Abstract
This research focuses on the analysis of credit loan data with long-term non-defaults, which is a vital issue in credit risk management. The study introduces cure fraction in default risk modelling, which offers a broad spectrum of progressive choices for advanced models resulting in reduced loan default risk and enhanced solvency. The work presents four mixture cure fraction models using the generalised trigonometric Fréchet distributions with and without covariates. These are sine-Fréchet, cosine-Fréchet, tangent-Fréchet, and secant-Fréchet mixture cure fraction models. The study shows that the developed mixture cure fraction models can be used as alternatives to current modelling techniques for survival data analysis in the area of credit risk management. Adopting these trigonometric Fréchet mixture cure fraction models can significantly enhance credit risk assessment processes, leading to better-informed decisions and improved financial outcomes. The best among the cure fraction models are the tangent-Fréchet and secant-Fréchet mixture cure fraction models in modelling cure events with and without covariates, respectively.
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