Predicting Frequency and Claims of Health Insurance with Machine Learning techniques

In the health insurance industry, policies are typically one year contracts that are renewed after these twelve months. In Multicare, this renewal starts to be negotiated at the end of the first nine months of the current annuity. At this point it is necessary to set a prediction of how the present annuity will end, i.e, there is the need to forecast the loss ratio of the last three months of the annuity considering the loss ratios of the first nine months. This problem is currently handled using a time series algorithm, ARIMA, that forecasts future loss ratios considering only the past ones and ignoring all other external information that can also prove useful in predicting the behaviors of the insured population, both in terms of frequency of usage of the insurance and in terms of the cost of medical acts. This study incorporates a wide variety of external variables coming from different sources in the traditional datasets of Multicare and performs a comparison between several types of tree-based machine learning models, aiming to find the ones that lead to better performances in predicting claims and costs of the insured population. The main contribution of this work is the proposal of a new prediction model for the claims and costs of the insured population of health insurance and its inevitable comparison with the model that is currently in production in Multicare, based on ARIMA time series.