Bayesian nonparametric predictive modeling of group health claims

Models commonly employed to fit current claims data and predict future claims are often parametric and relatively inflexible. An incorrect model assumption can cause model misspecification which leads to reduced profits at best and dangerous, unanticipated risk exposure at worst. Even mixture models may not be sufficiently flexible to properly fit the data. Using a Bayesian nonparametric model instead can dramatically improve claim predictions and consequently risk management decisions in group health practices. The improvement is significant in both simulated and real data from a major health insurer's medium-sized groups. The nonparametric method outperforms a similar Bayesian parametric model, especially when predicting future claims for new business (entire groups not in the previous year's data). In our analysis, the nonparametric model outperforms the parametric model in predicting costs of both renewal and new business. This is particularly important as healthcare costs rise around the world.