Classification Model for Detecting and Managing Credit Loan Fraud Based on Individual-Level Utility Concept
DC Field | Value | Language |
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dc.contributor.author | Choi, Keunho | - |
dc.contributor.author | Kim, Gunwoo | - |
dc.contributor.author | Suh, Yongmoo | - |
dc.date.accessioned | 2021-09-05T23:12:13Z | - |
dc.date.available | 2021-09-05T23:12:13Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2013-08 | - |
dc.identifier.issn | 0095-0033 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/102553 | - |
dc.description.abstract | As credit loan products significantly increase in most financial institutions, the number of fraudulent transactions is also growing rapidly. Therefore, to manage the financial risks successfully, the financial institutions should reinforce the qualifications for a loan and augment the ability to detect and manage a credit loan fraud proactively. In the process of building a classification model to detect credit loan frauds, utility from classification results (i.e., benefits from correct prediction and costs from incorrect prediction) is more important than the accuracy rate of classification. The objective of this paper is two-fold: (1) to propose a new approach to building a classification model for detecting credit loan fraud based on an individual-level utility, and (2) to suggest customized interest rate for each customer - from both opportunity utility and cash flow perspectives. Experimental results show that our proposed model comes up with higher utility than the fraud detection models which do not take into account the individual-level utility concept. Also, it is shown that the individual-level utility from our model is more accurate than the mean-level utility used in previous researches, from both opportunity utility and cash flow perspectives. Implications of the experimental results from both perspectives are provided. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.subject | SUBSCRIPTION FRAUD | - |
dc.subject | MINING FRAMEWORK | - |
dc.subject | RULES | - |
dc.title | Classification Model for Detecting and Managing Credit Loan Fraud Based on Individual-Level Utility Concept | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Suh, Yongmoo | - |
dc.identifier.scopusid | 2-s2.0-84885653676 | - |
dc.identifier.wosid | 000323869300005 | - |
dc.identifier.bibliographicCitation | DATA BASE FOR ADVANCES IN INFORMATION SYSTEMS, v.44, no.3, pp.49 - 67 | - |
dc.relation.isPartOf | DATA BASE FOR ADVANCES IN INFORMATION SYSTEMS | - |
dc.citation.title | DATA BASE FOR ADVANCES IN INFORMATION SYSTEMS | - |
dc.citation.volume | 44 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 49 | - |
dc.citation.endPage | 67 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Information Science & Library Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Information Science & Library Science | - |
dc.subject.keywordPlus | SUBSCRIPTION FRAUD | - |
dc.subject.keywordPlus | MINING FRAMEWORK | - |
dc.subject.keywordPlus | RULES | - |
dc.subject.keywordAuthor | Utility-Sensitive Classification | - |
dc.subject.keywordAuthor | Individual-Level Utility | - |
dc.subject.keywordAuthor | Credit Loan Fraud | - |
dc.subject.keywordAuthor | Fraud Detection | - |
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