As the demand for insurance continues to skyrocket in our daily lives, accurately predicting claim amount has become a critical demand for insurance companies. This capacity enables to identify high-risk individuals for minimizing substantial claims and, conversely, help to lower premiums for other policyholders. Recently, graph neural networks (GNNs) have achieved remarkable performance on classification and recommendation for insurance, but have not been extended to this regression task. Will GNNs serve as a powerful tool for claim amount prediction? Surprisingly, our research reveals that GNNs perform well in this area, but encounter three challenges, i.e., the mixture of heterophilic and homophilous network pattern, label scarcity and label uncertainty. To address these challenges, we propose Decoupled Graph Neural Networks Enhanced by Pseudo Labels for Claim Prediction (GClaim) to empower existing GNNs for claim amount prediction. Specifically, GClaim automatically organizing nodes into distinct clusters, facilitating the independent learning of nodes within each latent network pattern. It further introduces trustworthy pseudo labels from unlabeled and uncertain nodes through a standard deviation-induced filtering strategy. Extensive experiments on three industrial datasets and five newly developed proxy public datasets with varying evaluation protocols demonstrate the effectiveness of GClaim, as well as each well-designed component in GClaim. In light of its outstanding performance, GClaim has been successfully deployed in the online auto-insurance platform of Alipay. With GClaim, the auto-insurance service has experienced an over 10% increase in the end-to-end conversion rate and over 25% rise in UV value.

The rapid growth of health insurance and the rising incidence of fraudulent claims underscore the necessity for an efficient and professional claims management system. However, there is a noticeable lack of shared relevant experience from previous research in this field. In response to this challenge, we introduce AntAkso, a robust claims management system specifically designed for health insurance operations within Alipay. AntAkso incorporates a digital and professional management system, achieving a notable decrease in the volume of false claims, reduction in administrative costs, and heightened satisfaction among its policyholders. We begin by highlighting the core components of this system, including the case stratification, hospital recommendation, and case dispatch modules, along with the pivotal algorithms employed, i.e., the fraud detection, recommendation, and robust satisficing algorithms. We also detail the system's implementation and deployment. We substantiate the proposed system's effectiveness and efficiency with empirical evidence from experiments on a large set of real-world health insurance claims data.

The fraudulent insurance claim is critical for the insurance industry.Insurance companies or agency platforms aim to confidently estimate the fraud risk of claims by gathering data from various sources.Although more data sources can improve the estimation accuracy, they inevitably lead to increased costs.Therefore, a great challenge of fraud risk verification lies in well balancing these two aspects.To this end, this paper proposes a framework named cost-efficient fraud risk optimization with submodularity (CEROS) to optimize the process of fraud risk verification.CEROS efficiently allocates investigation resources across multiple information sources, balancing the trade-off between accuracy and cost.CEROS consists of two parts that we propose: a submodular set-wise classification model * Equal Contribution.