Analysis Accuracy of Random Forest Model for Big Data – A Case Study of Claim Severity Prediction in Car Insurance

Insurance claim is one of the important elements in the field of insurance services. Claim severity refers to the amount of fund that must be spent to repair the damage. The amount of insurance claim is influenced by many factors. This causes the volume of data to be very large. Therefore, a suitable method is required. Random Forest, one of the machine learning methods can be implemented to handle this problem. This thesis applies the Random Forest model to predict the amount of this claim severity on car insurance. Furthermore, analysis of the effect of the number of features used on model accuracy is conducted. The simulation result shows that the Random Forest model can be applied in cases of prediction of claim severity, which is a case of regression in the context of machine learning. Only by using 1/3 of the overall features, the accuracy of the Random Forest model can produce accuracy that is comparable to that obtained when using all features which is around 99%. This result confirms the scalability of Random Forest, especially in terms of the number of features. Hence, the Random Forest model can be used as a solution to Big Data problems related to data volume.