LASCA: A Large-Scale Stable Customer Segmentation Approach to Credit Risk Assessment

Customer segmentation plays a crucial role in credit risk assessment by dividing users into specific risk levels based on their credit scores. Previous methods fail to comprehensively consider the stability in the segmentation process, resulting in frequent changes and inconsistencies in users' risk levels over time. This increases potential risks to a company. To this end, this paper at first introduces and formalizes the concept of stability regret in the segmentation process. However, evaluating stability is challenging due to its black-box nature and the computational burden posed by vast user data sets. To address these challenges, this paper proposes a large-scale stable customer segmentation approach named LASCA. LASCA consists of two phases: high-quality dataset construction (HDC) and reliable data-driven optimization (RDO). Specifically, HDC utilizes an evolutionary algorithm to collect high-quality binning solutions. RDO subsequently builds a reliable surrogate model to search for the most stable binning solution based on the collected dataset. Extensive experiments conducted on real-world large-scale datasets (up to 0.8 billion) show that LASCA surpasses the state-of-the-art binning methods in finding the most stable binning solution. Notably, HDC greatly enhances data quality by 50%. RDO efficiently discovers more stable binning solutions with a 36% improvement in stability, accelerating the optimization process by 25 times via data-driven evaluation. Currently, LASCA has been successfully deployed in the large-scale credit risk assessment system of Alipay.