{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:19:19Z","timestamp":1750306759032,"version":"3.41.0"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2013,8,6]],"date-time":"2013-08-06T00:00:00Z","timestamp":1375747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGMIS Database"],"published-print":{"date-parts":[[2013,8,6]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1145\/2516955.2516959","type":"journal-article","created":{"date-parts":[[2013,8,23]],"date-time":"2013-08-23T13:36:26Z","timestamp":1377264986000},"page":"49-67","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Classification model for detecting and managing credit loan fraud based on individual-level utility concept"],"prefix":"10.1145","volume":"44","author":[{"given":"Keunho","family":"Choi","sequence":"first","affiliation":[{"name":"Korea University, Seoul, South Korea"}]},{"given":"Gunwoo","family":"Kim","sequence":"additional","affiliation":[{"name":"Hanbat National University, Daejeon, South Korea"}]},{"given":"Yongmoo","family":"Suh","sequence":"additional","affiliation":[{"name":"Korea University, Seoul, South Korea"}]}],"member":"320","published-online":{"date-parts":[[2013,8,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1571-0661(04)80695-9"},{"key":"e_1_2_1_2_1","unstructured":"Chawla N. and Li X. (2006). \"Pricing Based Framework for Benefit Scoring.\" In Proceedings of KDD Workshop on Utility-Based Data Mining Philadelphia.  Chawla N. and Li X. (2006). \"Pricing Based Framework for Benefit Scoring.\" In Proceedings of KDD Workshop on Utility-Based Data Mining Philadelphia."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.02.031"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1089827.1089833"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/0377-2217(95)00246-4"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/312129.312220"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2005.09.028"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2010.05.009"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.10.012"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.03.031"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2008.03.026"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/1540438.1540466"},{"key":"e_1_2_1_13_1","unstructured":"Kou Y. Lu C. T. Sirwongwattana S. and Huang Y. P. (2004). \"Survey of Fraud Detection Techniques.\" In Proceedings of IEEE International Conference on Networking Sensing and Control.  Kou Y. Lu C. T. Sirwongwattana S. and Huang Y. P. (2004). \"Survey of Fraud Detection Techniques.\" In Proceedings of IEEE International Conference on Networking Sensing and Control."},{"key":"e_1_2_1_14_1","unstructured":"Ling C. X. and Sheng V. S. (2008). Cost-Sensitive Learning and the Class Imbalance Problem: Springer.  Ling C. X. and Sheng V. S. (2008). Cost-Sensitive Learning and the Class Imbalance Problem: Springer."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150530"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.131"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(01)00052-2"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1566-2535(02)00100-8"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2010.08.006"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2011.08.018"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2007.01.011"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007738"},{"key":"e_1_2_1_23_1","unstructured":"Phua C. Lee V. Smith K. Gayler R. (2010). \"A Comprehensive Survey of Data Mining-based Fraud Detection Research.\" arXiv:1009.6119: pp. 1--14.  Phua C. Lee V. Smith K. Gayler R. (2010). \"A Comprehensive Survey of Data Mining-based Fraud Detection Research.\" arXiv:1009.6119: pp. 1--14."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.02.001"},{"key":"e_1_2_1_25_1","unstructured":"Stolfo S. J. Fan D. W. Lee W. and Prodromidis A. L. (1997). \"Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results.\" In AAAI Workshop AI Approaches to Fraud Detection and Risk Management.  Stolfo S. J. Fan D. W. Lee W. and Prodromidis A. L. (1997). \"Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results.\" In AAAI Workshop AI Approaches to Fraud Detection and Risk Management."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.07.143"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2007.04.009"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0950-7051(00)00050-2"},{"key":"e_1_2_1_29_1","unstructured":"Witten I. 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