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Article
Publication date: 25 September 2009

Hussein A. Abdou and John Pointon

The main aims of this paper are: first, to investigate how decisions are currently made within the Egyptian public sector environment; and, second, to determine whether…

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Abstract

Purpose

The main aims of this paper are: first, to investigate how decisions are currently made within the Egyptian public sector environment; and, second, to determine whether the decision making can be significantly improved through the use of credit scoring models. A subsidiary aim is to analyze the impact of different proportions of sub‐samples of accepted credit applicants on both efficient decision making and the optimal choice of credit scoring techniques.

Design/methodology/approach

Following an investigative phase to identify relevant variables in the sector, the research proceeds to an evaluative phase, in which an analysis is undertaken of real data sets (comprising 1,262 applicants), provided by the commercial public sector banks in Egypt. Two types of neural nets are used, and correspondingly two types of conventional techniques are applied. The use of two evaluative measures/criteria: average correct classification (ACC) rate and estimated misclassification cost (EMC) under different misclassification cost (MC) ratios are investigated.

Findings

The currently used approach is based on personal judgement. Statistical scoring techniques are shown to provide more efficient classification results than the currently used judgemental techniques. Furthermore, neural net models give better ACC rates, but the optimal choice of techniques depends on the MC ratio. The probabilistic neural net (PNN) is preferred for a lower cost ratio, whilst the multiple discriminant analysis (MDA) is the preferred choice for a higher ratio. Thus, there is a role for MDA as well as neural nets. There is evidence of statistically significant differences between advanced scoring models and conventional models.

Research limitations/implications

Future research could investigate the use of further evaluative measures, such as the area under the ROC curve and GINI coefficient techniques and more statistical techniques, such as genetic and fuzzy programming. The plan is to enlarge the data set.

Practical implications

There is a huge financial benefit from applying these scoring models to Egyptian public sector banks, for at present only judgemental techniques are being applied in credit evaluation processes. Hence, these techniques can be introduced to support the bank credit decision makers.

Originality/value

Thie paper reveals a set of key variables culturally relevant to the Egyptian environment, and provides an evaluation of personal loans in the Egyptian public sector banking environment, in which (to the best of the author's knowledge) no other authors have studied the use of sophisticated statistical credit scoring techniques.

Details

International Journal of Managerial Finance, vol. 5 no. 4
Type: Research Article
ISSN: 1743-9132

Keywords

Article
Publication date: 14 April 2014

Hussein A. Abdou, Shaair T. Alam and James Mulkeen

This paper aims 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…

Abstract

Purpose

This paper aims 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 is used consisting of 336 accepted credit applications and 151 rejected credit applications made to an Islamic finance house in the UK. To build the proposed scoring models, the data set is divided into training and hold-out subsets. The training subset is used to build the scoring models, and the hold-out subset is used to test the predictive capabilities of the scoring models. Seventy per cent of the overall applicants will be used for the training subset, and 30 per cent will be used for the testing subset. Three statistical modeling techniques, namely, discriminant analysis, logistic regression (LR) and multilayer perceptron (MP) neural network, are used to build the proposed scoring models.

Findings

The findings reveal that the LR model has the highest correct classification (CC) rate in the training subset, whereas MP outperforms other techniques and has the highest CC rate in the hold-out subset. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest misclassification cost 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.

Originality/value

This contribution is the first to apply credit scoring modeling techniques in Islamic finance. Also in building a scoring model, the authors' 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.

Details

International Journal of Islamic and Middle Eastern Finance and Management, vol. 7 no. 1
Type: Research Article
ISSN: 1753-8394

Keywords

Article
Publication date: 21 May 2018

Maher Ala’raj, Maysam Abbod and Mohammed Radi

The purpose of this study is to propose an objective and efficient method for assessing credit risk by introducing and investigating to a greater extent the applicability…

441

Abstract

Purpose

The purpose of this study is to propose an objective and efficient method for assessing credit risk by introducing and investigating to a greater extent the applicability of credit scoring models in the Jordanian banks and to what range they can be used to achieve their strategic and business objectives.

