In today’s evolving insurance landscape, growing portfolio sizes and complex models are increasing computational demands. As regulatory reporting, risk management, and capital management all require timely and accurate model runs, both runtime efficiency and information availability have become critical priorities. This paper explores emerging clustering techniques for model compression, offering significant computational efficiency gains and providing practical solutions. The paper describes cluster modeling and introduces a new way to use it, allowing the algorithm to target specified tolerances that can be defined for each model fit variable. We also present a novel second method, “replicating policies,” which uses an optimization routine to construct a small portfolio of insurance policies that closely matches key characteristics of the full portfolio within specified tolerances.

Highlights

• Overview of cluster modeling and its typical applications.
• Introduction of the “replicating policies” approach using an optimization routine.
• Case studies of fixed indexed annuities and endowment/annuity portfolios.
• Best practices for practical implementation, including validation, calibration, automation, stakeholder communication, and cost-benefit evaluation.