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.