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Predicting creditworthiness in retail banking with limited scoring data

Predicting creditworthiness in retail banking with limited scoring data
Predicting creditworthiness in retail banking with limited scoring data
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.
89-103
Abdou, Hussein A.
c5679c57-2de9-452f-8434-1a0452563e0a
Tsafack, Dongmo M.
f2c27a01-0351-42dc-9968-c1167e65bc26
Ntim, Collins
1f344edc-8005-4e96-8972-d56c4dade46b
Baker, Rose
25332b71-2f5b-4dc6-bfb1-711381b4e84e
Abdou, Hussein A.
c5679c57-2de9-452f-8434-1a0452563e0a
Tsafack, Dongmo M.
f2c27a01-0351-42dc-9968-c1167e65bc26
Ntim, Collins
1f344edc-8005-4e96-8972-d56c4dade46b
Baker, Rose
25332b71-2f5b-4dc6-bfb1-711381b4e84e

Abdou, Hussein A., Tsafack, Dongmo M., Ntim, Collins and Baker, Rose (2016) Predicting creditworthiness in retail banking with limited scoring data. Knowledge-Based Systems, 103, 89-103. (doi:10.1016/j.knosys.2016.03.023).

Record type: Article

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.

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More information

Accepted/In Press date: 25 March 2016
e-pub ahead of print date: 12 April 2016
Published date: 1 July 2016
Organisations: Centre of Excellence for International Banking, Finance & Accounting

Identifiers

Local EPrints ID: 401001
URI: https://eprints.soton.ac.uk/id/eprint/401001
PURE UUID: d8066fe5-1d30-4f63-9251-867b3854f74b
ORCID for Collins Ntim: ORCID iD orcid.org/0000-0002-1042-4056

Catalogue record

Date deposited: 03 Oct 2016 09:27
Last modified: 19 Dec 2018 05:01

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