Abstract :

Health Insurance Fraud Has Become A Significant Challenge For Insurance Providers, Leading To Substantial Financial Losses And Increased Healthcare Costs. Fraudulent Activities Such As False Claims, Duplicate Billing, Exaggerated Treatments, And Unnecessary Medical Procedures Not Only Impact Insurance Companies But Also Affect Policyholders Through Increased Premiums. Therefore, Identifying Fraudulent Health Insurance Claims Has Become An Important Research Area In The Field Of Data Analytics And Machine Learning. This Study Focuses On Developing An Intelligent System For Identifying Health Insurance Claim Frauds Using Advanced Data Mining And Machine Learning Techniques. The Proposed Approach Analyzes Historical Claim Data And Extracts Relevant Features Such As Claim Amount, Treatment Type, Patient History, Hospital Details, And Claim Frequency To Detect Suspicious Patterns. Machine Learning Algorithms Such As Random Forest, Decision Trees, Support Vector Machines, And Logistic Regression Are Utilized To Classify Claims As Legitimate Or Fraudulent. The System Applies Data Preprocessing, Feature Selection, And Model Training To Improve Detection Accuracy And Reduce False Positives. By Identifying Unusual Claim Patterns And Anomalies, The Proposed Framework Helps Insurance Companies Detect Potential Fraud Cases At An Early Stage. Experimental Results Demonstrate That Machine Learning–based Fraud Detection Models Can Significantly Enhance The Efficiency And Reliability Of Claim Verification Processes. Overall, The Proposed System Provides An Effective And Scalable Solution For Identifying Fraudulent Health Insurance Claims, Thereby Reducing Financial Losses, Improving Operational Efficiency, And Ensuring Fairness In The Health Insurance Ecosystem

Published:

09-10-2022

Issue:

Vol. 22 No. 10 (2022)

Page Nos:

51 - 58

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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