2022 APR 08 (NewsRx) — By a News Reporter-Staff News Editor at Health Policy and Law Daily — Investigators discuss new findings in engineering. According to news reporting originating from Moscow, Russia, by NewsRx correspondents, research stated, “Roughly 10 percent of the insurance industry’s incurred losses are estimated to stem from fraudulent claims.”
Our news reporters obtained a quote from the research from Skolkovo Institute of Science and Technology: “One solution is to use tabular data to construct models that can distinguish between claims that are legitimate and those that are fraudulent. However, while canonical tabular data models enable robust fraud detection, complex sequential data have been out of the insurance industry’s scope. For health insurance, we propose deep learning architectures that process insurance data consisting of sequential records of patient visits and characteristics. Both the sequential and tabular components improve the quality of the model, generating new insights into the detection of health insurance fraud. Empirical results derived using relevant data from a health insurance company show that our approach outperforms state-of-the-art models and can substantially improve the claims management process. We obtain a ROC AUC metric of 0.873, while the best competitor based on state-of-the-art models achieves 0.815.”
According to the news editors, the research concluded: “Moreover, we demonstrate that our architectures are more robust to data corruption. As more and more semi-structured event sequence data become available to insurers, our methods will be valuable for many similar applications, particularly when variables have a large number of categories, such as those from the International Classification of Disease (ICD) codes or other classification codes.”
For more information on this research see: Sequence Embeddings Help Detect Insurance Fraud. IEEE Access, 2022,10():32060-32074. (IEEE Access – http://ieeexplore.ieee.org/servlet/opac?punumber=6287639). The publisher for IEEE Access is IEEE.
A free version of this journal article is available at https://doi.org/10.1109/ACCESS.2022.3149480.
Our news journalists report that more information may be obtained by contacting Ivan Fursov, Skolkovo Institute of Science and Technology, Moscow, Russia. Additional authors for this research include Elizaveta Kovtun, Rodrigo Rivera-Castro, Alexey Zaytsev, Rasul Khasyanov, Martin Spindler, Evgeny Burnaev.
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