Modelo de regresión con cura de tiempo de promoción con distribución potencia exponencial por partes para datos de calificación crediticia

In the financial context, survival analysis has been used in a variety of ways, like in the study (Borelli & Lucena, 2022), which aims to estimate the time until the recovery of overdue credit portfolios, with a focus on pricing non-performing credit portfolios. As in the study by Ramirez (2016), which aimed to estimate the time until default, evaluating the conditioning variables for credit delay. One of the objectives of this paper is to analyze a real dataset about credit, to model the time until the customer of a financial company, located in Rio Grande do Sul - Brazil, becomes defaulter. Therefore, in this paper it is proposed a model for survival data with cure rate, in which the distribution of the times is adjusted by the power piecewise exponential (PPE) distribution. The other objective is to propose a model that has not yet been explored jointly in the literature, through the construction of a long-term survival model, considering the approach of the promotion time models (Yakovlev & Tsodikov, 1996) and the power piecewise exponential distribution (Gómez et al., 2017). To evaluate the performance of the proposed model, a simulation study was carried out comparing the proposed model with models available in the literature (Weibull and piecewise exponential), using the R software. Finally, an application of the proposed model was conducted on a real data set related to personal loans, to evaluate the applicability of the model in a credit context.