DEPICTING RISK PROFILE OVER TIME: A NOVEL MULTIPERIOD LOAN DEFAULT PREDICTION APPROACH

• 成果类型: Article
• 署名作者: Wang, Zhao; Jiang, Cuiqing; Zhao, Huimin
• 署名单位: Hefei University of Technology; Hefei University of Technology; University of Wisconsin System; University of Wisconsin Milwaukee
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2022/17491
• 发表日期: 2023
• 页码: 1455-1485
• 关键词: credit; performance; BEHAVIOR; MODEL; FRAUD; BIAS

摘要

With the rapid development of fintech, the need for dynamic credit risk evaluation is becoming increasingly important. While previous studies on credit scoring have mostly focused on single-period loan default prediction, we call for a new avenue-multiperiod default prediction (MPDP)-to depict risk profiles over time. To address the challenges raised by MPDP, such as monotonic default probability prediction and complex relationship accommodation, we propose a novel approach, hybrid and collective scoring (HACS). We design a hybrid modeling strategy to predict whether and when a borrower will default separately through a default discrimination model and a default time estimation model, respectively, and synthesize them through a probabilistic framework. To accommodate various possible patterns of default time and measure the distribution of default probability over successive time intervals, we propose a joint default modeling method to train the default time estimation model. Empirical evaluations at the model (time-to-default prediction performance and discrimination performance) and mechanism (identifiability and discriminability) levels, as well as impact analyses at the application (granting performance and profitability performance) level, show that HACS outperforms the benchmarked survival analysis and multilabel learning methods on all fronts. It can more accurately predict time-to-default and provide financial institutions and investors better decision-support in granting loans and selecting loan portfolios.