Genetic programming for credit scoring: The case of Egyptian public sector banks

Abdou, Hussein A.

doi:10.1016/j.eswa.2009.01.076

Cited by 95 publications

(84 citation statements)

References 27 publications

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“…For example, the ratio of the misclassification cost for type-I error to the misclassification cost for type-II error in the German database was reported to be 5:1 (West 2000), which has further been taken as the ratio between the costs of both errors for other data in a number of papers (Abdou et al 2007(Abdou et al , 2008Abdou 2009b;Lee and Chen 2005). Figure 4 displays the percentages of papers that have employed each of the most typical performance metrics.…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

2014

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Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more robust decision-making systems capable of managing and analyzing complex data. As a result, plentiful techniques have been developed with the aim of producing accurate prediction models that are able to tackle these issues. However, the design of experiments to assess and compare these models has attracted little attention so far, even though it plays an important role in validating and supporting the theoretical evidence of performance. The experimental design should be done carefully for the results to hold significance; otherwise, it might be a potential source of misleading and contradictory conclusions about the benefits of using a particular prediction system. In this work, we review more than 140 papers published in refereed journals within the period 2000-2013, putting the emphasis on the bases of the experimental design in credit scoring and bankruptcy prediction applications. We provide some caveats and guidelines for the usage of databases, data splitting methods, performance evaluation metrics and hypothesis testing procedures in order to converge on a systematic, consistent validation standard.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

2014

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“…As the vast majority of banks in the Middle Eastern region are commercial banks, we then focus on this group of banks to avoid any potential comparison problems between different types of banks and for homogeneity across different countries included in our final sample. We use data from 10 Middle Eastern countries 3 , as shown in Table 1. Our data are collected from Bankscope database by 3 Israel, Palestinian Territory, Iraq and Syrian Arab Republic are excluded from the sample, because they do not have commercial banks rated by CI.…”

Section: Data Collectionmentioning

confidence: 99%

“…We use data from 10 Middle Eastern countries 3 , as shown in Table 1. Our data are collected from Bankscope database by 3 Israel, Palestinian Territory, Iraq and Syrian Arab Republic are excluded from the sample, because they do not have commercial banks rated by CI. Iran is also excluded from the sample as all Iranian banks are classified as Islamic banks.…”

Section: Data Collectionmentioning

confidence: 99%

Prediction of financial strength ratings using machine learning and conventional techniques

Abdou

Abdallah

Mulkeen

et al. 2017

Investment Management and Financial Innovations

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Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007-2009 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks' financial strength ratings (FSRs) group membership using machine learning and conventional techniques. Here the authors use five different statistical techniques, namely CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression. They also use three different evaluation criteria namely average correct classification rate, misclassification cost and gains charts. The data are collected from Bankscope database for the Middle Eastern commercial banks by reference to the first decade of the 21st century. The findings show that when predicting bank FSRs during the period

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“…To improve the less accuracy of parametric statistical methods, many models based on data-mining methods are built. These methods include the decision trees (DT) (Daviset al, 1992), (Frydman et al, 1985), (Zhou and Zhang (2008)); artificial neural networks (ANN) (Jensen (1992)), (West (2000)), (West et al, 2005); k-nearest neighbour (Henley and Hand (1996)), genetic programming (GP) (Abdou (2009)), (Onget al, 2005); genetic algorithm (GA) (Desai (1997)), (Walker et al,1995), (Zhang et al, 2007); case-based reasoning (CBR) (Chuang and Lin (2009)), (Jo et al, 1997), (Park and Han (2002)); Artificial Immune System Algorithm (Leung et al, 2007); rule extraction based on NN (Setionoet al, 2008); classification based on association rules (Li et al, 2001), (Liu et al, 1998), (Yin and Han (2003)) and support vector machines (SVM) , , , etc.…”

Section: Introductionmentioning

confidence: 99%

Credit scoring using ensemble of various classifiers on reduced feature set

Dahiya¹,

Handa²,

Singh³

2015

Industrija

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Genetic programming for credit scoring: The case of Egyptian public sector banks

Cited by 95 publications

References 27 publications

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

Prediction of financial strength ratings using machine learning and conventional techniques

Credit scoring using ensemble of various classifiers on reduced feature set

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