The client needed to validate a pricing model to determine automobile premiums for policyholders based on their risk profiles and historical claim experience.

Solytics conducted a comprehensive validation of the Auto Insurance Pricing model being used by the FI in a structured manner:

1. Data Integrity: Validated the data preprocessing steps, including data cleaning, feature encoding, and normalization, to ensure data integrity and consistency. Verified that missing values are handled appropriately, and categorical variables are encoded correctly to avoid bias in model training.

2. Model Selection: Validated the performance of different machine learning algorithms (e.g., GLMs, decision trees, GBMs) using cross-validation techniques.

3. Model Training: Validated the model training process on a training dataset to ensure convergence and optimization of model parameters.

4. Model Evaluation: Validated the calibration of the pricing model's predicted probabilities with observed claim frequencies through calibration plots, reliability diagrams, and statistical tests (e.g., Hosmer-Lemeshow test), ensuring that predicted probabilities accurately reflect the true likelihood of insurance claims.

5. Reporting and Documentation: Documented the validation process, methodologies, findings, and recommendations in a comprehensive validation report in line with SR 11-7 guidelines and industry best practices.

Performed Model Validation within stringent deadlines and submitted a SR 11-7 compliant comprehensive model document Defined minimum standards for sensitivity methods for different asset classes, products, risk factors and scenarios.