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.
Background/Objectives: The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting overall claim amount based on the chosen characteristics of the data. Methods/Statistical analysis: In the field of actuarial research, forecasting the third-party claim amount for Motor vehicles is a challenging task, and only limited empirical research studies are done in predicting the claim. In this context, the annual time series historical claim data were collected for a period of 34 years to examine the predictive performance of the linear regression model, exponential smoothing model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict third party claim amount of motor insurance data in India. Findings: The data are analyzed, and the empirical evidence from the study shows that the ANN model improved the accuracy prediction when compared to Linear Regression, Exponential smoothing model, ARIMA and a hybrid model with respect to the performance criteria such as root mean squared error (RMSE) and mean absolute percentage error (MAPE). Therefore, the ANN model is more potent in forecasting TP claim amounts by considering the adequacy, suitability, and accuracy of the data modeling. Novelty/Applications: This data analytics approach would help motor insurance companies in India to have an idea about the expected future claim amounts. Also, this modeling approach will help the Motor Insurance companies of India to provide a better customer-centric forecasting model, which ensures better claims settlement and management.
In the area of insurance, probability modeling has a wide variety of applications. In life insurance, the compensation sum is calculated in advance and may often be estimated using actuarial techniques, while in motor insurance, the claim amount is generally not known in advance. In the insurance business, the improvement of actuarial risk control strategies is an essential technique for controlling insurance risk. Although an insurance company’s risk assessment about its solvency is a complex and detailed problem, its solution begins with statistical modeling of individual claims’ amounts. This article emphasizes the possible ways of obtaining a suitable probability distribution model that accurately explains insurance risks and how to use such a model for risk management purposes. For this reason, we have applied modern programming techniques and statistical software implemented the methods provided based on data on premium amounts of third-party motor insurance claims.
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