Survival analysis in credit risk management: a review study

Lunhui Shang, Jiamei Zhao, Gang Li and Xinrui Zhang

Abstract

The use of survival analysis, which can predict default timing and effectively handle censored data, in credit risk management has generated an extensive literature and valuable innovations over the past three decades. However, a systematic literature review of these published works is somewhat lacking. Our study aims to fill this gap. First, we introduce basic survival analysis methods and their applications in credit risk management models. Second, our review categorizes existing research along two main directions. The first involves improving the implicit and sometimes unreasonable assumptions in survival analysis for practical credit risk management; some researchers have enhanced the model’s applicability by relaxing or modifying these assumptions. The second direction is the expansion of traditional survival analysis models and risk management objectives to adapt to the evolving credit market environment, including the emergence of new risk data and heightened requirements for credit risk management. Finally, this work explores future directions, aiming to provide research insights for both researchers and practitioners and to foster further application of survival analysis in credit risk management.