environment

• Required libraries: numpy, pandas, scikit-learn, matplotlib, seaborn.

• Brief notes on reproducibility (random seed, cross-validation).

5. Creating Actuarial Datasets (Simulated for Case Studies)

• Case Study 1 (Life Insurance Mortality Risk): mortality modeled by age, BMI, smoking, and medical history indicators.

• Case Study 2 (Health Insurance Claim Severity): claim costs modeled from policyholder demographics and health utilization variables.

• Case Study 3 (Motor Insurance Claim Frequency/Cost): accident frequency modeled by driver age, vehicle age, mileage, and risk factors.

6. Preprocessing Actuarial Data

• Scaling and standardization of predictors with StandardScaler.

• Train-test split for robust evaluation.

• Handling categorical actuarial features (e.g., smoker status, vehicle type).

7. Building Elastic Net Regression Models in Python

• Using ElasticNetCV for hyperparameter tuning.

• Selecting best α and l1_ratio.

• Fitting models for each case study.

8. Case Study 1: Life Insurance Mortality Risk

• Train and evaluate Elastic Net.

• Report R² and RMSE.

• Interpret coefficients: age and lifestyle factors driving mortality.

9. Case Study 2: Health Insurance Claim Severity

• Fit Elastic Net to claim severity data.

• Evaluate performance.

• Coefficient interpretation: impact of demographics and comorbidities on claim costs.

10. Case Study 3: Motor Insurance Claim Frequency

• Apply Elastic Net to predict accident frequency or severity.

• Evaluate performance.

• Insights into driver and vehicle characteristics.

11. Comparison with Ridge and Lasso

• Side-by-side results across case studies.

• Visualize R² and RMSE by model type.

• Discussion: Elastic Net as a practical balance for actuaries.

12. Advantages and Limitations in Actuarial Applications

• Strengths: stability, feature selection, interpretability, regulatory friendliness.

• Weaknesses: assumes linearity, requires tuning, sensitive to scaling.

13. End-to-End Python Script (All Case Studies)

• Unified workflow integrating dataset creation, preprocessing, model training, evaluation, and comparison.

14. Conclusion

• Summary of Elastic Net’s role in actuarial modeling.

• Emphasis on handling correlated actuarial factors.

• Outlook: integration with GLMs, GLMMs, and survival analysis for extended actuarial applications.

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