Modeling and Predicting of Motor Insurance Claim Amount using Artificial Neural Network

Abstract

In India the insurance industry is in its growth stage. It consists of 58 insurance companies of which 24 in life and 34 are non-life insurance. The Non-life Insurance companies which cater to motor insurance business presently utilize different trend models to forecast paid claim amount. Motor Insurance Claim amount prediction is one of the most difficult tasks to accomplish in financial forecasting due to the complex nature of data points. The main objective of this study is to determine a reliable time series forecasting model to predict own damage (OD) claim amount of motor insurance data in India from 1981 to 2016. In this context, the annual time series claim data was collected and modeled by using the Generalized linear model (GLM), Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) method. The validation of the model has been done by comparison of predicted and actual values for the period of 36 years. Also, different types of possible models were evaluated using Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) for accuracy. The results showed that ANN outperformed other traditional time series models (GLM & ARIMA) for predicting the future own damage claim amount with a lesser residual error. Further, the outcome of these data analytics studies would help Insurance companies to have an idea about the expected future claim amounts with more accuracy. Thus, predicting the Motor insurance's own damage claim will help insurance companies to budget their future revenue.