Abstract
Credit is a widely used tool to finance personal and corporate projects. The risk of default has motivated lenders to use a credit scoring system, which helps them make more efficient decisions about whom to extend credit. Credit scores serve as a financial user model, and have been traditionally computed from the user’s past financial history. As a result, people without any prior financial history might be excluded from the credit system. In this paper we present MobiScore, an approach to build a model of the user’s financial risk from mobile phone usage data, which previous work has shown to convey information about e.g. personality and socioeconomic status. MobiScore could replace traditional credit scores when no financial history is available, providing credit access to currently excluded population sectors, or be used as a complementary source of information to improve traditional finance-based scores. We validate the proposed approach using real data from a telecommunications operator and a financial institution in a Latin American country, resulting in an accurate model of default comparable to traditional credit scoring techniques.
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References
Abdou, H.A., Pointon, J.: Credit scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent Systems in Accounting, Finance and Management 18(2–3), 59–88 (2011)
Agarwal, S., Chomsisengphet, S., Liu, C.: Consumer bankruptcy and default: The role of individual social capital. Journal of Economic Psychology 32(4), 632–650 (2011)
Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., Vanthienen, J.: Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society 54(6), 627–635 (2003)
Candia, J., González, M.C., Wang, P., Schoenharl, T., Madey, G., Barabási, A.-L.: Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical 41(22), 224015 (2008)
de Montjoye, Y.-A., Quoidbach, J., Robic, F., Pentland, A.S.: Predicting personality using novel mobile phone-based metrics. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 48–55. Springer, Heidelberg (2013)
de Oliveira, R., Karatzoglou, A., Concejero Cerezo, P., Armenta Lopez de Vicuña, A., Oliver, N.: Towards a psychographic user model from mobile phone usage. In: CHI 2011 Extended Abstracts on Human Factors in Computing Systems, CHI EA 2011, New York, NY, USA, pp. 2191–2196. ACM (2011)
Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences 106(36), 15274–15278 (2009)
Frias-Martinez, V., Frias-Martinez, E., Oliver, N.: A gender-centric analysis of calling behavior in a developing economy using call detail records. In: Proceedings of the AAAI Spring Symposium: Artificial Intelligence for Development (2010)
Gathergood, J.: Self-control, financial literacy and consumer over-indebtedness. Journal of Economic Psychology 33(3), 590–602 (2012)
Grable, J.E., Joo, S.-H.: Environmental and biophysical factors associated with financial risk tolerance. Journal of Financial Counseling and Planning 15(1) (2004)
Henegar, J.M., Archuleta, K., Grable, J., Britt, S., Anderson, N., Dale, A.: Credit card behavior as a function of impulsivity and mothers socialization factors. Journal of Financial Counseling and Planning 24(2), 37–49 (2013)
Meier, S., Sprenger, C.: Impatience and credit behavior: Evidence from a field experiment. Social Science Research Network Working Paper Series (2007)
Mester, L.J.: Whats the point of credit scoring? Business review 3, 3–16 (1997)
Montoliu, R., Gatica-Perez, D.: Discovering human places of interest from multimodal mobile phone data. In: Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia, p. 12. ACM (2010)
Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., Barabási, A.-L.: Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences 104(18), 7332–7336 (2007)
Singh, V.K., Freeman, L., Lepri, B., Pentland, A.S.: Predicting spending behavior using socio-mobile features. In: 2013 International Conference on Social Computing (SocialCom), pp. 174–179. IEEE (2013)
Soto, V., Frias-Martinez, V., Virseda, J., Frias-Martinez, E.: Prediction of socioeconomic levels using cell phone records. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 377–388. Springer, Heidelberg (2011)
Wang, G., Hao, J., Ma, J., Jiang, H.: A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications 38(1), 223–230 (2011)
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Pedro, J.S., Proserpio, D., Oliver, N. (2015). MobiScore: Towards Universal Credit Scoring from Mobile Phone Data. In: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (eds) User Modeling, Adaptation and Personalization. UMAP 2015. Lecture Notes in Computer Science(), vol 9146. Springer, Cham. https://doi.org/10.1007/978-3-319-20267-9_16
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DOI: https://doi.org/10.1007/978-3-319-20267-9_16
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