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Detecting Credit Loan Fraud Based on Individual-Level Utility
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Keunho Choi (Business School, Korea University)
Gunwoo Kim (Department of Business and Accounting, Hanbat National University)
Yongmoo Suh (Business School, Korea University)
Vol. 18, No. 4, Page: 79 ~ 95
Keywords
Utility-Sensitive Classification, Credit Loan Fraud, Fraud Detection
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 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 to propose a new approach to building a classification model for detecting credit loan fraud based on an individual-level utility. Experimental results show that the 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 computed by the model is more accurate than the mean-level utility computed by other models, in both opportunity utility and cash flow perspectives. We provide diverse views on the experimental results from both perspectives.
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개인별 유틸리티에 기반한 신용 대출 사기 탐지
최근호 (고려대학교 경영대학)
김건우 (국립한밭대학교 경영회계학과)
서용무 (고려대학교 경영대학)
Abstract
금융기관들에서 개발한 신용 대출 상품이 증가함에 따라 사기 거래의 수 또한 급속히 증가하고 있다. 따라서, 재정적 위험을 성공적으로 관리하기 위해 금융기관들은 대출 승인 심사를 강화하고 신용 대출 사기를 사전에 탐지할 수 있는 능력을 증대시켜 나가야 한다. 신용 대출 사기를 탐지하기 위한 분류 모델을 구축하는 과정에서 분류 결과에 따른 유틸리티(즉, 정분류에 따른 이익과 오분류에 따른 비용)는 분류의 정확도보다 더 중요하다. 본 연구는 개인별 유틸리티에 기반하여 신용 대출 사기를 탐지하기 위한 분류 모델을 구축하는 것을 목적으로 하였다. 다양한 실험을 통해, 본 연구에서 제시한 모델이 기회 유틸리티와 현금 흐름의 두 관점 모두에서 개인별 유틸리티에 기반하지 않은 모델보다 더 높은 유틸리티를 제공하며, 평균 유틸리티에 기반한 모델보다 더 정확한 유틸리티를 제공한다는 것을 보였다. 본 연구는 기회 유틸리티와 현금 흐름의 두 관점에서 얻어진 실험 결과를 다양한 측면에서 살펴보았다.
Cite this article
JIIS Style
Choi, K., G. Kim, and Y. Suh, "Detecting Credit Loan Fraud Based on Individual-Level Utility ", Journal of Intelligence and Information Systems, Vol. 18, No. 4 (2012), 79~95.

IEEE Style
Keunho Choi, Gunwoo Kim, and Yongmoo Suh, "Detecting Credit Loan Fraud Based on Individual-Level Utility ", Journal of Intelligence and Information Systems, vol. 18, no. 4, pp. 79~95, 2012.

ACM Style
Choi, K., Kim, G., and Suh, Y., 2012. Detecting Credit Loan Fraud Based on Individual-Level Utility . Journal of Intelligence and Information Systems. 18, 4, 79--95.
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@article{Choi:JIIS:2012:513,
author = {Choi, Keunho and Kim, Gunwoo and Suh, Yongmoo},
title = {Detecting Credit Loan Fraud Based on Individual-Level Utility },
journal = {Journal of Intelligence and Information Systems},
issue_date = {December 2012},
volume = {18},
number = {4},
month = Dec,
year = {2012},
issn = {2288-4866},
pages = {79--95},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Utility-Sensitive Classification, Credit Loan Fraud and Fraud Detection },
}
%0 Journal Article
%1 513
%A Keunho Choi
%A Gunwoo Kim
%A Yongmoo Suh
%T Detecting Credit Loan Fraud Based on Individual-Level Utility
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 18
%N 4
%P 79-95
%D 2012
%R
%I Korea Intelligent Information System Society