Enhanced Insurance Claim Prediction via Decoupled Graph Neural Networks with Pseudo Labeling

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.