Improving Credit Risk Analysis with Cluster Based Modeling and Threshold Selection Byanjankar, Ajay 2020-01-04T07:26:19Z 2020-01-04T07:26:19Z 2020-01-07
dc.description.abstract Credit risk has been an integral part of financial industry and is a challenging and difficult risk to manage. The diverse behavior of borrowers adds challenges to the risk analysis. Failing to accurately identify the borrowers' risk can lead to huge investment losses. Credit scoring is a popular and commonly used technique to analyze credit risk. A single credit scoring model may not be capable of generating a common rule to classify borrowers and hence segmented modeling can be applied to create more specific classification rules for achieving higher classification accuracy. In this study segmented modeling is applied with threshold selection for each segment to reduce relative cost of misclassification. The results from the study show that threshold selection based on the segmented modeling can give improvement over a single credit scoring model.
dc.format.extent 8 pages
dc.identifier.doi 10.24251/HICSS.2020.174
dc.identifier.isbn 978-0-9981331-3-3
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Machine Learning and Predictive Analytics in Accounting, Finance and Management
dc.subject credit risk
dc.subject credit scoring
dc.subject relative cost
dc.subject segmented modeling
dc.title Improving Credit Risk Analysis with Cluster Based Modeling and Threshold Selection
dc.type Conference Paper
dc.type.dcmi Text
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