Machine Learning (ML) based Anomaly Detection in Insurance Industries

Abstract

Handling claims presents significant difficulties for the insurance sector particularly in cases of duplicate claims, missing information, and false claims. Conventional manual techniques are prone to mistakes and inefficiencies, which substantially raises running expenses. This work presents an automated machine learning (ML) based solution for these problems. DBSCAN Clustering, Isolation Forest Classifier, and Random Forest Classifier are three specific ML techniques applied here. Early intervention is possible with the Random Forest Classifier as it can detect claims with lacking proof. While DBSCAN Clustering combines like data points to assist uncover and control duplicate claims, the Isolation Forest Classifier detects fraudulent claims by identifying abnormalities in the data. Using a big dataset, the suggested fix demonstrated significant performance and accuracy benefits in claim processing. Results demonstrate the ML models lower operational costs, less hand-made intervention, and better fraud detection. Reducing delays and mistakes in claim processing benefits the automated method in increasing client satisfaction as well. By automating major portions of claim processing, this paper shows the possibilities of ML in changing the insurance industry and generating cost savings, higher efficiency, and fraud protection. ML technology will become increasingly important in increasing the accuracy and efficiency of claim processing as the sector maintains its digital transformation under progress.