Improving Credit Risk Analysis with Cluster Based Modeling and Threshold Selection

Ajay Byanjankar*

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review


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
Original languageEnglish
Title of host publicationHawaii International Conference on System Sciences (HICSS)
PublisherHawaii International Conference on System Sciences
Number of pages8
ISBN (Electronic)978-0-9981331-3-3
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication


  • Machine learning
  • Credit Risk
  • Credit Scoring


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