Predictive Analytics for Fraud Detection in Reinsurance Claims: Enhancing Early Detection and Decision-Making Through Data Intelligence
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Predictive Analytics for Fraud Detection in Reinsurance Claims: Enhancing Early Detection and Decision-Making Through Data Intelligence
Authors:
Sashi Kiran Vuppala
McKinney, USA
sashivuppala93@gmail.com
ORCID : 0009-0008-0404-041X
Abstract— Fraudulent claims pose a significant threat to the financial stability of the reinsurance industry, necessitating more proactive and intelligent detection mechanisms. This paper explores the application of predictive analytics to identify and mitigate fraudulent activities in reinsurance claims. By leveraging machine learning models, historical claims data, and anomaly detection techniques, predictive analytics can uncover subtle patterns and indicators of potential fraud that traditional methods often miss. The study demonstrates how predictive models enable early identification of high-risk claims, allowing for timely intervention and improved decision-making. The implementation of predictive analytics significantly enhances the accuracy, efficiency, and consistency of fraud detection processes. Results highlight a reduction in false positives, faster claims assessment, and minimized financial losses. This research provides a comprehensive framework for integrating predictive analytics into reinsurance fraud detection, offering a data-driven approach to safeguarding assets and maintaining operational integrity.
Keywords— Predictive Analytics, Fraud Detection, Reinsurance Claims, Machine Learning, Anomaly Detection, Risk Mitigation, Data-Driven Decision-Making, Financial Stability.