Credit risk assessment is a crucial aspect of financial decision-making, enabling institutions to predict the likelihood of default and make informed lending decisions. Two prominent methodologies in credit risk modeling are logistic regression and survival analysis. Logistic regression is widely used in scorecard development due to its simplicity, interpretability, and effectiveness in estimating the probability of binary outcomes, such as default versus non-default. In contrast, survival analysis -- particularly within the hazard rate framework -- provides insights into the timing of events, such as the time to default. By integrating logistic regression with survival analysis, traditional scorecard models can be enhanced to capture not only the probability of default but also the dynamics of default over time. This combined approach offers a more comprehensive view of credit risk, enabling institutions to manage risk proactively and tailor strategies to individual borrower profiles. This article presents the process of developing a monthly hazard rate model using logistic regression and augmented data with survival analysis techniques to incorporate time-varying risk factors. The process includes data preparation, model construction, and the evaluation of performance metrics. Monthly hazard rates are then converted into default probabilities. Finally, a behavioral scorecard is developed using offset adjustment.

翻译：信用风险评估是金融决策的关键环节，它使机构能够预测违约可能性并做出明智的借贷决策。逻辑回归与生存分析是信用风险建模中两种重要的方法。逻辑回归因其简单性、可解释性以及在估计二元结果（如违约与非违约）概率方面的有效性，被广泛应用于评分卡开发。相比之下，生存分析——尤其是在风险率框架内——能够提供事件发生时间的洞察，例如违约发生的时间。通过将逻辑回归与生存分析相结合，传统的评分卡模型可以得到增强，不仅能捕捉违约概率，还能捕捉违约随时间变化的动态特征。这种组合方法提供了更全面的信用风险视图，使机构能够主动管理风险，并根据个体借款人特征定制策略。本文阐述了利用逻辑回归与生存分析技术，结合增强数据开发月度风险率模型的过程。该过程包括数据准备、模型构建以及性能指标评估。随后将月度风险率转换为违约概率。最后，通过偏移调整开发出行为评分卡。