 PROBABILISTIC APPROACHES FOR CREDIT SCREENING AND BANKRUPTCY PREDICTIONParag C. Pendharkar Intelligent Systems in Accounting, Finance and Management, 2011, vol. 18, issue 4, 177-193 Abstract: Three probabilistic neural network approaches are used for credit screening and bankruptcy prediction: a logistic regression neural network (LRNN), a probabilistic neural network (PNN) and a semi‐supervised expectation maximization‐based neural network. Using real‐world bankruptcy prediction and credit screening datasets, we compare the three probabilistic approaches using various performance criteria of sensitivity, specificity, accuracy, decile lift and area under receiver operating characteristics (ROC) curves. The results of our experiments indicate that the PNN outperforms the other two techniques for decile lift and specificity performance metric. Using the area under ROC curve, we find that for bankruptcy prediction data the PNN outperforms the other two approaches when false positive rates (FPRs) are less than 40 %. LRNN outperforms the other two techniques for FPRs higher than 40 % for bankruptcy data. We observe that the LRNN results are very sensitive to the ratio of examples belonging to two classes in training data and there is a tendency to overfit training data. Copyright © 2012 John Wiley & Sons, Ltd. Date: 2011 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:isacfm:v:18:y:2011:i:4:p:177-193 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Intelligent Systems in Accounting, Finance and Management from John Wiley & Sons, Ltd. |
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