Abstract
Microlending has grown rapidly and now benefits around 250 million people globally, half who would otherwise not have access to credit. Use of social credit systems for microlending risk assessment is most pronounced in Asia, as most Western countries tightly regulate personal information available to lenders. In most of the developing world, geography, social structure, disease, climate and culture have a much stronger influence on credit risk and borrowing than do governmental and corporate systems. In this study, we obtained 784 loan contracts with 3577,912 personal communications and locations. Exploratory analysis found loan default depends on social network structure; graph analysis indicated that those who were likely to default tended to communicate with other likely defaulters. Detailed tests were equivocal, suggesting that social network communication structure provided little additional information to predict default, and may even add noise to the data. Our tests strongly supported the importance of location and proximity to particular sorts of landmarks on the potential for default. Proximity to some landmarks, e.g. city hall, moving companies and train stations, were associated with lower loan default. Others, such as parks, stadiums and bus stations, were correlated with a higher loan default. We restructured our tests based on risk-return versus loan default effect with little change in results.
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Adler, P. S., & Concept, N. (2002). Social captial: Prospets fro a new concept. The Academy of Management Review,27(1), 17–40.
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. Paper presented at the 2nd international symposium on information theory, Akademiai Kiado, Budapest, 1973.
Akaike, H. (1998). A Bayesian analysis of the minimum AIC procedure. Selected papers of Hirotugu Akaike (pp. 275–280). Springer.
Arnaboldi, V., Conti, M., Passarella, A., & Pezzoni, F. (2012). Analysis of ego network structure in online social networks. Paper presented at the privacy, security, risk and trust (PASSAT), 2012 international conference on and 2012 international conference on social computing (SocialCom).
Barabási, A. L. (2005). The origin of bursts and heavy tails in human dynamics. arXiv preprint cond-mat/0505371.
Barabási, A. L. (2007). Network medicine—from obesity to the “diseasome”. Waltham: Mass Medical Soc.
Böhme, R., & Pötzsch, S. (2010). Privacy in online social lending. Paper presented at the AAAI spring symposium: Intelligent information privacy management.
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science,323(5916), 892–895.
Burtch, G., Ghose, A., & Wattal, S. (2014). Cultural differences and geography as determinants of online prosocial lending. MIS Quarterly,38(3), 773–794.
Callaway, D. S., Newman, M. E., Strogatz, S. H., & Watts, D. J. (2000). Network robustness and fragility: Percolation on random graphs. Physical Review Letters,85(25), 5468.
Carr, J., Dickinson, E., McKinnon, S. L., & Chávez, K. R. (2016). Kiva’s flat, flat world: Ten years of microcredit in cyberspace. Globalizations,13(2), 143–157.
Cohen, R., & Havlin, S. (2003). Scale-free networks are ultrasmall. Physical Review Letters,90(5), 058701.
Cook, R. D. (1977). Detection of influential observation in linear regression. Technometrics,19(1), 15–18.
Cook, R. D. (1979). Influential observations in linear regression. Journal of the American Statistical Association,74(365), 169–174.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems,1695(5), 1–9.
Csárdi, G., & Nepusz, T. (2010). igraph reference manual. http://igraph.sourceforge.net/documentation.html. Accessed 20 April.
Cull, R., Demirgüç-Kunt, A., & Morduch, J. (2018). The microfinance business model: Enduring subsidy and modest profit. The World Bank Economic Review,32(2), 221–244.
Daft, R. L., & Robert, H. L. (1986). Organizational information requirements, media richness and structural design. Management Science,32(5), 554–571.
de Nooy, W. (2012). Graph theoretical approaches to social network analysis. In Computational complexity: Theory, techniques, and applications (pp. 2864–2877). Heidelberg: Springer.
Dennis, A. R., Robert, M. F., & Joseph, S. V. (2008). Media, tasks, and communication processes: A theory of media synchronicity. MIS Quarterly,32(3), 575–600.
de Soto, H. (2014). Missing ingredients of globalization. In The future of globalization (pp. 37–51). Abingdon: Routledge.
de Soto, H. (2017). A tale of two civilizations in the era of Facebook and blockchain. Small Business Economics,49(4), 729–739.
Dillon, T. W., & Lending, D. (2010). Will they adopt? Effects of privacy and accuracy. Journal of Computer Information Systems,50(4), 20–29.
Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge: Cambridge University Press.
