Appendix A. Mybank
China has been at the forefront of fintech development and is the largest fintech market in the world, with virtual banking being one of its wide-reaching features. Enabled by digital technology and big data, China’s big four tech players—Alibaba, Baidu, Tencent, and JD—have made incursions into financial services. During 2014–16, China’s banking regulator issued 11 new privately-owned banking licenses, including to MYbank. By aiming to bring private money into the Chinese financial services sector, three banks (MYbank, WeBank, and XW Bank) have set up a wider reach to millions of small and medium-size enterprises (SMEs).
Headquartered in Hangzhou, Zhejiang province (in southeast China), MYbank was founded in 2015 by Alibaba’s affiliate firm, Ant Group, through a 30 percent stake in a joint venture comprising a group of private firms. MYbank uses big data, machine learning, and the associated flexible risk management approach to offer credit to SMEs and manage risks. MYbank has about 20 million SME borrowers, about 80 percent of whom have never borrowed from banks in the past, and keeps its nonperforming loan (NPL) ratio at about 1 percent.
MYbank provides financing for SMEs and individuals in urban and rural areas. Its data/cloud-based model with no physical branches makes its operational costs much lower than the traditional brick-and-mortar banking model. By harnessing its credit-profiling techniques driven by big data analytics (e.g., e-commerce and cash flow), MYbank manages to approve small loans for individuals and businesses around the clock 24 hours a day, seven days a week, providing capital and liquidity for the masses in China.
In 2019, MYbank’s total assets were RMB 139.6 billion ($20 billion), the average loan size was RMB 31,000 ($4,500), the accumulated SMEs served reached 20.9 million, the capital adequacy ratio was 16.4 percent, and the NPL ratio was 1.3 percent.
Appendix B. Algorithms
Abdulsaleh, A. M., and A. C. Worthington. 2013. Small and Medium-Sized Enterprises Financing: A Review of Literature. International Journal of Business and Management 8:36–54.
Agarwal, S., S. Alok, P. Ghosh, and S. Gupta. 2019. Financial Inclusion and Alternate Credit Scoring for the Millennials: Role of Big Data and Machine Learning in Fintech. SSRN Scholarly Paper, Social Science Research Network, Rochester, NY.
Ahmed, M. S. I., and P. R. Rajaleximi. 2019. An Empirical Study on Credit Scoring and Credit Scorecard for Financial Institutions. International Journal of Advanced Research in Computer Engineering & Technology 8:275–79.
Bazarbash, M. 2019. Fintech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk. IMF Working Papers 19, International Monetary Fund, Washington, DC.
Berg, T., V. Burg, A. Gombović, and M. Puri. 2019. On the Rise of FinTechs: Credit Scoring Using Digital Footprints. Review of Financial Studies.
Berger, A. N., W. S. Frame, and V. Ioannidou. 2016. Reexamining the Empirical Relation between Loan Risk and Collateral: The Roles of Collateral Liquidity and Types. Journal of Financial Intermediation 26:28–46.
Berger, A. N., and G. F. Udell. 2002. Small Business Credit Availability and Relationship Lending: The Importance of Bank Organisational Structure. Economic Journal 112:32–53.
Berger, A. N., and G. F. Udell. 2006. A More Complete Conceptual Framework for SME Finance. Journal of Banking & Finance 30:2945–66.
Besanko, D., and A. V. Thakor. 1987a. Collateral and Rationing: Sorting Equilibria in Monopolistic and Competitive Credit Markets. International Economic Review 28:671–89.
Besanko, D., and A. V. Thakor. 1987b. Competitive Equilibrium in the Credit Market under Asymmetric Information. Journal of Economic Theory 42:167–82.
Butaru, F., Q. Chen, B. Clark, S. Das, A. W. Lo, and A. Siddique. 2016. Risk and Risk Management in the Credit Card Industry. Journal of Banking & Finance 72:218–39.
Cerqueiro, G., S. Ongena, and K. Roszbach. 2016. Collateralization, Bank Loan Rates, and Monitoring. Journal of Finance 71:1295–1322.
Cornée, S. 2019. The Relevance of Soft Information for Predicting Small Business Credit Default: Evidence from a Social Bank. Journal of Small Business Management 57:699–719.
Demirgüç-Kunt, A., and L. Klapper. 2013. Measuring Financial Inclusion: Explaining Variation in Use of Financial Services across and within Countries. Brookings Papers on Economic Activity 2013:279–340.
Demirgüç-Kunt, A., L. Klapper, D. Singer, S. Ansar, and J. Hess. 2018. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank.
Freel, M., S. Carter, S. Tagg, and C. Mason. 2012. The Latent Demand for Bank Debt: Characterizing “Discouraged Borrowers.” Small Business Economics 38:399–418.
Frost, J., L. Gambacorta, Y. Huang, H. S. Shin, and P. Zbinden. 2019. BigTech and the Changing Structure of Financial Intermediation. BIS Working Papers 779, Bank for International Settlements, Basel, Switzerland.
