Time to Default in Credit Scoring Using Survival Analysis: A Benchmark Study

Dirick, Lore; Claeskens, Gerda; Baesens, Bart

doi:10.2139/ssrn.2663267

Cited by 14 publications

(22 citation statements)

References 28 publications

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“…We considered typical measures for model evaluation such as mean absolute error (MAE) and mean squared error (MSE) to predict the time of default. Hereby we followed Dirick et al (2017), see also Zhang and Thomas (2012). The dataset is divided in a test set consisting of 1/3rd of the observations and a training set consisting of the remaining 2/3rd of the observations.…”

Section: Decision On the Number Of Subgroupsmentioning

confidence: 99%

A hierarchical mixture cure model with unobserved heterogeneity for credit risk

Dirick

Claeskens

Vasnev

et al. 2022

Econometrics and Statistics

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The specific nature of credit loan data requires the use of mixture cure models within the class of survival analysis tools. The constructed models allow for competing risks such as early repayment and default, and for incorporating maturity, expressed as an unsusceptible part of the population. A novel further extension of such models incorporates unobserved heterogeneity within the risk groups. A hierarchical expectation-maximization algorithm is derived to fit the models and standard errors are obtained. Simulations and a data analysis illustrate the applicability and benefits of these models, and in particular an improved event time estimation.

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Section: Decision On the Number Of Subgroupsmentioning

confidence: 99%

A hierarchical mixture cure model with unobserved heterogeneity for credit risk

Dirick

Claeskens

Vasnev

et al. 2022

Econometrics and Statistics

Self Cite

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“…This means that some information about the individual's event time (default time) is known, but the exact event time (default time) is not known. The second definition of censoring in [11] will be used, which states that a customer who did not experience default by the moment of data gathering, corresponds to a censored case. In this definition mature cases and early settlement cases are considered censored since the only event of interest is default.…”

Section: Basic Conceptsmentioning

confidence: 99%

“…Some of these include Banasik et al [3], which extended the use of the AFT model and also included a non-parametric Cox Proportional Hazards (CPH) model and Bellotti & Crook [4], which allowed for time varying covariates in the CPH model. Tong et al [35], Dirick et al [10] and Dirick et al [11] introduced the mixture cure model as a more general alternative to the CPH model for modelling data in the credit risk environment. Zhang et al [37] took it a step further and developed a new mixture cure model under different competing risks in order to score online consumer loans.…”

Section: Introductionmentioning

confidence: 99%

An overview of survival analysis with an application in the credit risk environment

Smuts

Allison

2021

ORION

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Survival analysis has become a popular technique to more accurately model the probability of default in the credit risk environment with the ultimate goal of finding the optimal price for credit. In this paper we present an overview of some of the basic concepts of survival analysis. The focus is specifically on the Cox Proportional Hazards (CPH) model and the mixture cure model, which is a general alternative to the CPH model. A detailed algorithm that can be used to simulate survival times (default times) from a mixture cure model is provided. A parametric CPH and mixture cure model are fitted to a simulated data set and the benefits of fitting the latter model are illustrated.

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“…Survival analysis models have also been proposed to monitor credit risk modelling, such as [16], followed by [17,18,19] and concluded by [20]. These studies compared the methods on the development sample and on random cross-validation samples.…”

Section: Yearmentioning

confidence: 99%

Analysis of Individual Loan Defaults Using Logit under Supervised Machine Learning Approach

Obare

Njoroge

Muraya

2019

AJPAS

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Financial institutions have a large amount of data on their borrowers, which can be used to predict the probability of borrowers defaulting their loan or not. Some of the models that have been used to predict individual loan defaults include linear discriminant analysis models and extreme value theory models. These models are parametric in nature since they assume that the response being investigated takes a particular functional form. However, there is a possibility that the functional form used to estimate the response is very different from the actual functional form of the response. The purpose of this research was to analyze individual loan defaults in Kenya using the logistic regression model. The data used in this study was obtained from equity bank of Kenya for the period between 2006 to 2016. A random sample of 1000 loan applicants whose loans had been approved by equity bank of Kenya during this period was obtained. Data obtained was on the credit history, purpose of the loan, loan amount, nature of the saving account, employment status, sex of the applicant, age of the applicant, security used when acquiring the loan and the area of residence of the applicant (rural or urban). This study employed a quantitative research design, it deals with individual loans defaults as group characteristics of a borrower. The data was pre-processed by seeding using R- Software and then split into training dataset and test data set. The train data was used to train the logistic regression model by employing Supervised machine learning approach. The R-statistical software was used for the analysis of the data. The test data set was used to do cross-validation of the developed logistic model which later was used for analysis prediction of individual loan defaults. This study focused on the analysis of individual loan defaults in Kenya using the logistic regression model in Machine learning. The logistic regression model predicted 303 defaults from train data set, 122 non-defaults and misclassified loans were 56 and 69. The model had an accuracy of 0.7727 with the train data and 0.7333 with the test data. The logistic regression model showed a precision of 0.8440 and 0.8244 with the train and test data respectively. The performance of the model with both the train and test data was illustrated using a plot of train errors and test errors against sample size on the same axes. The plot showed that the performance of the model increases with an increase in sample size. The study recommended the use of logistic regression in conjunction with supervised machine learning approach in loan default prediction in financial institutions and also more research should be carried out on ensemble methods of loan defaults prediction in order to increase the prediction accuracy.

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Time to Default in Credit Scoring Using Survival Analysis: A Benchmark Study

Cited by 14 publications

References 28 publications

A hierarchical mixture cure model with unobserved heterogeneity for credit risk

A hierarchical mixture cure model with unobserved heterogeneity for credit risk

An overview of survival analysis with an application in the credit risk environment

Analysis of Individual Loan Defaults Using Logit under Supervised Machine Learning Approach

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