AI-Powered Fraud Detection Systems in Insurance

Abstract

The advent of insurance fraud can be traced back to the inception of the insurance system. The act of insurance fraud seeks to unlawfully manipulate the financial remuneration provided by an insurance company for making a business loss. Insurance fraud commonly comes in two forms: soft and hard, the latter of which refers to direct illegal behaviors, such as falsifying insurance claims and staging fake incidents to defraud insurance companies. Hard fraud accounts for a significant portion of policyholder fraud cases, although it culminates in the highest insurance claim losses.

Although the insurtech sector has taken off in recent years, insurance companies have yet to deploy a robust or substantially effective strategy for fraud detection, primarily due to the underrepresentation of highly acknowledged AI technologies in the insurance sector. The majority of current anti-fraud strategies employed are focused on traditional rule-based expert systems currently in operation. With the establishment of the IoT ecosystem that runs on digital devices, big data storage, cloud computing, and many different kinds of sensors, the information captured contains significant discriminative data that can contribute to attacking fraud in the insurance industry. Therefore, a new generation of AI-powered fraud detection systems is essential in order to address the demands of insurance stakeholders and confirm and actualize hidden potential empirical intelligence business value, notably in predicting policyholder propensity rates, claim values, and the potential to refund non-liable claims in auto insurance. In the pursuit of fulfilling these tasks, the resulting fraud detections should possess the adaptive ability to self-evolve through unsupervised learning processes as new data is submitted.