Using adaptive learning in credit scoring to estimate take-up probability distribution

Intell. Sys. Acc. Fin. Mgmt.

Pointon

2011

294

209

SummaryCredit scoring has been regarded as a core appraisal tool of different institutions during the last few decades, and has been widely investigated in different areas, such as finance and accounting. Different scoring techniques are being used in areas of classification and prediction, where statistical techniques have conventionally been used. Both sophisticated and traditional techniques, as well as performance evaluation criteria are investigated in the literature. The principal aim of this paper is to carry out a comprehensive review of 214 articles/books/theses that involve credit scoring applications in various areas, in general, but primarily in finance and banking, in particular. This paper also aims to investigate how credit scoring has developed in importance, and to identify the key determinants in the construction of a scoring model, by means of a widespread review of different statistical techniques and performance evaluation criteria. Our review of literature revealed that there is no overall best statistical technique used in building scoring models and the best technique for all circumstances does not yet exist. Also, the applications of the scoring methodologies have been widely extended to include different areas, and this subsequently can help decision makers, particularly in banking, to predict their clients" behaviour. Finally, this paper also suggests a number of directions for future research.

Section: Review Of the Literaturementioning

confidence: 99%

Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature

Intell. Sys. Acc. Fin. Mgmt.

Pointon

2011

294

209

“…Hybrid models, as well as neural networks and advanced statistical techniques have been used in building scoring models (Kim & Sohn, 2004;Lee et al, 2002;Blochlinger & Leippold, 2006;Lee & Chen, 2005;Seow & Thomas, 2006). Meanwhile, comparisons between traditional and advanced statistical techniques have been investigated too (Lee & Chen, 2005;Lee et al, 2002;Zekic-Susac et al, 2004;Malhotra & Malhotra, 2003;Ong, Huang, & Tzeng, 2005).…”

Section: Literature Reviewmentioning

confidence: 99%

Neural nets versus conventional techniques in credit scoring in Egyptian banking

Expert Systems with Applications

Pointon

El‐Masry

2008

131

Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank's personal loans' data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques.

“…Other statistical models, as well as neural networks and fuzzy algorithms have been used in building scoring models (Tsai and Wu, 2008;Anderson, 2007;Crook et al, 2007;Nur Ozkan-Gunay and Ozkan, 2007;Yu et al, 2007;Seow and Thomas, 2006;Hand et al, 2005;Zhang and Bhattacharyya, 2004). Furthermore, comparisons between conventional and advanced statistical scoring techniques have been investigated as well (Lee and Chen, 2005;Ong et al, 2005;Lee et al, 2002;Desai et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

An evaluation of alternative scoring models in private banking

The Journal of Risk Finance

2009

Purpose -This paper aims to investigate the efficiency and effectiveness of alternative credit-scoring 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.