In recent years, online consumer credit services have emerged in e-commerce. Although such services boost sales, the best way to allocate credit to consumers is a critical issue to be explored. In this paper, a comprehensive scheme is proposed using data augmentation and model enhancement to infer online consumer credit risk. The proposed scheme augments consumer profiles by incorporating phone usage information to alleviate the “thin file” challenge and enhance the predictive model by taking a multi-staged view of consumers' repayment timing to achieve a more finely grained credit risk determination. A three-step analysis, including prediction evaluation, model interpretation using Shapley Additive Explanations (SHAP), and welfare analysis, was performed to evaluate our proposed scheme's efficacy. We found that phone usage information enhanced predictive performance and that underlying psychological mechanisms can be analyzed by corresponding feature interpretations to theories. The follow-up welfare analysis illustrates the business value of the proposed scheme.

近年来，电子商务中出现了在线消费信贷服务。尽管此类服务促进了销售，但将信贷分配给消费者的最佳方式是一个需要探索的关键问题。在本文中，提出了一种使用数据增强和模型增强来推断在线消费者信用风险的综合方案。提议的方案通过结合电话使用信息来增强消费者档案，以减轻“瘦文件”挑战，并通过对消费者的还款时间采取多阶段视图来增强预测模型，以实现更细粒度的信用风险确定。进行了三步分析，包括预测评估、使用 Shapley Additive Explanations (SHAP) 的模型解释和福利分析，以评估我们提出的方案的有效性。我们发现电话使用信息增强了预测性能，并且可以通过对理论的相应特征解释来分析潜在的心理机制。后续福利分析说明了拟议计划的商业价值。