Motor Insurance Claim Status Prediction using Machine Learning Techniques

Abstract

The insurance claim is a basic problem in insurance companies. Insurance insurers always have a challenge to the growing of insurance claim loss. Because there is the occurrence of claim fraud and the volume of claim data increases in the insurance companies. As a result, it is difficult to classify the insured claim status during the claim review process. Therefore, the aims of the study was to build a machine learning model that classifies and make motor insurance claim status prediction in machine learning approach. To achieve this study Missing value ratio, Z- Score, encoding techniques and entropy were used as data set preparation techniques. The final preprocessed data sets split using K- Fold cross validation techniques into training and testing sets. Finally the prediction model was built using Random Forest (RF) and Multi Class –Support Vector Machine (SVM).The performance of the models, RF and Multi –Class SVM classifiers were evaluated using Accuracy, Precision, Recall, and F- measure. The prediction accuracy of the model is capable of predicting the motor insurance claim status with 98.36% and 98.17% by RF and SVM classifiers respectively. As a result, RF classifier is slightly better than Multi-Class Support vector machines. Developing and implementing hybrid model to benefit from the advantages of different algorithms having graphical user interface to apply the solution to real world problem of the insurance company is a pressing future work.