Application of support vector machines for reject inference in credit scoring

F. Yaurita, Zuherman Rustam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the banking industry, credit scoring models are commonly built to help gauge the level of risk associated with approving an applicant. Credit scoring models are built on a sample of accepted applicants whose repayment and behavior information is observable once the loan has been issued. For application credit scoring, any declined applicant did not use anymore in the model, since the observation contains no outcome. However, when an applicant is rejected, there is a probability that he has good behavior, but he is rejected because of a miss classification. That is why the rejected applicant should be reconsidered. It will be useful for increasing the company's market share. Reject inference is a technique used in the credit industry that attempts to infer the good or bad loan status of the rejected applicants. The objective of this research is we want to classify the rejected applicants into 'good' and 'bad' behavior by using Support Vector Machines (SVM). The results are very encouraging, we found that SVM achieved 85% accuracy rate with RBF kernel, 40% data training, and σ = 0.0001.

Original languageEnglish
Title of host publicationProceedings of the 3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
EditorsRatna Yuniati, Terry Mart, Ivandini T. Anggraningrum, Djoko Triyono, Kiki A. Sugeng
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735417410
DOIs
Publication statusPublished - 22 Oct 2018
Event3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017 - Bali, Indonesia
Duration: 26 Jul 201727 Jul 2017

Publication series

NameAIP Conference Proceedings
Volume2023
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference3rd International Symposium on Current Progress in Mathematics and Sciences 2017, ISCPMS 2017
CountryIndonesia
CityBali
Period26/07/1727/07/17

Keywords

  • credit scoring
  • reject inference
  • support vector machines

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