New Machine Learning Study Findings Have Been Reported by Investigators at Stevens Institute of Technology (A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement)

2020 MAY 19 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- New research on Machine Learning is the subject of a report. According to news reporting from Hoboken, New Jersey, by NewsRx journalists, research stated, “The private insurance sector is recognized as one of the fastest-growing industries. This rapid growth has fueled incredible transformations over the past decade.”

The news correspondents obtained a quote from the research from the Stevens Institute of Technology, “Nowadays, there exist insurance products for most high-value assets such as vehicles, jewellery, health/life, and homes. Insurance companies are at the forefront in adopting cutting-edge operations, processes, and mathematical models to maximize profit whilst servicing their customers claims. Traditional methods that are exclusively based on human-in-the-loop models are very time-consuming and inaccurate. In this paper, we develop a secure and automated insurance system framework that reduces human interaction, secures the insurance activities, alerts and informs about risky customers, detects fraudulent claims, and reduces monetary loss for the insurance sector. After presenting the blockchain-based framework to enable secure transactions and data sharing among different interacting agents within the insurance network, we propose to employ the extreme gradient boosting (XGBoost) machine learning algorithm for the aforementioned insurance services and compare its performances with those of other state-of-the-art algorithms. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7 & x0025; higher accuracy compared to decision tree models when detecting fraudulent claims. The obtained results reveal that, when applied to an auto insurance dataset, the XGboost achieves high performance gains compared to other existing learning algorithms. For instance, it reaches 7 & x0025; higher accuracy compared to decision tree models when detecting fraudulent claims. Furthermore, we propose an online learning solution to automatically deal with real-time updates of the insurance network and we show that it outperforms another online state-of-the-art algorithm.”

According to the news reporters, the research concluded: “Finally, we combine the developed machine learning modules with the hyperledger fabric composer to implement and emulate the artificial intelligence and blockchain-based framework.”

For more information on this research see: A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement. IEEE Access, 2020;8():58546-58558. IEEE Access can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA.

Our news journalists report that additional information may be obtained by contacting H. Ghazzai, Stevens Institute of Technology, Sch Syst & Enterprises, Hoboken, NJ 07030, United States. Additional authors for this research include N. Dhieb, Y. Massoud and H. Besbes.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/access.2020.2983300. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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