Would Credit Scoring Work for Islamic Finance? A Neural Network Approach

International Journal of Islamic and Middle Eastern Finance and Management, 7 (3), June 2014

19 Pages Posted: 3 Jan 2014 Last revised: 31 Mar 2016

See all articles by Hussein Abdou

Hussein Abdou

The Lancashire School of Business & Enterprise; Department of Management, Faculty of Commerce, Mansoura University

Shaair Alam

University of Salford - Business School

James Mulkeen

University of Salford - Business School

Date Written: January 1, 2013

Abstract

Purpose – The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process.

Design/methodology/approach – A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models.

Findings – Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process.

Research limitations/implications – Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness.

Originality/value – Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected.

Keywords: Islamic finance; decision-making processes; credit scoring techniques; neural networks

JEL Classification: C45

Suggested Citation

Abdou, Hussein and Alam, Shaair and Mulkeen, James, Would Credit Scoring Work for Islamic Finance? A Neural Network Approach (January 1, 2013). International Journal of Islamic and Middle Eastern Finance and Management, 7 (3), June 2014. Available at SSRN: https://ssrn.com/abstract=2374106

Hussein Abdou (Contact Author)

The Lancashire School of Business & Enterprise ( email )

The Lancashire Law School
Corporation Street
Preston, PR1 2HE
United Kingdom
00441772894700 (Phone)

Department of Management, Faculty of Commerce, Mansoura University ( email )

Mansoura, 35516
Egypt

Shaair Alam

University of Salford - Business School ( email )

University of Salford
M5 4WT Salford, Lancashire M5 4WT
United Kingdom

James Mulkeen

University of Salford - Business School ( email )

University of Salford
M5 4WT Salford, Lancashire M5 4WT
United Kingdom

Register to save articles to
your library

Register

Paper statistics

Downloads
34
Abstract Views
520
PlumX Metrics