Abstract
The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting third party claim amount for different categories of vehicles based on the chosen characteristics of the data. In actuarial research, predicting the insurance claim amount for different vehicle categories is a challenging task, and minimal empirical research studies were done to forecast the claims. In the present study, the annual time series historical data were collected for a period of 34 years. We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively. In this context, we exhibited the feasibility of using a statistical machine learning approach such as Linear regression Model, the Exponential Smoothing Model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict the various categories of vehicles claim amount. The data were analyzed, compared, and the empirical analysis showed that Artificial Neural Network is a better predictive model among the other time series models based on performance evaluation metrics RMSE and MAPE with lesser variance. Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement and management for different categories of vehicles.
Highlights
Motor Insurance is one of the most exciting branches of the insurance sector
This article generates third-party claim forecasting models of the various types of motor vehicles by using three traditional time series models, artificial neural network (ANN), and the hybrid autoregressive integrated moving average (ARIMA)-ANN model based on secondary data
We have applied a time series modeling technique such as Linear model, Exponential Smoothing, ARIMA, ANN, and hybrid ARIMA-ANN model to see which of these models are better for forecasting the claim amount and calculated the performance measure: Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) for all models
Summary
Motor Insurance is one of the most exciting branches of the insurance sector. In the year 1895, the third-party liability insurance policy was introduced in the Insurance field. A study [1] stock exchange by applying machine learning techniques reveals that the ANN model is more potent in forecasting such as stacking and blending gives better prediction than power distribution data than the time series models. The ANN accidental claims using telematics data, the logistic predictive efficiency is better than GARCH models in regression showed better prediction than the XGBoost forecasting the stock exchange rate [3] Another stock machine learning algorithm [26]. We have studied the third-party claim better accuracy as compared with other advanced models amount variable for different categories of motor vehicles [12]. Another study suggested that the For the historical TP claim data set for various vehicles, hybrid ARIMA-ANN model has the best model for we have applied a simple linear model, ARIMA, forecasting Indian Robusta coffee projection [19]. Where Xi is a time variable in a yearly unit; Yi is a TP claims amount; e i is a residual error term
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