Integrating Deep Learning and Machine Learning Algorithms in Insurance Claims Processing: A Study on Enhancing Accuracy, Speed, and Fraud Detection for Policyholders

Abstract

This paper focuses on the integration of deep learning and machine learning algorithms in insurance claims processing. The aim is to augment the processing speed and accuracy of reviewing claims, as well as to improve the fraud detection on insurers' (policyholders') side. Utilization of advanced computational techniques as both risk assessment and bad experience are shown having positive influence on the claims events. For the insurance company the speed of analyzing and processing the insurance claims is crucially necessary in assuring the company's reputation, as loyal policyholders may have thought the company abandoned them. From the policyholders aspect, accurate fraud prevention is also very necessary as it happens to help onlookers or channelers to put their interest on the loss, adding those who work in unjustified professions for a living. Exploring both deep learning and machine learning algorithms to analyze and extract information from text and imagery files on the insurance claims, the company as insurer is expected to ease and fasten the verification process. While in the policyholders' side capabilities are enhanced in gauging the validity perpetrators' statement fraud mechanism used. Strengthening the perception of fraud and bad behavior conspire to damage, harm, and defraud companies, employers, policyholders, or third parties. A descriptive fraud, correlating with the damaged fraud specially abuse the misstated damage conditions. Companies are triggered to act as limited as possible with accidents and occurrences of loss on claims events. Insatisfaction, complaint, or anger felt by one party against another party (insuree) is tricked to fulfill their desire to their advantage, by putting their interest in onlookers or channelers to the loss, especially those who work/stay in service or profession which are not justified to be there for their survival. Reliability of events (evidence) can evolve from bad experience or violation of contract parameters, such as redundancy and exaggerated requirements for claim detail (evidence). A risk and bad experience may not directly affect the operational, but can be financially impactful. On the other hand, with the same equally/risk-inclined person, fun experience is expected to be shared, loyal behavior, which will be more profitable.