Credit-scoring models in the credit-union environment using neural networks and genetic algorithms

Desai, Vijay S.; Conway, Daniel G.; Crook, Jonathan; Overstreet, George A.

doi:10.1093/imaman/8.4.323

Cited by 92 publications

(61 citation statements)

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“…Others are from the machine learning/data mining domain. Examples include genetic algorithms (Desai et al, 1997;Finlay, 2005) and support vector machines (Baesens et al, 2003;Huysmans et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Multiple classifier architectures and their application to credit risk assessment

Finlay

2011

European Journal of Operational Research

179

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Abstract.Multiple classifier systems combine several individual classifiers to deliver a final classification decision. An increasingly controversial question is whether such systems can outperform the single best classifier and if so, what form of multiple classifier system yields the greatest benefit.In this paper the performance of several multiple classifier systems are evaluated in terms of their ability to correctly classify consumers as good or bad credit risks. Empirical results suggest that many, but not all, multiple classifier systems deliver significantly better performance than the single best classifier. Overall, bagging and boosting outperform other multi-classifier systems, and a new boosting algorithm, Error Trimmed Boosting, outperforms bagging and AdaBoost by a significant margin.

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Section: Introductionmentioning

confidence: 99%

Multiple classifier architectures and their application to credit risk assessment

Finlay

2011

European Journal of Operational Research

179

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“…Some studies [15,16,19] found statistical techniques to perform better than AI techniques, while others [20,21] concluded just the opposite. Their comparison results are shown in Table I.…”

Section: Discussionmentioning

confidence: 99%

“…Desai et al [15] investigated the use of GAs as a credit scoring model in a credit-union environment while Yobas et al [16] compared the predictive performances of four techniques, one of which is GAs, in identifying good and bad credit card holders. Interestingly, they [16] found that DA performed better followed by GAs.…”

Section: B Genetic Algorithms (Gas)mentioning

confidence: 99%

Consumer credit scoring using an artificial immune system algorithm

Leung

Cheong

2007

2007 IEEE Congress on Evolutionary Computation

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Abstract-Credit scoring has become a very important task in the credit industry and its use has increased at a phenomenal speed through the mass issue of credit cards since the 1960s. This paper compares the performance of current classifiers against an artificial intelligence technique based on the natural immune system, named simple artificial immune system (SAIS). Experiments were performed on three benchmark credit datasets and SAIS was found to be a very competitive classifier.

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“…In technical terms credit scoring models are mathematical algorithms or statistical programmes that determine the probable repayments of debts by consumers, assigning a score to an individual based on the information processed from a number of data sources and categorising credit applicants according to risk classes. They involve data mining techniques which include statistics, artificial intelligence, machine learning, and other fields aiming at getting knowledge from large databases (see, for example, Bigus 1996, Desai et al 1997, Diana 2005, Handzic et al 2003, Jensen 1992, Yobas et al 2000. Credit scoring shares a number of issues with consumer credit reporting, as a large amount of personal data used to generate the score is built upon the latter.…”

Section: The Role Of Credit Registries In the Consumer Credit Marketmentioning

confidence: 99%

A European Perspective on Consumer Loans and the Role of Credit Registries: the Need toReconcile Data Protection, Risk Management, Efficiency, Over-indebtedness, and a Better Prudential Supervision of the Financial System

Ferretti

2010

J Consum Policy

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The paper examines the role of Credit Registries in the context of European consumer credit markets and the current policies of the EU in this area. It attempts to show the institutional challenges relating to some competing rights or interests among consumers and financial institutions, and the need for a strengthened prudential supervision of the financial system as evidenced by the recent crisis whose effects have spread into the global economy. In particular, it shows that there is a conflict between the right to data protection of consumers, the risk-management interests of lenders, and the prudential supervision of the credit system. The ultimate goal, thus, is to present some weaknesses of the current arrangements and to put forward a proposal that is probably controversial but that is intended to stimulate a debate from an alternative policy perspective that is wider than the current one.

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Credit-scoring models in the credit-union environment using neural networks and genetic algorithms

Cited by 92 publications

References 0 publications

Multiple classifier architectures and their application to credit risk assessment

Multiple classifier architectures and their application to credit risk assessment

Consumer credit scoring using an artificial immune system algorithm

A European Perspective on Consumer Loans and the Role of Credit Registries: the Need toReconcile Data Protection, Risk Management, Efficiency, Over-indebtedness, and a Better Prudential Supervision of the Financial System

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