Citation :

Anand Laxman Mhatre, 2024. "Using AI to Combat Medicaid Fraud, Waste, and Abuse" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 4: 84-86.

Abstract :

Although traditional fraud detection and prevention methods have been integral in reducing fraud and losses in the Medicaid programs, these methods have weaknesses such as inability to process large quantities of data, the inability to provide real-time monitoring of claims processing, the lack of predictive capabilities, and limitations in leveraging unstructured data. Integration of AI in Medicaid fraud mitigation bridges these limitations. The technology is advanced in data processing, allowing scalability of fraud investigations, real-time monitoring of claims, and forecasting of potential frauds. This publication describes the limitations of traditional Medicaid fraud control methods and how AI can be leveraged to bridge these inefficiencies.

References :

[1] Kumaraswamy, N., Markey, M. K., Ekin, T., Barner, J. C., & Rascati, K. (2022). Healthcare fraud data mining methods: A look back and look ahead. Perspectives in health information management, 19(1).

[2] Bauder, R., & Khoshgoftaar, T. (2018, July). A survey of medicare data processing and integration for fraud detection. In 2018 IEEE international conference on information reuse and integration (IRI) (pp. 9-14). IEEE.

[3] KPMG (2024), Artificial Intelligence Prevents Fraud. Retrieved From: https://kpmg.com/dk/en/home/insights/2020/04/artificial-intelligence-prevents-fraud-.html

Keywords :

Medicaid, Fraud, AI, Technology, Medicaid Fraud Prevention Methods.