Clustering Techniques for Model Compression in Insurance

This technical report discusses clustering techniques and their practical implementation in the insurance sector, focusing on model compression methods to enhance computational efficiency. The document outlines the challenges faced by actuaries in managing large portfolios and the necessity for timely and accurate model runs due to increasing computational demands. It introduces cluster modeling as a solution, detailing its methodology, including the creation of model cells that approximate cash flows and other values. The report also presents a new approach to cluster modeling that targets specified tolerances for model fit variables and introduces a method called 'replicating policies' for optimizing portfolio characteristics. Additionally, the report includes case studies that demonstrate the application of these techniques, highlighting their effectiveness in reducing runtime for various insurance calculations. The findings suggest that these methods can be broadly applicable beyond insurance, potentially benefiting other sectors with large data sets.