Applying simulation optimization to dynamic financial analysis for the asset–liability management of a property–casualty insurer

Abstract

The Dynamic Financial Analysis (DFA) system is a useful decision-support system for the insurer, but it lacks optimization capability. This article applies a simulation optimization technique to a DFA system and use the enhanced system to search an Asset–Liability Management (ALM) solution for a Property–Casualty (P&C) insurance company. The simulation optimization technique used herein is a Genetic Algorithm (GA), and the optimization problem is a constrained, multi-period asset allocation problem that takes account of insurance liability dynamics. We find that coupling a DFA system with simulation optimization results in significant improvements over the search method currently available to the DFA system. The results were robust across random number sets. Furthermore, the resulting asset allocations changes with the asset–liability setting in a way that is consistent with the differences in the settings. Applying simulation optimization to a DFA system is therefore promising.