Skip to main content

Advertisement

Log in

Microlending on mobile social credit platforms: an exploratory study using Philippine loan contracts

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Adapted from Cull et al. [17]

Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. We have accomplished graph construction data cleaning and plotting using the igraph package in R ([15]; [16]). Graph statistics for centralization, degree, modularity, community structure, diameter, connectedness and other statistics were computed using SNAP [41].

References

  1. Adler, P. S., & Concept, N. (2002). Social captial: Prospets fro a new concept. The Academy of Management Review,27(1), 17–40.

    Article  Google Scholar 

  2. 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.

  3. Akaike, H. (1998). A Bayesian analysis of the minimum AIC procedure. Selected papers of Hirotugu Akaike (pp. 275–280). Springer.

  4. 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).

  5. Barabási, A. L. (2005). The origin of bursts and heavy tails in human dynamics. arXiv preprint cond-mat/0505371.

  6. Barabási, A. L. (2007). Network medicine—from obesity to the “diseasome”. Waltham: Mass Medical Soc.

    Book  Google Scholar 

  7. Böhme, R., & Pötzsch, S. (2010). Privacy in online social lending. Paper presented at the AAAI spring symposium: Intelligent information privacy management.

  8. Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science,323(5916), 892–895.

    Article  Google Scholar 

  9. Burtch, G., Ghose, A., & Wattal, S. (2014). Cultural differences and geography as determinants of online prosocial lending. MIS Quarterly,38(3), 773–794.

    Article  Google Scholar 

  10. 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.

    Article  Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. Cohen, R., & Havlin, S. (2003). Scale-free networks are ultrasmall. Physical Review Letters,90(5), 058701.

    Article  Google Scholar 

  13. Cook, R. D. (1977). Detection of influential observation in linear regression. Technometrics,19(1), 15–18.

    Google Scholar 

  14. Cook, R. D. (1979). Influential observations in linear regression. Journal of the American Statistical Association,74(365), 169–174.

    Article  Google Scholar 

  15. Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems,1695(5), 1–9.

    Google Scholar 

  16. Csárdi, G., & Nepusz, T. (2010). igraph reference manual. http://igraph.sourceforge.net/documentation.html. Accessed 20 April.

  17. 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.

    Article  Google Scholar 

  18. Daft, R. L., & Robert, H. L. (1986). Organizational information requirements, media richness and structural design. Management Science,32(5), 554–571.

    Article  Google Scholar 

  19. de Nooy, W. (2012). Graph theoretical approaches to social network analysis. In Computational complexity: Theory, techniques, and applications (pp. 2864–2877). Heidelberg: Springer.

  20. 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.

    Article  Google Scholar 

  21. de Soto, H. (2014). Missing ingredients of globalization. In The future of globalization (pp. 37–51). Abingdon: Routledge.

  22. de Soto, H. (2017). A tale of two civilizations in the era of Facebook and blockchain. Small Business Economics,49(4), 729–739.

    Article  Google Scholar 

  23. Dillon, T. W., & Lending, D. (2010). Will they adopt? Effects of privacy and accuracy. Journal of Computer Information Systems,50(4), 20–29.

    Google Scholar 

  24. Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  25. Ebel, H., Mielsch, L. I., & Bornholdt, S. (2002). Scale-free topology of e-mail networks. Physical Review E,66(3), 035103.

    Article  Google Scholar 

  26. Everett, M., & Borgatti, S. P. (2005). Ego network betweenness. Social Networks,27(1), 31–38.

    Article  Google Scholar 

  27. 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.

    Article  Google Scholar 

  28. 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.

    Article  Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. Hoogeveen, J. G. M. (2002). Income risk, consumption security and the poor. Oxford Development Studies,30(1), 105–121.

    Article  Google Scholar 

  31. 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.

    Article  Google Scholar 

  32. Hurley, M., & Adebayo, J. (2016). Credit scoring in the era of big data. Yale JL & Tech.,18, 148.

    Google Scholar 

  33. 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.

    Article  Google Scholar 

  34. 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.

    Article  Google Scholar 

  35. Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. Oxford: OUP USA.

    Google Scholar 

  36. Kiruthika, & Dilsha, M. (2015). A neural network approach for microfinance credit scoring. Journal of Statistics and Management Systems,18(1–2), 121–138.

