Automated Health Insurance Processing Framework with Intelligent Fraud Detection, Risk Classification and Premium Prediction

Abstract

Private insurance represents one of the sectors poised for significant growth. There are insurance solutions available for most high-value assets such as homes, jewelry, vehicles, and other valuable items. To optimize profitability while managing client claims, insurance companies have embraced advanced operations, procedures, and mathematical models to assess risks and prioritize customer satisfaction, all while maximizing profits. This article introduces a machine learning-driven automated framework designed to reduce human intervention, safeguard insurance operations, identify high-risk clients, detect fraudulent claims, and mitigate financial losses within the insurance sector. Initially, the framework focuses on fraud detection to determine the legitimacy of claims. Genuine claims leverage the patient’s medical history to calculate associated risk factors and premiums. Various machine learning-based classification models and ensemble techniques were employed and evaluated for each of the three insurance processing tasks. Performance assessments using relevant metrics are presented and thoroughly discussed. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.