PDF가 표시되지 않으면 여기를 클릭하여 다운로드

1
Abdou, H. A. and Pointon, J. (2011). “Credit Scoring Statistical Techniques and Evaluation Criteria A Review of the Literature,” Intelligent Systems in Accounting, Finance and Management, 18(2-3), pp.59-88. Google Scholar ›
2
Bao, T., Ding, Y., Gopal, R. and Möhlmann, M. (2024). “Throwing Good Money after Bad Risk Mitigation Strategies in P2P Lending Platforms,” Information Systems Frontiers, 26(4), pp.1453-1473. Google Scholar ›
3
Barth, J. R., Gotur, P., Manage, N. and Yezer, A. M. (1983). “The Effect of Government Regulations on Personal Loan Markets A Tobit Estimation of a Microeconomic Model,” Journal of Finance, 38(4), pp.1233-1251. Google Scholar ›
4
Blumenstock, J., Cadamuro, G. and On, R. (2015). “Predicting Poverty and Wealth from Mobile Phone Metadata,” Science, 350(6264), pp.1073-1076. Google Scholar ›
5
Boyes, W. J., Hoffman, D. L. and Low, S. A. (1989). “An Econometric Analysis of the Bank Credit Scoring Problem,” Journal of Econometrics, 40(1), pp.3-14. Google Scholar ›
6
Centola, D. (2011). “An Experimental Study of Homophily in the Adoption of Health Behavior,” Science, 334(6060), pp.1269-1272. Google Scholar ›
7
Chen, T. and Guestrin, C. (2016). “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.785-794. Google Scholar ›
8
Crandall, D. J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D. and Kleinberg, J. (2010). “Inferring Social Ties from Geographic Coincidences,” Proceedings of the National Academy of Sciences, 107(52), pp.22436-22441. Google Scholar ›
9
Dandekar, P., Goel, A. and Lee, D. T. (2013). “Biased Assimilation Homophily and the Dynamics of Polarization,” Proceedings of the National Academy of Sciences, 110(15), pp.5791-5796. Google Scholar ›
10
de Montjoye, Y. A., Hidalgo, C. A., Verleysen, M. and Blondel, V. D. (2013). “Unique in the Crowd The Privacy Bounds of Human Mobility,” Scientific Reports, 3(1), Article 1376. Google Scholar ›
11
Fang, Z., Gu, B., Luo, X. and Xu, Y. (2015). “Contemporaneous and Delayed Sales Impact of Location-Based Mobile Promotions,” Information Systems Research, 26(3), pp.552-564. Google Scholar ›
12
Florida, R. L. (2004). The Rise of the Creative Class and How It’s Transforming Work Leisure Community and Everyday Life, Basic Books. Google Scholar ›
13
Friedman, J. H. (2001). “Greedy Function Approximation A Gradient Boosting Machine,” Annals of Statistics, 29(5), pp.1189-1232. Google Scholar ›
14
Glaeser, E. L., Kolko, J. and Saiz, A. (2001). “Consumer City,” Journal of Economic Geography, 1(1), pp.27-50. Google Scholar ›
15
Goffman, E. (1963). Stigma: Notes on the Management of Spoiled Identity, Prentice-Hall. Google Scholar ›
16
Golub, B. and Jackson, M. O. (2012). “How Homophily Affects the Speed of Learning and Best-Response Dynamics,” Quarterly Journal of Economics, 127(3), pp.1287-1338. Google Scholar ›
17
Han, I. G., Kwon, Y. S. and Lee, K. C. (1995). “Development of Intelligent Corporate Credit Evaluation System: NICE-AI,” Korean Management Review, 24(4), pp.91-118. Google Scholar ›
18
Hand, D. J. and Jacka, S. D. (1998). Statistics in Finance, Wiley. Google Scholar ›
19
Hotho, A., Nürnberger, A. and Paaß, G. (2005). “A Brief Survey of Text Mining,” LDV Forum, 20(1), pp.19-62. Google Scholar ›
20
Huang, J., Sena, V., Li, J. and Ozdemir, S. (2021). “Message Framing in P2P Lending Relationships,” Journal of Business Research, 122, pp.761-773. Google Scholar ›
21
Jiang, C., Wang, Z., Wang, R. and Ding, Y. (2018). “Loan Default Prediction by Combining Soft Information Extracted from Descriptive Text in Online Peer-to-Peer Lending,” Annals of Operations Research, 266, pp.511-529. Google Scholar ›
22
Kang, D. C. and Kim, J. T. (2024). “An Analysis of Credit Rating Agency’s Rating Adjustments Effects on Rating Split,” Korean Management Review, 53(2), pp.321-344. Google Scholar ›