Ebel, H., Mielsch, L. I., & Bornholdt, S. (2002). Scale-free topology of e-mail networks. Physical Review E,66(3), 035103.
Everett, M., & Borgatti, S. P. (2005). Ego network betweenness. Social Networks,27(1), 31–38.
Fernandes, G. B., & Artes, R. (2016). Spatial dependence in credit risk and its improvement in credit scoring. European Journal of Operational Research,249(2), 517–524.
Godlewski, C. J., Sanditov, B., & Burger-Helmchen, T. (2012). Bank lending networks, experience, reputation, and borrowing costs: Empirical evidence from the French syndicated lending market. Journal of Business Finance & Accounting,39(1), 113–140.
Grodzinsky, F. S., & Tavani, H. T. (2005). P2P networks and the Verizon v. RIAA case: Implications for personal privacy and intellectual property. Ethics and Information Technology,7(4), 243–250.
Hoogeveen, J. G. M. (2002). Income risk, consumption security and the poor. Oxford Development Studies,30(1), 105–121.
Huang, Y. L. (2009). Prediction of contractor default probability using structural models of credit risk: An empirical investigation. Construction Management and Economics,27(6), 581–596.
Hurley, M., & Adebayo, J. (2016). Credit scoring in the era of big data. Yale JL & Tech.,18, 148.
Jalali, M. S., Ashouri, A., Herrera-Restrepo, O., & Zhang, H. (2016). Information diffusion through social networks: The case of an oline petition. Expert Systems with Applications,44, 187–197.
Jones, C., & Volpe, E. H. (2011). Organizational identification: Extending our understanding of social identities through social networks. Journal of Organizational Behavior,32(3), 413–434.
Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. Oxford: OUP USA.
Kiruthika, & Dilsha, M. (2015). A neural network approach for microfinance credit scoring. Journal of Statistics and Management Systems,18(1–2), 121–138.
Lacker, J. M. (2002). The economics of financial privacy: To opt out or opt in? Economic Quarterly-Federal Reserve Bank of Richmond,88(3), 1–16.
Lawrence, E. C., Smith, L. D., & Rhoades, M. (1992). An analysis of default risk in mobile home credit. Journal of Banking & Finance,16, 299–312.
Lee, C. H., & Chiravuri, A. (2019). Dealing with initial success versus failure in crowdfunding market: Serial crowdfunding, changing strategies, and funding performance. Internet Research. https://doi.org/10.1108/INTR-03-2018-0132.
Leskovec, J., & Mcauley, J. J. (2012). Learning to discover social circles in ego networks. Paper presented at the Advances in neural information processing systems.
Leskovec, J., & Sosič, R. (2016). Snap: A general-purpose network analysis and graph-mining library. ACM Transactions on Intelligent Systems and Technology (TIST),8(1), 1.
Lian, S., Cha, T., & Xu, Y. (2019). Enhancing geotargeting with temporal targeting, behavioral targeting and promotion for comprehensive contextual targeting. Decision Support Systems,117, 28–37.
Lucas, R. E. (1976). Econometric policy evaluation: A critique. Paper presented at the Carnegie-Rochester conference series on public policy.
McCord, G. C., & Sachs, J. D. (2015). Physical geography and the history of economic development.
Mellinas, J. P., Nicolau, J. L., & Park, S. (2019). Inconsistent behavior in online consumer reviews: The effects of hotel attribute ratings on location. Tourism Management,71, 421–427.
Meissner, M. (2017). China’s social credit system: A big-data enabled approach to market regulation with broad implications for doing business in China. Mercator Institute for China studies, 24, 1–13.
Mimouni, K. (2017). Currency risk and microcredit interest rates. Emerging Markets Review,31, 80–95.
Morduch, J., Cull, R., & Demirgüç-Kunt, A. (2017). The microfinance business model: Modest profit and enduring subsidy. World Bank Economic Review.
Oh, Y. J., Park, H. S., & Min, Y. (2019). Understanding location-based service application connectedness: Model development and cross-validation. Computers in Human Behavior,94, 82–91.
Onnela, J. P., Saramäki, J., Hyvönen, J., Szabó, G., De Menezes, M. A., Kaski, K., et al. (2007). Analysis of a large-scale weighted network of one-to-one human communication. New Journal of Physics,9(6), 179.
Onnela, J. P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., et al. (2007). Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences,104(18), 7332–7336.
Óskarsdóttir, M., Bravo, C., Sarraute, C., Baesens, B., & Vanthienen, J. (2018). Credit scoring for good: Enhancing financial inclusion with smartphone-based microlending. In the 39th international conference on information systems, San Francisco.
