Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

Khashman, Adnan

doi:10.1016/j.eswa.2010.02.101

Cited by 198 publications

(113 citation statements)

References 41 publications

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“…This is because the continuous growth of biomedical data has largely deteriorated the performance and accuracy of many evaluation techniques [2]. These problems are largely seen in our modern life, for instance, web page classification [3], web spam detection [4], and medical diagnosis [5], [6]. Some of the other fields that involve the analysis of enormous multiclass datasets are mobile commerce [7], bankruptcy or credit detection [8], fraud detection, and crime activity analysis.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

Iram

Jumeily

Fergus

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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-The optimization and evaluation of a pattern recognition system requires different problems like multi-class and imbalanced datasets be addressed. This paper presents the classification of multi-class datasets which present more challenges when compare to binary class datasets in machine learning. Furthermore, it argues that the performance evaluation of a classification model for multi-class imbalanced datasets in terms of simple "accuracy rate" can possibly provide misleading results. Other parameters such as failure avoidance, true identification of positive and negative instances of a class and class discrimination are also very important. We, in this paper, hypothesize that "misclassification of true positive patterns should not necessarily be categorized as false negative while evaluating a classifier for multi-class datasets", a common practice that has been observed in the existing literature. In order to address these hidden challenges for the generalization of a particular classifier, several evaluation metrics are compared for a multi-class dataset with four classes; three of them belong to different neurodegenerative diseases and one to control subjects. Three classifiers, linear discriminant, quadratic discriminant and Parzen are selected to demonstrate the results with examples.

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Section: Introductionmentioning

confidence: 99%

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

Iram

Jumeily

Fergus

et al. 2014

2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems

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show abstract

“…Paper [13] explores the performance of credit scoring using ANN and multivariate adaptive regression splines (MARS). Paper [14] neural network to evaluate credit risk.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Credit Risk Assessment of Bank Customers

Pourdarab¹,

Nadali²,

Nosratabadi³

2011

IJTEF

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Abstract-Credit risk assessment is one of the crucial issues which financial institutions particularly banks are faced and determining the effective variables is one of the critical parts in this type of studies. The purpose of this research is to present a hybrid method for evaluating credit risk of bank customers. Here, after extracting significant financial ratios from balance sheet, Kolmogorove-Smirnov test has been used to specify kind of financial ratios distribution. Then, T test has been run to select meaningful variables and DEMATEL method to determine effective ones. Finally, a Fuzzy Expert system has been developed to assess credit risk according to specified effective financial ratios as the system inputs. The presented steps have been studied in an Iranian Bank as empirical study.

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“…Credit rating is one of technical factor in credit risk evaluation (Khashman, 2010). The aim of credit rating is to categorize the applicants into two groups; applicants with good credit and applicants with bad credit (Ghodselahi & Amirmadhi, 2011).…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring Credit Risk of Bank Customers Using Artificial Neural Network

Nazari

Alidadi

2013

jmr

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In many studies, the relationship between development of financial markets and economic growth has been proved. Credit risk is one of problems which banks are faced with it while doing their tasks. Credit risk means the probability of non-repayment of bank financial facilities granted to investors. If the credit risk decreases, banks will be more successful in performing their duties and have greater effect on economic growth of the country. Credit rating of customers and identifying good and bad customers, help banks lend to their good payers and hereby, they reduce probability of non-repayment. This paper aims to identify classification criteria for good customers and bad customers in Iranian banks. This study can classified in applied studies group and the research strategy is descriptive. Artificial neural network technique is used for financial facilities applicants' credit risk measurement and the calculations have been done by using SPSS and MATLAB software. Number of samples was 497 and 18 variables were used to identify good customers from bad customers. The results showed that, individual loan frequency and amount of loan had most important effect and also status of customer's bank account, history of customer relationship with bank and received services had least important effect in identifying classification criteria of good and bad customers. The major contribution of this paper is specifying the most important determinants for rating of customers in Iran's banking sector.

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Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes

Cited by 198 publications

References 41 publications

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets Using Multiple Classifiers

A Hybrid Method for Credit Risk Assessment of Bank Customers

Measuring Credit Risk of Bank Customers Using Artificial Neural Network

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