23
Lawrence, E. C., Smith, L. D. and Rhoades, M. (1992). “An Analysis of Default Risk in Mobile Home Credit,” Journal of Banking and Finance, 16(2), pp.299-312. Google Scholar ›
24
Lin, M., Prabhala, N. R. and Viswanathan, S. (2013). “Judging Borrowers by the Company They Keep: Friendship Networks and Information Asymmetry in Online Peer-to-Peer Lending,” Management Science, 59(1), pp.17-35. Google Scholar ›
25
Link, B. G. and Phelan, J. C. (2001). “Conceptualizing Stigma,” Annual Review of Sociology, 27(1), pp.363-385. Google Scholar ›
26
Liu, D., Brass, D. J., Lu, Y. and Chen, D. (2015). “Friendships in Online Peer-to-Peer Lending: Pipes Prisms and Relational Herding,” MIS Quarterly, 39(3), pp.729-742. Google Scholar ›
27
Mc Pherson, M., Smith-Lovin, L. and Cook, J. M. (2001). “Birds of a Feather: Homophily in Social Networks,” Annual Review of Sociology, 27(1), pp.415-444. Google Scholar ›
28
Molitor, D., Reichhart, P. and Spann, M. (2016). “Location-based Advertising and Contextual Mobile Targeting,” In Proceedings of the Thirty Seventh International Conference on Information Systems, Dublin, Ireland. Google Scholar ›
29
Mundial, B. (2016). World Development Report 2016 Digital Dividends, World Bank Group. Google Scholar ›
30
Narang, U. and Luco, F. (2025). “Privacy and Prediction: How Useful Are Geo-Tracking Data for Predicting Consumer Visits,” Quantitative Marketing and Economics, 23(4), pp.523-544. Google Scholar ›
31
Pan, W., Aharony, N. and Pentland, A. S. (2011). “Composite Social Network for Predicting Mobile App Installation,” Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI Press, San Francisco, pp.821-827. Google Scholar ›
32
Park, J. W. and Ahn, S. M. (2014). “Corporate Bankruptcy Prediction Using Financial Ratios: Focused on the Korean Manufacturing Companies Audited by External Auditors,” Korean Management Review, 43(3), pp.639-669. Google Scholar ›
33
Pena, M. V. T. and Breidbach, C. F. (2021). “On Emergence in Service Platforms: An Application to P2P Lending,” Journal of Business Research, 135, pp.337-347. Google Scholar ›
34
Phelps, J., Nowak, G. and Ferrell, E. (2000). “Privacy Concerns and Consumer Willingness to provide Personal Information,” Journal of Public Policy & Marketing, 19(1), pp.27-41. Google Scholar ›
35
Provost, F., Martens, D. and Murray, A. (2015). “Finding Similar Mobile Consumers with a Privacy-Friendly Geosocial Design,” Information Systems Research, 26(2), pp.243-265. Google Scholar ›
36
Steenackers, A. and Goovaerts, M. J. (1989). “A Credit Scoring Model for Personal Loans,” Insurance: Mathematics and Economics, 8(1), pp.31-34. Google Scholar ›
37
Stepanova, M. and Thomas, L. (2002). “Survival Analysis Methods for Personal Loan Data,” Operations Research, 50(2), pp.277-289. Google Scholar ›
38
Šušteršič, M., Mramor, D. and Zupan, J. (2009). “Consumer Credit Scoring Models with Limited Data,” Expert Systems with Applications, 36(3), pp.4736-4744. Google Scholar ›
39
Thomas, L., Crook, J. and Edelman, D. (2017). Credit Scoring and Its Applications, SIAM. Google Scholar ›
40
Thorne, D. and Anderson, L. (2006). “Managing the Stigma of Personal Bankruptcy,” Sociological Focus, 39(2), pp.77-97. Google Scholar ›
41
Volkwein, J. F. and Szelest, B. P. (1995). “Individual and Campus Characteristics Associated with Student Loan Default,” Research in Higher Education, 36(1), pp.41-72. Google Scholar ›
42
Wei, Y., Yildirim, P., Van den Bulte, C. and Dellarocas, C. (2016). “Credit Scoring with Social Network Data,” Marketing Science, 35(2), pp.234-258. Google Scholar ›
43
World Bank. (2017). World Development Indicators 2017, World Bank. Google Scholar ›

도시 내 이동이 신용을 드러낸다: 지리적 위치 정보 기반 신용 채무불이행 예측