Publication:
The blend of credit scoring model for individual in the dmaic process for reducing non-performing loan risk

dc.contributor.authorVongseriprathna Thavarithen_US
dc.contributor.authorJirapan Liangrokaparten_US
dc.contributor.otherMahidol Universityen_US
dc.date.accessioned2020-01-27T08:19:54Z
dc.date.available2020-01-27T08:19:54Z
dc.date.issued2019-05-24en_US
dc.description.abstract© 2019 ACM. Non-performing loan (NPL) is the main threat for all financial institutions. In order to improve loan approval process and reduce the risk of NPL, this research proposes an application of six sigma and credit scoring model. Six Sigma is an outstanding tool for process improvement by reducing defects in the process in manufacturing and service industries. Credit scoring model is a statistical model that aid in the decision making for the bank and other financial institution whether they should approve or reject the loan application. Six Sigma offers value by reducing defects and Credit scoring model can enhance credit lending policy. The implementation of Six sigma and Credit scoring model in bank loan approval process is a new topic and few literatures have studied in this area. The objectives of this research are to identify factors causing NPL and propose framework using Six Sigma and Credit scoring model to improve bank loan process and enhance the credit lending policy to reduce the risk of NPL. Case study of a bank in Cambodia is illustrated.en_US
dc.identifier.citationACM International Conference Proceeding Series. (2019), 195-202en_US
dc.identifier.doi10.1145/3335550.3335583en_US
dc.identifier.other2-s2.0-85071004859en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/50633
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85071004859&origin=inwarden_US
dc.subjectComputer Scienceen_US
dc.titleThe blend of credit scoring model for individual in the dmaic process for reducing non-performing loan risken_US
dc.typeConference Paperen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85071004859&origin=inwarden_US

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