Enhancing Credit Risk Management Through Integration of Multiple Imputation Methodology and Long-Term Survival Modelling

Abstract

Credit risk management plays a crucial role in financial institutions by identifying, assessing and controlling the credit risks arising from lending activities. However, missing data pose a common problem in credit risk modelling, leading to biased estimates and a loss of statistical power. To address this issue and improve predictive accuracy, multiple imputation methods are increasingly employed. This study evaluates the performance of the Multivariate Imputation by Chained Equations (MICE) method in identifying factors associated with time to default, using the publicly available Prosper personal loan data. The analysis is conducted within the framework of mixture cure rate models based on the generalised gamma family of distributions. This research is the first of its kind to integrate the MICE approach into mixture cure rate modelling. The flexibility of the generalised gamma distribution was utilised to select the optimal mixture cure rate model. The estimated cure rate using complete cases (CC) was higher than that obtained using MICE imputation. This highlights the potential pitfalls of solely relying on CC analysis in survival analysis.