A Hybrid Approach to Health Insurance Fraud Detection Using Machine Learning and Blockchain Smart Contracts - Kottapally, Shilpa (International Journal of Computer Sciences and Engineering, 2025)
Since fraudulent activities account for an projected 3- 10% of total healthcare expenses, detecting fraud in healthcare systems is vital. Developed and underdeveloped nations alike are not immune to healthcare fraud. The criminals` goal was to take advantage of the shortcomings of the healthcare system as it stands. Unfortunately, fraud often prevents those who should be able to benefit from universal health coverage, such as individuals with health insurance, from actually receiving it. The purpose of this research was to compile a comprehensive literature review on the topic of health insurance claim fraud detection using ML-techniques. Further, we suggest preventing and detecting healthcare fraud, particularly in claims processing, by utilizing blockchain technology and ML-strategies. In order to sort the initial claims dataset, a decision-tree classification technique is used. In order to identify and stop healthcare fraud, the extracted knowledge is put into an Ethereum blockchain smart contract. Furthermore, our objective is to examine the information gathered from the literature over the last twenty years in order to shed light on topics such as research prospects and obstacles. Several obstacles stand in the way of machine learning`s ability to detect healthcare claims fraud. Some of these issues include data inconsistency, privacy worries, a lack of standardized and integrated data, and an insufficient amount of labelled fraudulent cases to train algorithms on. Results from the comparing experiments reveal that the top tool obtains a sensitivity level of 99.09% and a classification accuracy of 98.96%. By using the suggested system, the blockchain smart contract can now detect fraud with a 98.96% accuracy rate.