What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance

References

• Banasik, J., Crook, J., & Thomas, L. (2003). Sample selection bias in credit scoring models. Journal of the Operational Research Society, 54(8), 822–832.
   Web of Science ®Google Scholar
• Copestake, J. (2007). Mainstreaming microfinance: social performance management or mission drift? World Development, 35(10), 1721–1738.
   Web of Science ®Google Scholar
• Domingos, P. (2005). Mining social networks for viral marketing. IEEE Intelligent Systems, 20(1), 80–82.
   Web of Science ®Google Scholar
• Emel, A. B., Oral, M., Reisman, A., & Yolalan, R. (2003). A credit scoring approach for the commercial banking sector. Socio-Economic Planning Sciences, 37(2), 103–123.
   Google Scholar
• Facebook Inc. (2014). Authorization and authentication based on an individual’s social network. Patent.
   Google Scholar
• Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., & Lin, C.-J. (2008). Liblinear: A library for large linear classification. The Journal of Machine Learning Research, 9, 1871–1874.
   Web of Science ®Google Scholar
• Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874.
   Web of Science ®Google Scholar
• Goel, S., Hofman, J. M., & Sirer, M. I. (2012). Who does what on the web: A large-scale study of browsing behavior. ICWSM.
   Google Scholar
• Hand, D. J., Sohn, S. Y., & Kim, Y. (2005). Optimal bipartite scorecards. Expert Systems with Applications, 29(3), 684–690.
   Web of Science ®Google Scholar
• Hermes, N., & Lensink, R. (2007). The empirics of microfinance: what do we know? The Economic Journal, 117(517), F1–F10.
   Web of Science ®Google Scholar
• Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 21(2), 256–276.
   Web of Science ®Google Scholar
• Hu, J., Zeng, H. J., Li, H., Niu, C., & Chen, Z. (2007). Demographic prediction based on user’s browsing behavior. In Proceedings of the 16th international conference on World Wide Web (pp. 151–160). New York City, NY: ACM.
   Google Scholar
• Junqué de Fortuny, E., Martens, D., & Provost, F. (2013). Wallenius Naive Bayes (Technical Report 2451/33545). New York, NY: New York University.
   Google Scholar
• Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805.
   PubMed Web of Science ®Google Scholar
• Lu, Q. & Getoor, L. (2003). Link-based classification. In ICML, 3, 496–503.
   Google Scholar
• Martens, D., Baesens, B., Van Gestel, T., & Vanthienen, J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. European Journal of Operational Research, 183(3), 1466–1476.
   Web of Science ®Google Scholar
• Martens, D., & Provost, F. (2011). Pseudo-social network targeting from consumer transaction data (Technical Report CEDER-11-05). New York, NY: New York University.
   Google Scholar
• Martens, D., & Provost, F. (2014). Explaining data-driven document classifications. MIS Quarterly, 38(1), 73–100.
   Web of Science ®Google Scholar
• Min, J. H., & Jeong, C. (2009). A binary classification method for bankruptcy prediction. Expert Systems with Applications, 36(3), 5256–5263.
   Web of Science ®Google Scholar
• Moeyersoms, J., & Martens, D. (2015). Including high-cardinality attributes in predictive models: A case study in churn prediction in the energy sector. Decision Support Systems, 72, 72–81.
   Web of Science ®Google Scholar
• Morduch, J. (1999). The microfinance promise. Journal of Economic Literature, 37(4), 1569–1614.
   Web of Science ®Google Scholar
• Provost, F., Martens, D., & Murray, A. (2015). Finding similar mobile consumers with a privacy-friendly geo-social design. Information Systems Research. In Press
   Google Scholar
• Putnam, R. D. (1995). Bowling alone: America’s declining social capital. Journal of Democracy, 6(1), 65–78.
   Google Scholar
• Raeder, T., Stitelman, O., Dalessandro, B., Perlich, C., & Provost, F. (2012). Design principles of massive, robust prediction systems. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1357–1365). New York, NY: ACM.
   Google Scholar
• Schreiner, M. (2000). Credit scoring for microfinance: Can it work? Journal of Microfinance/ESR Review, 2(2), 105–118.
   Google Scholar
• Schreiner, M. (2003). Scoring: the next breakthrough in microcredit. Consultative group to assist the poorest (CGAP).
   Google Scholar
• Sharma, M., & Zeller, M. (1997). Repayment performance in group-based credit programs in Bangladesh: An empirical analysis. World Development, 25(10), 1731–1742.
   Web of Science ®Google Scholar
• Stankova, M., Martens, D., & Provost, F. (2015). Classification over bipartite graphs through projection (Technical Report 2015|001). Antwerp University.
   Google Scholar
• Ugander, J., Karrer, B., Backstrom, L., & Marlow, C. (2011). The anatomy of the facebook social graph. arXiv preprint arXiv:1111.4503.
   Google Scholar
• Van Gestel, T., Baesens, B., & Martens, D. (2015). Predictive analytics: Techniques and applications in credit risk modelling. Oxford: Oxford University Press.
   Google Scholar
• Van Gool, J., Verbeke, W., Sercu, P., & Baesens, B. (2012). Credit scoring for microfinance: is it worth it? International Journal of Finance & Economics, 17(2), 103–123.
   Web of Science ®Google Scholar
• Verbeke, W., Martens, D., & Baesens, B. (2014). Social network analysis for customer churn prediction. Applied Soft Computing, 14 Part C(0), 431–446.
   Web of Science ®Google Scholar
• Weber, I., Garimella, V. R. K., & Borra, E. (2013). Inferring audience partisanship for youtube videos. In Proceedings of the 22nd international conference on World Wide Web companion (pp. 43–44). International World Wide Web Conferences Steering Committee.
   Google Scholar
• Wei, Y., Yildirim, P., Van den Bulte, C., & Dellarocas, C. (2014). Credit scoring with social network data. Retrieved from SSRN 2475265.
   Google Scholar
• Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., ... Steinberg, D. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.
   Web of Science ®Google Scholar
• Zeller, M. (1998). Determinants of repayment performance in credit groups: The role of program design, intragroup risk pooling, and social cohesion. Economic Development and Cultural Change, 46(3), 599–620.
   Web of Science ®Google Scholar