Design/methodology/approach

The research methodology comprises two phases. The first phase is the model development. Three modeling techniques are used to build the scoring models, namely, logistic regression (LR), artificial neural network (NN) and support vector machine (SVM), and the best performing model is selected for next stage. The second phase is two-fold: linking the credit expert knowledge in a way that can enhance the outcomes of the scoring model and a profitability test to explore if the selected model is efficient in meeting banks’ strategic and business objectives.

Findings

The findings showed that LR model outperformed both ANN and SVM across various performance indicators. The LR model also fits best with achieving the bank’s strategic and business objectives.

Originality/value

To the best of the authors’ knowledge, this study is the first that applied several modeling and classification techniques for Jordanian banks and calibrated the best model in terms of its strategic and business objectives. Furthermore, credit experts’ knowledge was engaged with the scoring model to determine its efficiency and reliability against the sole use of an automated scoring model in the hope to encourage the application of credit scoring models as an advisory tool for credit decisions.

Details

International Journal of Islamic and Middle Eastern Finance and Management, vol. 11 no. 4
Type: Research Article
ISSN: 1753-8394

Keywords

Article
Publication date: 9 July 2018

Ceylan Onay and Elif Öztürk

This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big…

3519

Abstract

Purpose

This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big Data bring to credit scoring.

Design/methodology/approach

Content analysis methodology is used to analyze 258 peer-reviewed academic papers from 147 journals from two comprehensive academic research databases to identify their research themes and detect trends and changes in the credit scoring literature according to content characteristics.

Findings

The authors find that credit scoring is going through a quantitative transformation, where data-centric underwriting approaches, usage of non-traditional data sources in credit scoring and their regulatory aspects are the up-coming avenues for further research.

Practical implications

The paper’s findings highlight the perils and benefits of using Big Data in credit scoring algorithms for corporates, governments and non-profit actors who develop and use new technologies in credit scoring.

Originality/value

This paper presents greater insight on how Big Data challenges traditional credit scoring models and addresses the need to develop new credit models that identify new and secure data sources and convert them to useful insights that are in compliance with regulations.

Details

Journal of Financial Regulation and Compliance, vol. 26 no. 3
Type: Research Article
ISSN: 1358-1988

Keywords

Article
Publication date: 1 March 1983

Lawrence C. Galitz

The last twenty years has seen a revolution in consumer credit, with more and more people borrowing on an increasing scale. The explosion in demand for consumer credit

Abstract

The last twenty years has seen a revolution in consumer credit, with more and more people borrowing on an increasing scale. The explosion in demand for consumer credit could probably not have been met successfully without the development of better and more efficient techniques for handling a key decision. This decision — whether or not to lend money to a prospective borrower — underpins all credit operations. The well‐being of a credit institution, and ultimately its survival, depends on the ability to make this fundamental lending decision correctly.

Details

Managerial Finance, vol. 9 no. 3/4
Type: Research Article
ISSN: 0307-4358

Article
Publication date: 5 October 2021

Hongming Gao, Hongwei Liu, Haiying Ma, Cunjun Ye and Mingjun Zhan

A good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to…

Abstract

Purpose

A good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.

Design/methodology/approach

Rooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.

Findings

The distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.

Originality/value

This paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 34 no. 5
Type: Research Article
ISSN: 1355-5855

Keywords

Article
Publication date: 2 January 2009

Hussein A. Abdou

This paper aims to investigate the efficiency and effectiveness of alternative creditscoring models for consumer loans in the banking sector. In particular, the focus is…

2064

Abstract

Purpose

This paper aims to investigate the efficiency and effectiveness of alternative creditscoring models for consumer loans in the banking sector. In particular, the focus is upon the financial risks associated with both the efficiency of alternative models in terms of correct classification rates, and their effectiveness in terms of misclassification costs (MCs).

Design/methodology/approach

A data set of 630 loan applicants was provided by an Egyptian private bank. A two‐thirds training sample was selected for building the proposed models, leaving a one‐third testing sample to evaluate the predictive ability of the models. In this paper, an investigation is conducted into both neural nets (NNs), such as probabilistic and multi‐layer feed‐forward neural nets, and conventional techniques, such as the weight of evidence measure, discriminant analysis and logistic regression.