Price, D. J. D. S. (1965). Networks of scientific papers. Science, 149(3683), 510–515.
Qian, X., Kong, D., & Du, L. (2019). Proximity, information, and loan pricing in internal capital markets: Evidence from China. China Economic Review,54, 434–456.
Riggins, F. J., & Weber, D. M. (2017). Information asymmetries and identification bias in P2P social microlending. Information Technology for Development,23(1), 107–126.
Sachs, J. D. (2015). The age of sustainable development. New York: Columbia University Press.
Samoggia, A., & Riedel, B. (2018). Coffee consumption and purchasing behavior review: Insights for further research. Appetite,129, 70–81.
San Pedro, J., Proserpio, D., & Oliver, N. (2015). MobiScore: Towards universal credit scoring from mobile phone data. Paper presented at the international conference on user modeling, adaptation, and personalization.
Sanchez, P., Palm, C., Sachs, J., Denning, G., Flor, R., Harawa, R., et al. (2007). The African millennium villages. Proceedings of the National Academy of Sciences,104(43), 16775–16780.
Scott, J. (2017). Social network analysis. Thousand Oaks: Sage.
Scott, W. R., & Davis, G. F. (2003). Networks in and around organizations. Organizations and Organizing. Pearson Prentice Hall.
Serrano-Cinca, C., Gutiérrez-Nieto, B., & Reyes, N. M. (2016). A social and environmental approach to microfinance credit scoring. Journal of Cleaner Production,112, 3504–3513.
Shi, W. (2015). Internet lending in China: Status quo, potentialrisks and regulatory options. Computer Law & Security Review,31, 793–809.
Strogatz, S. H. (2001). Exploring complex networks. Nature,410(6825), 268.
Tang, S., & Guo, S. (2017). Formal and informal credit markets and rural credit demand in China. Paper presented at the industrial economics system and industrial security engineering (IEIS’2017), 2017 4th international conference on.
Tsarenko, Y., & Rooslani Tojib, D. (2009). Examining customer privacy concerns in dealings with financial institutions. Journal of Consumer Marketing,26(7), 468–476.
Uysal, V. B., Kedia, S., & Panchapagesan, V. (2008). Geography and acquirer returns. Journal of Financial Intermediation,17, 256–275.
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge University Press.
Wei, Y., Yildirim, P., Van den Bulte, C., & Dellarocas, C. (2015). Credit scoring with social network data. Marketing Science,35(2), 234–258.
Wikipedia, Maximum likelihood estimation, https://en.wikipedia.org/wiki/Maximum_likelihood_estimation.
Xia, Y., Chi, K. T., Tam, W. M., Lau, F. C., & Small, M. (2005). Scale-free user-network approach to telephone network traffic analysis. Physical Review E,72(2), 026116.
Xu, J. J., & Chau, M. (2018). Cheap talk? The impact of lender borrower communication on peer-to-peer lending outcomes. Journal of Management Information Systems,35(1), 53–85.
Yan, J., Wang, K., Liu, Y., Xu, K., Kang, L., Chen, X., et al. (2018). Mining social lending motivations for loan project recommendations. Expert Systems with Applications,111, 100–106.
Yunus, M. (1999). The Grameen bank. Scientific American,281(5), 114–119.
Yunus, M. (2007). Banker to the poor. New Delhi: Penguin Books India.
Yunus, M. (2009). Creating a world without poverty: Social business and the future of capitalism. New York: Public Affairs.
Zhang, K., & Zhang, F. (2016). Report on the construction of the social credit system in China’s Special Economic Zones. Annual report on the development of China’s Special Economic Zones (2016) (pp. 153–171). Springer.
Zhang, Y., Jia, H., Diao, Y., Hai, M., & Li, H. (2016). Research on credit scoring by fusing social media information in online peer-to-peer lending. Procedia Computer Science,91, 168–174.
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We would like to thank the editor and anonymous reviewers for their comments, which have greatly improved our paper. This study supported by the Fundamental Research Funds for the Central Universities of No. (BX180604).
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Mou, J., Christopher Westland, J., Phan, T.Q. et al. Microlending on mobile social credit platforms: an exploratory study using Philippine loan contracts. Electron Commer Res 20, 173–196 (2020). https://doi.org/10.1007/s10660-019-09391-2
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DOI: https://doi.org/10.1007/s10660-019-09391-2