Cost-Efficient Fraud Risk Optimization with Submodularity in Insurance Claim

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 called SSCM to estimate the submodular objective function, and a primal-dual algorithm with segmentation point called PDA-SP to solve the objective function. Specifically, SSCM models the fraud probability associated with multiple information sources and ensures the properties of submodularity of fraud risk without making independence assumption. The submodularity in SSCM enables PDA-SP to significantly speed up dual optimization. Theoretically, we disclose that when PDA-SP optimizes this dual optimization problem, the process is monotonicity. Finally, the trade-off coefficients output by PDA-SP that balance accuracy and cost in fraud risk verification are applied to online insurance claim decision-making. We conduct experiments on offline trials and online A/B tests in two business areas at Alipay: healthcare insurance recommendation and claim verification. The extensive results indicate that, compared with other methods, CEROS achieves acceleration of 66.9% in convergence speed and meanwhile 18.8% in cost reduction. Currently, CEROS has been successfully deployed in Alipay.