    Article  Google Scholar 

  37. 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.

    Google Scholar 

  38. 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.

    Article  Google Scholar 

  39. 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.

    Article  Google Scholar 

  40. Leskovec, J., & Mcauley, J. J. (2012). Learning to discover social circles in ego networks. Paper presented at the Advances in neural information processing systems.

  41. 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.

    Article  Google Scholar 

  42. 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.

    Article  Google Scholar 

  43. Lucas, R. E. (1976). Econometric policy evaluation: A critique. Paper presented at the Carnegie-Rochester conference series on public policy.

  44. McCord, G. C., & Sachs, J. D. (2015). Physical geography and the history of economic development.

  45. 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.

    Article  Google Scholar 

  46. 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.

  47. Mimouni, K. (2017). Currency risk and microcredit interest rates. Emerging Markets Review,31, 80–95.

    Article  Google Scholar 

  48. Morduch, J., Cull, R., & Demirgüç-Kunt, A. (2017). The microfinance business model: Modest profit and enduring subsidy. World Bank Economic Review.

  49. 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.

    Article  Google Scholar 

  50. 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.

    Article  Google Scholar 

  51. 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.

    Article  Google Scholar 

  52. 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.

  53. Price, D. J. D. S. (1965). Networks of scientific papers. Science, 149(3683), 510–515.

    Article  Google Scholar 

  54. 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.

    Article  Google Scholar 

  55. Riggins, F. J., & Weber, D. M. (2017). Information asymmetries and identification bias in P2P social microlending. Information Technology for Development,23(1), 107–126.

    Article  Google Scholar 

  56. Sachs, J. D. (2015). The age of sustainable development. New York: Columbia University Press.

    Book  Google Scholar 

  57. Samoggia, A., & Riedel, B. (2018). Coffee consumption and purchasing behavior review: Insights for further research. Appetite,129, 70–81.

    Article  Google Scholar 

  58. 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.

  59. 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.

    Article  Google Scholar 

  60. Scott, J. (2017). Social network analysis. Thousand Oaks: Sage.

    Book  Google Scholar 

  61. Scott, W. R., & Davis, G. F. (2003). Networks in and around organizations. Organizations and Organizing. Pearson Prentice Hall.

  62. 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.

    Article  Google Scholar 

  63. Shi, W. (2015). Internet lending in China: Status quo, potentialrisks and regulatory options. Computer Law & Security Review,31, 793–809.

    Article  Google Scholar 

  64. Strogatz, S. H. (2001). Exploring complex networks. Nature,410(6825), 268.

    Article  Google Scholar 

  65. 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.

  66. Tsarenko, Y., & Rooslani Tojib, D. (2009). Examining customer privacy concerns in dealings with financial institutions. Journal of Consumer Marketing,26(7), 468–476.

    Article  Google Scholar 

  67. Uysal, V. B., Kedia, S., & Panchapagesan, V. (2008). Geography and acquirer returns. Journal of Financial Intermediation,17, 256–275.

    Article  Google Scholar 

  68. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge University Press.

    Book  Google Scholar 

  69. Wei, Y., Yildirim, P., Van den Bulte, C., & Dellarocas, C. (2015). Credit scoring with social network data. Marketing Science,35(2), 234–258.

    Article  Google Scholar 

  70. Wikipedia, Maximum likelihood estimation, https://en.wikipedia.org/wiki/Maximum_likelihood_estimation.

  71. 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.

    Article  Google Scholar 

  72. 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.

    Article  Google Scholar 

  73. 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.

    Article  Google Scholar 

  74. Yunus, M. (1999). The Grameen bank. Scientific American,281(5), 114–119.

    Article  Google Scholar 

  75. Yunus, M. (2007). Banker to the poor. New Delhi: Penguin Books India.

    Google Scholar 

  76. Yunus, M. (2009). Creating a world without poverty: Social business and the future of capitalism. New York: Public Affairs.

    Google Scholar 

  77. 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.

  78. 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.

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jian Mou.

Ethics declarations

Conflict of interest

There is no conflict of interest for this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-019-09391-2

Keywords

Navigation