Process Innovation for Credit Scoring Using Machine-Learning Approach for Small Financial Institutions.
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| Title: | Process Innovation for Credit Scoring Using Machine-Learning Approach for Small Financial Institutions. |
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| Authors: | Sakchai Suthipipat1 |
| Source: | Turkish Online Journal of Qualitative Inquiry. 2021, Vol. 12 Issue 7, p10977-10994. 18p. |
| Subject Terms: | *Machine learning, Credit ratings, Financial institutions, Loans, Credit risk |
| Geographic Terms: | Thailand |
| Abstract: | Lending is important activity for overall economy, in which it helps fund investment for entrepreneurs to produce goods and services and also helps speed up consumption for the economy. However, credit default, which is the credit to the borrowers who cannot pay back the loan, can create draw back to the economy and cause higher cost of borrowing to all borrowers as financial institutions will increase interest to cover loss from default customers. Then, managing credit default risk is the key success for financial institutions and credit scoring is one of the tools that financial institutions use to manage their credit default risk for consumer loans. Machine Learning with supervised learning technique has been used to develop credit scoring model to classify good customers from default customers for many years. However, due to its complexity and less friendly than other techniques i.e. statistic or judgement method, the use of machine learning to build credit scoring model is limited to only large financial institutions, especially in Thailand market. This study aims to focus on building credit scoring model using supervised learning for medium to small financial institutions in Thailand, in which there are more limitations than large financial institutions in terms of size and quality of credit dataset. This study also focus on imbalanced data problem between majority and minority class of the dataset, which normally number of good customers always dominates number of default customers. [ABSTRACT FROM AUTHOR] |
| Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 161812401 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Process Innovation for Credit Scoring Using Machine-Learning Approach for Small Financial Institutions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sakchai+Suthipipat%22">Sakchai Suthipipat</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Turkish+Online+Journal+of+Qualitative+Inquiry%22">Turkish Online Journal of Qualitative Inquiry</searchLink>. 2021, Vol. 12 Issue 7, p10977-10994. 18p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Credit+ratings%22">Credit ratings</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+institutions%22">Financial institutions</searchLink><br /><searchLink fieldCode="DE" term="%22Loans%22">Loans</searchLink><br /><searchLink fieldCode="DE" term="%22Credit+risk%22">Credit risk</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Thailand%22">Thailand</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Lending is important activity for overall economy, in which it helps fund investment for entrepreneurs to produce goods and services and also helps speed up consumption for the economy. However, credit default, which is the credit to the borrowers who cannot pay back the loan, can create draw back to the economy and cause higher cost of borrowing to all borrowers as financial institutions will increase interest to cover loss from default customers. Then, managing credit default risk is the key success for financial institutions and credit scoring is one of the tools that financial institutions use to manage their credit default risk for consumer loans. Machine Learning with supervised learning technique has been used to develop credit scoring model to classify good customers from default customers for many years. However, due to its complexity and less friendly than other techniques i.e. statistic or judgement method, the use of machine learning to build credit scoring model is limited to only large financial institutions, especially in Thailand market. This study aims to focus on building credit scoring model using supervised learning for medium to small financial institutions in Thailand, in which there are more limitations than large financial institutions in terms of size and quality of credit dataset. This study also focus on imbalanced data problem between majority and minority class of the dataset, which normally number of good customers always dominates number of default customers. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Turkish Online Journal of Qualitative Inquiry is the property of Turkish Online Journal of Qualitative Inquiry and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 10977 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Credit ratings Type: general – SubjectFull: Financial institutions Type: general – SubjectFull: Loans Type: general – SubjectFull: Credit risk Type: general – SubjectFull: Thailand Type: general Titles: – TitleFull: Process Innovation for Credit Scoring Using Machine-Learning Approach for Small Financial Institutions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sakchai Suthipipat IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 13096591 Numbering: – Type: volume Value: 12 – Type: issue Value: 7 Titles: – TitleFull: Turkish Online Journal of Qualitative Inquiry Type: main |
| ResultId | 1 |
