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：暂无翻译