Credit risk assessment model for Jordanian commercial banks

Neural scoring approach

Hussain A. Bekhet, Shorouq Fathi Kamel Eletter

Research output: Contribution to journalArticle

37 Citations (Scopus)

Abstract

Despite the increase in the number of non-performing loans and competition in the banking market, most of the Jordanian commercial banks are reluctant to use data mining tools to support credit decisions. Artificial neural networks represent a new family of statistical techniques and promising data mining tools that have been used successfully in classification problems in many domains. This paper proposes two credit scoring models using data mining techniques to support loan decisions for the Jordanian commercial banks. Loan application evaluation would improve credit decision effectiveness and control loan office tasks, as well as save analysis time and cost. Both accepted and rejected loan applications, from different Jordanian commercial banks, were used to build the credit scoring models. The results indicate that the logistic regression model performed slightly better than the radial basis function model in terms of the overall accuracy rate. However, the radial basis function was superior in identifying those customers who may default.

Original languageEnglish
Pages (from-to)20-28
Number of pages9
JournalReview of Development Finance
Volume4
Issue number1
DOIs
Publication statusPublished - 01 Jan 2014

Fingerprint

Credit risk
Risk assessment
Loans
Scoring
Commercial banks
Data mining
Radial basis function
Credit
Credit scoring
Logistic regression model
Banking
Artificial neural network
Non-performing loans
Costs
Evaluation

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

Cite this

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abstract = "Despite the increase in the number of non-performing loans and competition in the banking market, most of the Jordanian commercial banks are reluctant to use data mining tools to support credit decisions. Artificial neural networks represent a new family of statistical techniques and promising data mining tools that have been used successfully in classification problems in many domains. This paper proposes two credit scoring models using data mining techniques to support loan decisions for the Jordanian commercial banks. Loan application evaluation would improve credit decision effectiveness and control loan office tasks, as well as save analysis time and cost. Both accepted and rejected loan applications, from different Jordanian commercial banks, were used to build the credit scoring models. The results indicate that the logistic regression model performed slightly better than the radial basis function model in terms of the overall accuracy rate. However, the radial basis function was superior in identifying those customers who may default.",
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Credit risk assessment model for Jordanian commercial banks : Neural scoring approach. / A. Bekhet, Hussain; Eletter, Shorouq Fathi Kamel.

In: Review of Development Finance, Vol. 4, No. 1, 01.01.2014, p. 20-28.

Research output: Contribution to journalArticle

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