Using Monte Carlo Simulation to Predict Captive Insurance Solvency

Abstract

The solvency of captive insurance is the key financial metric captive managers care about. We built a solvency prediction model for a captive insurance fund using Monte Carlo simulation with the fund's historical losses, current financial data and setups. This model can predict the solvency score of the current captive fund using the fund survival probability as a measurement of solvency. If the simulated future solvency ratios break the upper and lower bounds, we count it as an insolvent case; otherwise, it is counted a solvent (or survival) case. After large scale simulation, we can approximate the future survival probability, i.e. the solvency score, of the current captive fund. The predicted income statements, the balance sheets and financial ratios, will also be generated. We use a heat-map to visualize the solvency score at each retention level so that it can provide support to captive insurance managers to make their decisions. This model is implemented in Excel VBA macro and MATLAB.