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

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-theloop 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.

Blockchain in Insurance: Exploratory Analysis of Prospects and Threats

“…Futuristically, several existing breakthroughs in computing like Cloud Computing [18], Internet of Things (IoT) [59], Artificial Intelligence (AI), machine learning methods [60], and data analytics [61] will be joined and leveraged to automate and drastically transform the manner in which insurance processes are handled contemporarily. The following are the major types of insurance being provided by modern companies and how blockchain's contribution can be implemented.…”

Need-Based and Optimized Health Insurance Package Using Clustering Algorithm

“…The design uses a combination of heuristic and predictive techniques to perform optimization for moving targets. An automated framework for insurance companies is suggested using an extreme gradient boosting algorithm on blockchain-based transactional records [5]. The results showed that XGboost produces high performance as compared to other existing learning algorithms.…”

“…The shape and scale parameters of gamma distributions are a and b respectively. The formula for Mean µ and standard deviation σ in terms of gamma distribution parameters are represented in Equations ( 4) and (5).…”

Blockchain-Based Implementation of Building Information Modeling Information Using Hyperledger Composer

“…The participants that are allowed to read or write to the ledger are called peers. Thus, it is easier to control the transactions on the ledger and it has higher system throughput [21][22][23]. Hyperledger Fabric secures the interactions among a group of authorized participants to preserve privacy and confidentiality [22].…”