Fuster, A., P. Goldsmith-Pinkham, T. Ramadorai, and A. Walther. 2020. Predictably Unequal? The Effects of Machine Learning on Credit Markets. SSRN Scholarly Paper, Social Science Research Network, Rochester, NY.
Gambacorta, L., Y. Huang, H. Qiu, and J. Wang. 2019. How Do Machine Learning and Non-Traditional Data Affect Credit Scoring? New Evidence from a Chinese Fintech Firm. BIS Working Papers 834:24, Bank for International Settlements, Basel, Switzerland.
Hopper, M. A., and E. M. Lewis. 2002. Behavioural Scoring and Adaptive Control Systems. In L. C. Thomas, J. N. Crook, and D. B. Edelman (eds.), Credit Scoring and Credit Control. Oxford University Press.
Jagtiani, J., and C. Lemieux. 2019. The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform. Financial Management 48:1009–29.
Khandani, A. E., A. J. Kim, and A. W. Lo. 2010. Consumer Credit-Risk Models via Machine-Learning Algorithms. Journal of Banking & Finance 34:2767–87.
Kithinji, A. M. 2010. Credit Risk Management and Profitability of Commercial Banks in Kenya. School of Business, University of Nairobi, Kenya.
Miller, M., and D. Rojas. 2004. Improving Access to Credit for SMEs: An Empirical Analysis of the Viability of Pooled Data SME Credit Scoring Models in Brazil, Colombia & Mexico. Working Paper 22, World Bank, Washington, DC.
Stiglitz, J. E., and A. Weiss. 1981. Credit Rationing in Markets with Imperfect Information. American Economic Review 71:393–410.
Thomas, L. C., R. W. Oliver, and D. J. Hand. 2005. A Survey of the Issues in Consumer Credit Modelling Research. Journal of the Operational Research Society 56:1006–15.
Vos, E., A. J.-Y. Yeh, S. Carter, and S. Tagg. 2007. The Happy Story of Small Business Financing. Journal of Banking & Finance 31:2648–72.
This paper is the outcome of a joint research project by the Asia Pacific Department of the IMF and the Institute of Digital Finance at the Peking University (IDF/PKU), led by Yiping Huang and Longmei Zhang. Yiping Huang is a professor and the Director of IDF at PKU. Zhenhua Li is the Executive Dean of the Research Institute of the Ant Group (Ant). Han Qiu and Xue Wang are PhD scholars at IDF/PKU. Xue Wang was an economist at the IMF Beijing office when the paper was written.
The authors would like to thank Kenneth Henry Kang and Helge Berger for detailed and helpful comments on the paper, and Dong He for suggestions. They are also grateful to Shu Chen, Fang Wang, Yongguo Li, Zhiyun Cheng, Jinyan Huang, Yiteng Zhai, Guangyao Zhu, Yanming Fang, Xiaodong Sun, Xin Li, Zhengjun Nie, Liang Guo, Ting Xu, Peng Liu, and Li Ma for providing data and logistic support. Neither Ant nor any of its employees asserted any influence on the analyses and conclusions.
Grameen Bank is a microfinance organization and community development bank founded in Bangladesh. It makes small loans to impoverished individuals without requiring collateral. Grameen Bank originated in 1976, through the work of Professor Muhammad Yunus, who launched a research project to study how to design a credit delivery system to provide banking services to the rural poor. As of November 2019, it had 9.6 million members, 97 percent of whom were women. With 2,568 branches, Grameen Bank provides services in 81,678 villages, covering more than 93 percent of the total villages in Bangladesh. (http://www.grameen.com/introduction/Grameen)
Loan forbearance and restructuring are resource-intensive and time consuming for traditional banks. Reflecting the small size of SME loans, the unit costs of providing financing services to SMEs are typically high, which deters traditional banks from reaching out proactively to SMEs.
Appendix A provides more information on MYbank.
China Small and Micro Enterprises Financial Services Report 2018, accessed June 5, 2020, http://www.gov.cn/xinwen/2019-06/25/5402948/files/f59aaafc00da4c848a322ac89fdec1e5.pdf.
Lending rates for MYbank are from the official website: https://render.mybank.cn/p/f/fd-j9fi9ern/index.html. The composite annual lending rate in the Wenzhou informal market was 15.66 percent in June 2020 (see http://www.wzpfi.gov.cn/).
Due to business confidentiality, this data set is not available to the public for cross-checking.
Appendix B provides a more detailed elaboration of the formulas and algorithms.
The results are robust to alternative subsample periods.
There is no official government guideline on city classification. The tiering system adopted here is widely used in the media, reflecting a confluence of factors, such as economic development, population size, and administrative hierarchy. Tier-1 cities represent the most densely populated and developed urban areas in China, while Tier-4 cities are small and less developed.
One caveat of our analysis is that traditional banks may have other soft information about borrowers and expert judgment that is not reflected in the data. In addition, our study focuses on uncollateralized loans, while traditional banks are more experienced in assessing collateral quality and issuing collateralized loans.