A credit scoring approach for the commercial banking sector

Emel, Ahmet Burak; Oral, Muhittin; Reisman, Arnold; Yolalan, Reha

doi:10.1016/s0038-0121(02)00044-7

Cited by 109 publications

(67 citation statements)

References 29 publications

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“…Firstly, DEA has been used to derive a classification algorithm to separate distressed firms from non-distressed firms (Paradi et al, 2004;Cielen et al 2004;Emel et al, 2003). Secondly, the relative efficiency of firms has been computed using DEA and this relative efficiency has been used as a feature of each firm in a subsequently developed classification rule (Xu and Wang, 2009;Yeh et al, 2010;Psillaki et al, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

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Chinese companies distress prediction: an application of data envelopment analysis

Crook

Andreeva

2014

Journal of the Operational Research Society

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Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency.In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency -Technical Efficiency, we assume Variable Returns to Scale, and decompose Technical Efficiency into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the levels of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, whilst the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power is improved by this corporate efficiency information.

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Section: Literature Reviewmentioning

confidence: 99%

“…Both Emel et al (2003) and Min and Lee (2008) estimated a statistical model to predict DEA efficiency using the input and output financial ratios that could be used to classify out of sample cases.…”

Section: Literature Reviewmentioning

confidence: 99%

Chinese companies distress prediction: an application of data envelopment analysis

Crook

Andreeva

2014

Journal of the Operational Research Society

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“…According to their study, if a company received a z score of lower than the critical level the company's position is regarded as unhealthy (Saunders & Allen, 2009). Emel et al, (2003) used data envelopment analysis (DEA) and proposed a credit scoring approach. They used current financial data of 82 industrial/productive companies that comprised the credit portfolio of one of the largest Turkish banks.…”

Section: Review Of Literaturementioning

confidence: 99%

“…They used current financial data of 82 industrial/productive companies that comprised the credit portfolio of one of the largest Turkish banks. To do so, they examined 42 financial ratios and chose 6 important ratios to be used for credit rankings of companies (Emel et al, 2003 Vol. 6, No.…”

Section: Review Of Literaturementioning

confidence: 99%

Credit Scoring Model for Iranian Banking Customers and Forecasting Creditworthiness of Borrowers

Mansouri¹,

Dastoori²

2013

IBR

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Credit risk is one of important challenges facing banks and credit institutions in all economic systems. Accordingly, much research has focused on credit scoring of bank customers and to predict and classify solvent customers and insolvent customers. In this line, the present study has attempted to identify and employ financial ratios affecting the creditworthiness of banking customers using discriminant analysis and logistic regression to determine the reliability of different credit scoring models in predicting creditworthiness of banking customers. To do so, customers' credit files in one of the branches of commercial Iranian banks in Province were investigated and totally 54 creditable firms and 46 non-creditable firms were identified. The creditworthiness of these firms was determined through discriminant analysis and logic regression using financial information obtained from the sample firms from 2006 to 2011. The results of the study indicated that the two fitted models are reasonable reliable in predicting the creditworthiness of banking customers. However, the logic regression model had a higher discrimination power than the discriminant analysis model.

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“…The main advantages of the proposed network are the ability for linguistic representation, linguistic aggregation and the learning ability of the neural network [5].There is another paper which considers the question of whether inaccurate self-assessment of credit is associated with undesirable financial market outcomes [6]. Another one has suggested the design a N-tier model integrated with the idea of Model-View-Controller to help to finalize the scoring models quickly and easily alter the embedded scoring models later [7].For managing credit risk, another paper upgrades the quantitative analysis used in the financial performance modules of state-of-the-art credit scoring methodologies [8].…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Labeling the Class of Bank Credit Customers via Classification Method in Data Mining

Nosratabadi¹,

Pourdarab²,

Nadali³

2011

IJIET

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Abstract-ONE of the most important parts in credit scoring is determining the class of customers to run the Data Mining algorithms. The purpose of this research is estimating the Label of Credit customers via Fuzzy Expert System. Here the class of customers has been specified by a Fuzzy Expert System and then the Data Mining Algorithms have been run on the final data with Clementine software. The presented steps have been studied in an Iranian Bank as empirical study.

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A credit scoring approach for the commercial banking sector

Cited by 109 publications

References 29 publications

Chinese companies distress prediction: an application of data envelopment analysis

Chinese companies distress prediction: an application of data envelopment analysis

Credit Scoring Model for Iranian Banking Customers and Forecasting Creditworthiness of Borrowers

A New Approach for Labeling the Class of Bank Credit Customers via Classification Method in Data Mining

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