A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement

Najmeddine Dhieb, Hakim Ghazzai, Hichem Besbes, Yehia Massoud

Research output: Contribution to journalArticlepeer-review

59 Scopus citations

Abstract

The private insurance sector is recognized as one of the fastest-growing industries. This rapid growth has fueled incredible transformations over the past decade. 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% 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% 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. Finally, we combine the developed machine learning modules with the hyperledger fabric composer to implement and emulate the artificial intelligence and blockchain-based framework.
Original languageEnglish (US)
Pages (from-to)58546-58558
Number of pages13
JournalIEEE Access
Volume8
DOIs
StatePublished - Jan 1 2020
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-13

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • General Materials Science

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