Predicting Creditworthiness in Retail Banking with Limited Scoring Data

Abdou, Hussein, Tsafack, M. Dongmo, Ntim, Collins & Baker, Rose, Predicting creditworthiness in retail banking with limited scoring data, Knowledge-Based Systems (2016), Forthcoming

50 Pages Posted: 1 Apr 2016 Last revised: 14 Apr 2016

See all articles by Hussein Abdou

Hussein Abdou

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

Marc Tsafack

University of Salford

Collins G. Ntim

University of Southampton Business School, UK; University of Southampton

Rose Baker

University of Salford - Department of Economics

Date Written: March 25, 2016

Abstract

The preoccupation with modelling credit scoring systems including their relevance to predicting and decision making in the financial sector has been with developed countries, whilst developing countries have been largely neglected. The focus of our investigation is on the Cameroonian banking sector with implications for fellow members of the Banque des Etats de L’Afrique Centrale (BEAC) family which apply the same system. We apply logistic regression (LR), Classification and Regression Tree (CART) and Cascade Correlation Neural Network (CCNN) in building our knowledge-based scoring models. To compare various models’ performances we use ROC curves and Gini coefficients as evaluation criteria and the Kolmogorov-Smirnov curve as a robustness test. The results demonstrate that an improvement in terms of predicting power from 15.69% default cases under the current system, to 7.68% based on the best scoring model, namely CCNN can be achieved. The predictive capabilities of all models are rated as at least very good using the Gini coefficient; and rated excellent using the ROC curve for CCNN. Our robustness test confirmed these results. It should be emphasised that in terms of prediction rate, CCNN is superior to the other techniques investigated in this paper. Also, a sensitivity analysis of the variables identifies previous occupation, borrower’s account functioning, guarantees, other loans and monthly expenses as key variables in the forecasting and decision making processes which are at the heart of overall credit policy.

Keywords: Predicting creditworthiness; credit scoring; cascade correlation neural networks; CART; limited data.

JEL Classification: E50; G21; C45

Suggested Citation

Abdou, Hussein and Tsafack, Marc and Ntim, Collins G. and Baker, Rose, Predicting Creditworthiness in Retail Banking with Limited Scoring Data (March 25, 2016). Abdou, Hussein, Tsafack, M. Dongmo, Ntim, Collins & Baker, Rose, Predicting creditworthiness in retail banking with limited scoring data, Knowledge-Based Systems (2016), Forthcoming . Available at SSRN: https://ssrn.com/abstract=2756746 or http://dx.doi.org/10.2139/ssrn.2756746

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

Marc Tsafack

University of Salford ( email )

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

Collins G. Ntim

University of Southampton Business School, UK ( email )

Southampton Business School
Highfield
Southampton, England SO17 IBJ
United Kingdom
+44 (0) 238059 4285 (Phone)
+44 (0) 238059 3844 (Fax)

HOME PAGE: http://www.southampton.ac.uk/business-school/about/staff/cgn1n11.page

University of Southampton ( email )

Southampton, SO17 1BJ
United Kingdom

Rose Baker

University of Salford - Department of Economics ( email )

Greater Manchester M5 4WT, England
United Kingdom

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