Findings

The results revealed that a best net search, which selected a multi‐layer feed‐forward net with five nodes, generated both the most efficient classification rate and the most effective MC. In general, NNs gave better average correct classification rates and lower MCs than traditional techniques.

Practical implications

By reducing the financial risks associated with loan defaults, banks can achieve a more effective management of such a crucial component of their operations, namely, the provision of consumer loans.

Originality/value

The use of NNs and conventional techniques in evaluating consumer loans within the Egyptian private banking sector utilizes rigorous techniques in an environment which merits investigation.

Details

The Journal of Risk Finance, vol. 10 no. 1
Type: Research Article
ISSN: 1526-5943

Keywords

Article
Publication date: 7 April 2015

Jie Sun, Hui Li, Pei-Chann Chang and Qing-Hua Huang

Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic…

Abstract

Purpose

Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring.

Design/methodology/approach

This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring.

Findings

The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model.

Originality/value

This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.

Details

Kybernetes, vol. 44 no. 4
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 22 October 2018

Sihem Khemakhem, Fatma Ben Said and Younes Boujelbene

Credit scoring datasets are generally unbalanced. The number of repaid loans is higher than that of defaulted ones. Therefore, the classification of these data is biased…

Abstract

Purpose

Credit scoring datasets are generally unbalanced. The number of repaid loans is higher than that of defaulted ones. Therefore, the classification of these data is biased toward the majority class, which practically means that it tends to attribute a mistaken “good borrower” status even to “very risky borrowers”. In addition to the use of statistics and machine learning classifiers, this paper aims to explore the relevance and performance of sampling models combined with statistical prediction and artificial intelligence techniques to predict and quantify the default probability based on real-world credit data.

Design/methodology/approach

A real database from a Tunisian commercial bank was used and unbalanced data issues were addressed by the random over-sampling (ROS) and synthetic minority over-sampling technique (SMOTE). Performance was evaluated in terms of the confusion matrix and the receiver operating characteristic curve.

Findings

The results indicated that the combination of intelligent and statistical techniques and re-sampling approaches are promising for the default rate management and provide accurate credit risk estimates.

Originality/value

This paper empirically investigates the effectiveness of ROS and SMOTE in combination with logistic regression, artificial neural networks and support vector machines. The authors address the role of sampling strategies in the Tunisian credit market and its impact on credit risk. These sampling strategies may help financial institutions to reduce the erroneous classification costs in comparison with the unbalanced original data and may serve as a means for improving the bank’s performance and competitiveness.

Details

Journal of Modelling in Management, vol. 13 no. 4
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 20 August 2018

Sihem Khemakhem and Younes Boujelbene

Data mining for predicting credit risk is a beneficial tool for financial institutions to evaluate the financial health of companies. However, the ubiquity of selecting…

1894

Abstract

Purpose

Data mining for predicting credit risk is a beneficial tool for financial institutions to evaluate the financial health of companies. However, the ubiquity of selecting parameters and the presence of unbalanced data sets is a very typical problem of this technique. This study aims to provide a new method for evaluating credit risk, taking into account not only financial and non-financial variables, but also the class imbalance.

Design/methodology/approach

The most significant financial and non-financial variables were determined to build a credit scoring model and identify the creditworthiness of companies. Moreover, the Synthetic Minority Oversampling Technique was used to solve the problem of class imbalance and improve the performance of the classifier. The artificial neural networks and decision trees were designed to predict default risk.

Findings

Results showed that profitability ratios, repayment capacity, solvency, duration of a credit report, guarantees, size of the company, loan number, ownership structure and the corporate banking relationship duration turned out to be the key factors in predicting default. Also, both algorithms were found to be highly sensitive to class imbalance. However, with balanced data, the decision trees displayed higher predictive accuracy for the assessment of credit risk than artificial neural networks.

Originality/value

Classification results depend on the appropriateness of data characteristics and the appropriate analysis algorithm for data sets. The selection of financial and non-financial variables, as well as the resolution of class imbalance allows companies to assess their credit risk successfully.

Details

Review of Accounting and Finance, vol. 17 no. 3
Type: Research Article
ISSN: 1475-7702

Keywords

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