Detection of Fraudulent Vehicle Insurance Claims Using Machine Learning

Abstract

One of the biggest and most pervasive issues facing the insurance sector is the filing of false insurance claims by customers. Insurance firms incur significant financial losses due to pricey fraudulent claims. Concerns from stakeholders and observers have been raised about insurance fraud, which continues to be a major concern for insurers and customers who pay the expenses through insurance premiums. Understanding the institution processes and operationalization of ICT in fraud detection is the first step in implementing the appropriate corrective actions. However, the procedure is time and money consuming because personally reviewing all insurance claims filed with insurance companies has become challenging. Given the prevalent issue of fraud in vehicle insurance claims, the manual approach to identifying fraudulent claims has been problematic because it is time-consuming and inaccurate. One of the various ways that researchers have tested is machine learning algorithms, which have demonstrated promising performance and enhanced accuracy in detecting fraudulent vehicle insurance claims. This study evaluated a range of ML algorithms, including AdaBoost, XGBoost NB, SVM, LR, DT, ANN, and RF, to discern between real and fraudulent automobile claims. Additionally, a machine learning-powered web-based system to predict and categorize vehicle insurance claims as either genuine or fraudulent was developed. The system was based on the machine learning classifier with the highest levels of prediction performance and classification accuracy. The AdaBoost and XGBoost classifiers outperformed the other models with both imbalanced and balanced data because they had the highest classification accuracy of 84.5%. The LR classifier performed poorly since it had the lowest classification accuracy for both unbalanced and balanced data. The ANN classifier performed better with unbalanced data than it did with balanced data. The final finding was that all eight classifiers could only be used on smaller datasets