Journal of Advances in Developmental Research

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Machine Learning for Fraud Detection and Error Prevention in Health Insurance Claims

Author(s) Adya Mishra
Country United States
Abstract Healthcare insurance processes millions of claims daily, which makes it a prime target for fraud and errors. Due to the mistakes, there has been a massive increase in health insurance costs in recent years, and it's because of the payment errors made by the insurance companies while processing the claims. We describe a technology that helps detect fraud and prevent errors using machine learning. Machine Learning techniques such as Supervised and unsupervised learning, natural language processing, and deep learning analyze vast datasets to identify patterns, anomalies, and inconsistencies in claims data. Payment Errors made by insurance companies while processing claims often result in reprocessing of the claims. The extra work to reprocess the claims is known as rework. Machine Learning improves accuracy, reduces costs, streamlines claims processing, and improves customer satisfaction. In the future, Machine Learning will have the potential for real-time decision-making and greater collaboration across the industry.
Keywords Machine Learning, Healthcare, Claims, Fraud Detection, Data Analysis
Field Engineering
Published In Volume 14, Issue 1, January-June 2023
Published On 2023-04-05
Cite This Machine Learning for Fraud Detection and Error Prevention in Health Insurance Claims - Adya Mishra - IJAIDR Volume 14, Issue 1, January-June 2023. DOI 10.5281/zenodo.14615792
DOI https://doi.org/10.5281/zenodo.14615792
Short DOI https://doi.org/g8x3sr
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