Systems and methods for forecasting loss metrics

Abstract

Methods and systems are provided for predicting and forecasting loss metrics for insurance. One or more models are created to generate development curves to predict ultimate losses for aggregations of long-tail losses, such as bodily injury claim payouts based on the first few months of payout data and other relevant variables. The relevant variables include internal data about policyholders and claims, and external data. Historical data, including potential influential variables and a target, are used to train a predictive development model. The variables are pre-processed and aggregated to an accident-month granularity, then feature reduction techniques are applied to determine the variables that exert the most influence on the target. Dimensionality reduction techniques are then applied to the remaining variables. The most influential variables and the variables created by dimension reduction are used as the input features to train the development model. One or more additional models are trained to forecast future pure premiums (or a different loss metric) based on ultimate losses predicted by the development model, and other relevant variables.