This article describes the R package DEoptim, which implements the differential evolution
algorithm for global optimization of a real-valued function of a real-valued parameter vector …

We investigate the performance of various survival analysis techniques applied to ten actual
credit data sets from Belgian and UK financial institutions. In the comparison we consider …

In this paper, a conditional survival function estimator for censored data is studied. It is
based on a double smoothing technique: both the covariate and the variable of interest …

In risk analysis, a global fit that appropriately captures the body and the tail of the distribution
of losses is essential. Modelling the whole range of the losses using a standard distribution …

This study introduces a machine learning competing risks survival analysis model aiming at
exploring the Probability of Default component of credit risk. Due to modeling of a cumulative …

Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides
an advanced solution to the credit-scoring problem by quantifying the probability of survival …

An estimator of the probability of default (PD) in credit risk is proposed. It is derived from a
nonparametric conditional survival function estimator based on cure models. Asymptotic …

Credit scoring has become very important issue due to the recent growth of the credit
industry, so the credit department of the bank faces a large amount of credit data. Clearly it is …

The article examines novel machine learning techniques for survival analysis in a credit risk
modelling context. Using a large dataset of US mortgages, we evaluate the adequacy of …

Purpose This study aims to develop a novel approach for predicting default risk in
bancassurance, which plays a crucial role in the relationship between interest rates in banks …