Journal ArticleDOI
A survey of the issues in consumer credit modelling research
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The development of the methodology is surveyed, the current environment for consumer lending is described, and some of the modelling areas and issues that are actively being researched or should be are identified.Abstract:
Methods for assessing the credit risk when lending to consumers has been in operation for 50 years. Yet, there are probably now more opportunities and challenges for research into the development of this area than ever before. This paper surveys the development of the methodology, describes the current environment for consumer lending and seeks to identify some of the modelling areas and issues that are actively being researched or should be.read more
Citations
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Journal ArticleDOI
Benchmarking state-of-the-art classification algorithms for credit scoring
TL;DR: It is found that both the LS-SVM and neural network classifiers yield a very good performance, but also simple classifiers such as logistic regression and linear discriminant analysis perform very well for credit scoring.
Journal ArticleDOI
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research
TL;DR: The study of Baesens et al. (2003) is updated and several novel classification algorithms to the state-of-the-art in credit scoring are compared, providing an independent assessment of recent scoring methods and offering a new baseline to which future approaches can be compared.
Journal ArticleDOI
Credit risk assessment with a multistage neural network ensemble learning approach
TL;DR: A multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level and the reliability values of the selected neural network models are scaled into a unit interval by logistic transformation.
Journal ArticleDOI
An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: The case of credit scoring
TL;DR: A novel intelligent-agent-based fuzzy group decision making (GDM) model is proposed as an effective multicriteria decision analysis (MCDA) tool for credit risk evaluation.
Journal ArticleDOI
Instance sampling in credit scoring: An empirical study of sample size and balancing
Sven F. Crone,Steven Finlay +1 more
TL;DR: An empirical study of instance sampling in predicting consumer repayment behaviour is described, evaluating the relative accuracies of logistic regression, discriminant analysis, decision trees and neural networks on two datasets across 20 samples of increasing size and 29 rebalanced sample distributions created by gradually under- and over-sampling the goods and bads respectively.
References
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Book
Construction and Assessment of Classification Rules
TL;DR: Basic Ideas Introduction Constructing Rules Fisher's LDA and Other Methods Based on Covariance Matrices Recursive Partitioning Methods Nonparametric Smoothing Methods Practical Issues Some Special Problems Some Illustrative Applications.
Book
Credit Scoring and Its Applications
TL;DR: Lyn C. Thomas as discussed by the authors is a Professor of Management Science at the University of Southampton, UK and David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.
Credit Scoring and its Applications
TL;DR: From the Publisher: About the Author Lyn C. Thomas is a Professor of Management Science at the University of Southampton and David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.
Journal ArticleDOI
Construction and Assessment of Classification Rules
TL;DR: The authors may not be able to make you love reading, but construction and assessment of classification rules will lead you to love reading starting from now.
Journal ArticleDOI
Survival Analysis Methods for Personal Loan Data
Maria Stepanova,Lyn C. Thomas +1 more
TL;DR: Three extensions of Cox's proportional hazards model applied to personal loan data are looked at and a new way of coarse-classifying of characteristics using survival-analysis methods is proposed, including time-by-characteristic interactions.