Abstract
SMEs in China always face financing constraints and hardly obtain bank loans under unsound financing system due to the information asymmetry, while thousands of SMEs have contributed greatly to Chinese economic development in the last decades. Credit reporting has been verified to be an effective way to lower information asymmetry. However, existed credit reporting systems for SMEs can not meet the development of SMEs and provide enough information to the financial institutions in China. This paper introduces an active and dynamic credit reporting framework based on Big data and Blockchain for SMEs. The framework is composed of five modules, including credit data acquisition, authentication, evaluation, reporting, and interaction. And it features in capturing diversified data online, conducting evaluation and analysis in real time, generating online credit reports for users automatically, and providing an effective way for different entities to interact. A case study from a real credit evaluation company is also proposed finally to show the proposed framework.
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References
Altman EI, Sabato G (2007) Modelling credit risk for SMEs: evidence from the U.S. market. Abacus 43(3):332–357
Angilella S, Mazzù S (2015) The financing of innovative SMEs: a multicriteria credit rating model. Eur J Oper Res 244(2):540–554
Beck T, Demirgüç-Kunt A, Maksimovic V (2005) Financial and legal constraints to growth: does firm size matter? J Financ 60(1):137–177
Beck T, Demirgüç-Kunt A, Laeven L, Maksimovic V (2006) The determinants of financing obstacles. J IntMoney Financ 25(6):932–952
Berger AN, Frame WS (2007) Small business credit scoring and credit availability. J Small Bus Manag 45(1):5–22
Berger AN, Udell GF (2006) A more complete conceptual framework for SME finance [J]. J Bank Financ 30(11):2945–2966
Berlin M, Mester LJ (1998) On the profitability and cost of relationship lending. J Bank Financ 22(6):873–897
Blanco A, Pino-MejíAs R, Lara J et al (2013) Credit scoringmodels for the microfinance industry using neural networks: evidence from Peru [J]. Expert Syst Appl 40(1):356–364
Brown M, Zehnder C (2007) Credit reporting, relationship banking, and loan repayment. J Money Credit Bank 39(8):1883–1918
Cao Y, Xiong S (2014) A sustainable financing credit reporting model for China’s small-and medium-sized enterprises [J]. Math Probl Eng 2014:1–5
Casin P (2018) Categorical multiblock linear discriminant analysis. J Appl Stat 45(8):1396–1409
Diamond DW, Verrecchia RE (1991) Disclosure, liquidity, and the cost of capital. J Financ 46(4):1325–1359
Frame WS, Srinivasan A, Woosley L (2001) The effect of credit scoring on small-business lending. J Money Credit Bank 33(3):813
Godbillon-Camus B, Godlewski CJ (2005) Credit risk management in banks: hard information, soft information and manipulation working paper. Available at SSRN: https://ssrn.com/abstract=882027
Jaffee DM, Russell T (1976) Imperfect information, uncertainty, and credit rationing [J]. Q J Econ 90:651–666
Jappelli T, Pagano M, Bianco M (2005) Courts and banks: effects of judicial enforcement on credit markets. J Money, Credit, Bank 37(2):223–244
Kallberg JG, Udell GF (2003) The value of private sector business credit information sharing: the US case. J Bank Financ 27(3):449–469
Khan N, Yaqoob I, Hashem IAT, Inayat Z, Ali WKM, Alam M, Shiraz M, Gani A (2014) Big data: survey, technologies, opportunities, and challenges. Sci World J 2014:1–18
Kou G, Peng YI et al (2005) Classifying credit card accounts for business intelligence and decision making: a multiple-criteria quadratic programming approach. Int J Inf Technol Decis Mak 4(4):581–599
Kshetri N (2016) Big data’s role in expanding access to financial services in China [J]. Int J Inf Manag 36(3):297–308
Li K, Niskanen J, Kolehmainen M et al (2016) Financial innovation: credit default hybrid model for SME lending [J]. Expert Syst Appl 1(61):343–355
Love I, Mylenko N(2003) Credit reporting and financing constraints. World Bank policy research working paper no. 3142. Available at SSRN: https://ssrn.com/abstract=610320
Mittal S, Gupta P, Jain K (2011) Neural network credit scoring model for micro enterprise financing in India. Qualitative Research in Financial Markets 3(3):224–242
Sohn SY, Kim DH, Yoon JH (2016) Technology credit scoring model with fuzzy logistic regression. Appl Soft Comput 43:150–158
Sousa MR, Gama J, Brandão E (2016) A new dynamic modeling framework for credit risk assessment [J]. Expert Syst Appl 3(45):341–351
SwanM (2015) Blockchain thinking : the brain as a decentralized autonomous corporation [commentary]. IEEE Technol Soc Mag 34(4):41–52
Wang K, Liu Y et al (2016) A dynamic assignment scheduling algorithmfor big data streamprocessing inmobile internet services. Pers Ubiquit Comput 20(3):373–383
Yli-Huumo J, Ko D, Choi S, Park S, Smolander K, Song H (2016) Where is current research on Blockchain technology?—a systematic Review. PLoS One 11(10):e0163477
Yoshino N, Taghizadeh-Hesary F (2015) Analysis of credit ratings for small and medium-sized enterprises: evidence from Asia [J]. Asian Dev Rev 32(2):18–37
Yuan GX, Wang H (2019) The general dynamic risk assessment for the enterprise by the hologram approach in financial technology. International Journal of Financial Engineering 06(01):1950001
Yuan GX, Wang H, Zeng T, Wu T, Gao S, Ma H (2019) The Dynamical Mechanism for SMEs Evolution Under the Hologram Approach. Available at SSRN: https://ssrn.com/abstract=3325013 or https://doi.org/10.2139/ssrn.3325013
Zhang Q, Wang J, Lu A, Wang S, Ma J (2018) An improved SMO algorithm for financial credit risk assessment – evidence from China’s banking. Neurocomputing 272:314–325
Acknowledgement
This research is supported by the National Natural Science Foundation of China (Grant No. 71772015), Beijing Social Science Foundation (Grant No.17GLB016). We express sincere appreciation for the kind supports from Dr. George Yuan.
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Sun, Y., Zeng, X., Cui, X. et al. An active and dynamic credit reporting system for SMEs in China. Pers Ubiquit Comput 25, 989–1000 (2021). https://doi.org/10.1007/s00779-019-01275-4
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DOI: https://doi.org/10.1007/s00779-019-01275-4