Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features

Simple item page
aut.researcherZhang, Wenjun
dc.contributor.authorBunker, RPen_NZ
dc.contributor.authorZhang, Wen_NZ
dc.contributor.authorNaeem, MAen_NZ
dc.date.accessioned2019-09-06T04:22:13Z
dc.date.available2019-09-06T04:22:13Z
dc.description.abstractIn this paper, we investigate the extent to which features derived from bank statements provided by loan applicants, and which are not declared on an application form, can enhance a credit scoring model for a New Zealand lending company. Exploring the potential of such information to improve credit scoring models in this manner has not been studied previously. We construct a baseline model based solely on the existing scoring features obtained from the loan application form, and a second baseline model based solely on the new bank statement-derived features. A combined feature model is then created by augmenting the application form features with the new bank statement derived features. Our experimental results using ROC analysis show that a combined feature model performs better than both of the two baseline models, and show that a number of the bank statement-derived features have value in improving the credit scoring model. The target data set used for modelling was highly imbalanced, and Naive Bayes was found to be the best performing model, and outperformed a number of other classifiers commonly used in credit scoring, suggesting its potential for future use on highly imbalanced data sets.en_NZ
dc.identifier.citationarXiv:1611.00252 [cs.LG]
dc.identifier.urihttps://hdl.handle.net/10292/12798
dc.publisherarXiv
dc.relation.urihttps://arxiv.org/abs/1611.00252
dc.rightsarXiv places no restrictions on whether articles also appear in local institutional repositories. Authors are welcome to download copies of their own articles from arXiv in order to submit to a local repository.
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectcs.LGen_NZ
dc.subjectcs.LGen_NZ
dc.subjectCredit Risk; ROC Curve; Imbalanced Data; Machine Learning
dc.titleImproving a Credit Scoring Model by Incorporating Bank Statement Derived Featuresen_NZ
dc.typeJournal Article
pubs.elements-id214061
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Engineering, Computer & Mathematical Sciences
pubs.organisational-data/AUT/PBRF
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies
pubs.organisational-data/AUT/PBRF/PBRF Design and Creative Technologies/PBRF ECMS
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
1611.00252v2.pdf
Size:
873.38 KB
Format:
Adobe Portable Document Format
Description:
Journal article
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
AUT Grant of Licence for Scholarly Commons Feb2017.pdf
Size:
239.25 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau