The Need and Importance of Augmented AI/ML systems for Health Claims Fraud Detection

Artificial Intelligence (AI) and Machine learning (ML) systems are increasingly being used to solve basic day-to-day problems. Chatbots such as Alexa and Siri play a big role in our day-to-day life with our reliance on these systems increasing by the day. The use of AI and ML systems is being increasingly explored, in fraud detection. particularly for the detection of fraud health insurance claims submitted by providers. Recently, several studies have leveraged AI/ML techniques to develop health claims fraud detection models with close to 100% accuracy rates. This paper looks at the limitations of the deep learning-based AI and ML models in the case of health claims fraud detection, and the associated challenges and implications. We then define an integrated AI/ML strategy augmented by human intelligence for comprehensive fraud control to address the challenges and fraud detection using AI/ML responsibly without impacting the patient experience and health outcomes.

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Publication Details

Journal
Mapana Journal of Sciences
Published
2024-11-06
DOI
https://doi.org/10.12723/mjs.sp3.5
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
0.00
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article

The Need and Importance of Augmented AI/ML systems for Health Claims Fraud Detection

Shiv Dutt Gupta, Parul Naib, Sudha Chandrashekar
Mapana Journal of Sciences
Artificial Intelligence in Law
article

The Need and Importance of Augmented AI/ML systems for Health Claims Fraud Detection

Shiv Dutt Gupta, Parul Naib, Sudha Chandrashekar
article en

Abstract

Artificial Intelligence (AI) and Machine learning (ML) systems are increasingly being used to solve basic day-to-day problems. Chatbots such as Alexa and Siri play a big role in our day-to-day life with our reliance on these systems increasing by the day. The use of AI and ML systems is being increasingly explored, in fraud detection. particularly for the detection of fraud health insurance claims submitted by providers. Recently, several studies have leveraged AI/ML techniques to develop health claims fraud detection models with close to 100% accuracy rates. This paper looks at the limitations of the deep learning-based AI and ML models in the case of health claims fraud detection, and the associated challenges and implications. We then define an integrated AI/ML strategy augmented by human intelligence for comprehensive fraud control to address the challenges and fraud detection using AI/ML responsibly without impacting the patient experience and health outcomes.

Mapana Journal of SciencesVol. 23
IIHMR University (IN)
Openalex Percentile: Top 51%
Artificial Intelligence in Law
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