Revolutionizing Insurance Fraud Detection: A Data-Driven Approach for Enhanced Accuracy and Efficiency

Authors : Sangishetty Akanksha

Volume/Issue : Volume 8 - 2023, Issue 10 - October

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Fraudulent activities are increasingly prevalent across various sectors, imposing significant financial burdens on the insurance industry, estimated to cost billions annually. Insurance fraud, a deliberate and illicit act for financial gain, has emerged as a critical challenge faced by insurance companies worldwide. Often, the root cause of this issue can be traced back to shortcomings in the investigation of fraudulent claims. The repercussions of insurance fraud are extensive, leading to substantial financial losses and billions in avoidable expenses for the industry. This, in turn, necessitates the adoption of technology-driven solutions to combat fraudulent activities, offering policyholders a trustworthy and secure environment while substantially reducing fraudulent claims. The financial impact of these fraudulent activities, covered by increasing policy premiums, ultimately affects society at large. Conventional claim investigation procedures have faced criticism for their time-consuming and labor-intensive nature, often yielding unreliable outcomes. Consequently, this research leverages the Random Forest Classifier to develop a machine learning-based framework for fraud detection. Our study showcases the practical application of data analytics and machine learning techniques in automating the assessment of insurance claims, with a specific focus on the automatic identification of erroneous claims. Additionally, our system has the potential to generate heuristics for detecting fraud indicators. As a result, this approach positively contributes to the insurance industry by enhancing both the reputation of insurance firms and the satisfaction of customers.

Keywords : Insurance Fraud Detection, Support Vector Machine, Random Forest Classifier, Fraud Prevention, Customer Satisfaction.


Paper Submission Last Date
31 - December - 2023

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In 1-2 Days

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In 2-3 Days

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