HOW CAN AI HELP IN FRAUDULENT CLAIM IDENTIFICATION

Abstract

Artificial intelligence (AI) development has shown promise in tackling this issue by enhancing fraud detection procedures. This academic essay offers a thorough analysis of the body of knowledge regarding the application of AI to the detection of fraudulent claims. The article covers several often-used AI fraud detection methods, including machine learning algorithms, natural language processing, network analysis, and anomaly detection. The effectiveness, scalability, interpretability, and ethical implications of AI-based systems for fraudulent claim identification are reviewed. The need for more interpretable and explicable AI models, addressing ethical issues, exploring novel data sources and feature engineering techniques, and assessing the scalability and effectiveness of AI-based approaches in various industries and regions are also discussed, along with gaps in the existing literature that need to be filled. There is also a discussion of the research's future effects on fraud detection and its usefulness. This study article concludes by highlighting the importance of AI in identifying fraudulent claims, pointing out gaps in the literature, and offering suggestions for future research and the responsible application of AI to enhance fraud detection procedures.

Keywords: artificial intelligence, anomaly detection, network analysis, natural language processing, fraud detection, fraudulent claims, and interpretability.