Asia-Pacific Journal of Business Venturing and Entrepreneurship (벤처창업연구)

The Korean Society of Business Venturing (한국벤처창업학회)
권호 표지

Research on the Application Methods of Big Data within SME Financing: Big data from Trading-area

소상공인의 자금공급 확대를 위한 빅데이터 활용 방안연구

  • Lee, Ju Hee (The Graduate School of Venture, Hoseo University) ;
  • Dong, Hak Lim (The Graduate School of Venture, Hoseo University)
  • Received : 2018.05.11
  • Accepted : 2018.06.26
  • Published : 2018.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

According to statistics, it is shown that domestic SMEs rely on bank loans for the majority of fund procurement. From financial information shortage (Thin file) that does not provide information necessary for credit evaluation from banks such as financial statements. In order to overcome these problems, recently, in alternative finance such as P2P, using differentiated information such as demographics, trading information and the like utilizing Fintech instead of existing financial information, small funds A new credit evaluation method has been expanding to provide SMEs with small amounts of money. In this paradigm of environmental change, in this research, credit evaluation which can expand fund supply to SMEs by utilizing big data based on trade area information such as sales fluctuation, location conditions etc. In this research, we try to find such a solution. By analyzing empirically the big data generated in the trade area, we verify the effectiveness as a credit evaluation factor and try to derive the main parameters necessary for the business performance evaluation of the founder of SMEs. In this research, for 17,116 material businesses in Seoul City that operate the service industry from 2009 to February 2018, we collect trade area information generated for each business location from Big Data specialized company NICE Zini Data Co., Ltd.. We collected and analyzed the data on the locations and commercial areas of the facilities that were difficult to obtain from SMEs and analyzed the data that affected the Corporate financial Distress. It is possible to refer to the variable of the existing unused big data and to confirm the possibility of utilizing it for efficient financial support for SMEs, This is to ensure that commercial lenders, even in general commercial banks, are made to be more prominent in one sector of the financing of SMEs. In this research, it is not the traditional financial information about raising fund of SMEs who have basically the problem of information asymmetry, but a trade area analysis variable is derived, and this variable is evaluated by credit evaluation There is differentiation of research in that it verified through analysis of big data from Trading-area whether or not there is an effect on.

통계에 의하면 국내 중소기업들은 자금조달의 대부분을 은행 대출에 의존하고 있는 것으로 나타나고 있다. 그러나 담보가 없고 금융거래 이력도 부족한 소상공인들은 은행으로부터 대출을 받는데 어려움을 겪고 있다. 재무제표 등 은행에서 신용평가를 위하여 필요로 하는 정보를 제공하지 못하는 금융정보부족 (Thin File) 때문이다. 이러한 문제를 타개하기 위해서 최근 P2P 등 대안금융에서는 기존의 금융정보 대신 핀테크를 활용한 인구통계, 거래정보 등 차별화된 정보들을 이용하여 소규모 자금을 소상공인들에게 제공하는 새로운 신용평가기법들이 확산되고 있다. 이러한 환경 변화 패러다임 속에서 본 연구는 매출액 변동, 입지조건 등 상권정보에 기초한 빅데이터를 활용하여 소상공인들에게 자금공급을 확대할 수 있는 신용평가방안을 모색하고자 한다. 상권에서 발생하는 빅데이터를 실증적으로 분석함으로써 신용평가요소로서의 효과성을 검증하여 소상공인의 사업성 평가에 필요한 주요변수들을 도출하고자 하는 것이다. 본 연구에서는 2009년에서 2018년 2월까지 서비스업을 영위하는 서울시 소재 사업체 17,116건을 대상으로 사업체의 위치별로 발생하는 상권정보를 빅데이터 전문기업 NICE지니데이터(주)로부터 수집하여 표본을 구성하였다. 소상공인들에게서도 어렵지 않게 구할 수 있는 사업장의 입지 및 상권과 관련된 빅데이터를 수집 분석하여 이들 데이터가 기업의 부실화에 영향을 미치는가를 분석하였다. 기존에 활용되지 못한 빅데이터 변수들을 탐색하여 소상공인에 대한 효율적인 금융지원에 활용 가능성을 확인함으로써 대부분 정책자금이나 담보에 의존하여 이루어지는 소상공인대출이 일반 상업은행에서도 중소기업대출의 한 부문으로 비중 있게 이루어질 수 있도록 하기 위함이다. 본 연구는 근본적으로 정보비대칭 (Information Asymmetry)의 문제가 내재되어 있는 소상공인들의 자금조달에 관하여 전통적인 재무정보가 아닌 상권분석 변수들을 도출하고, 이 변수들이 신용평가에 효과성이 있는지 여부를 상권 빅데이터의 분석을 통하여 검증하였다는 점에서 연구의 차별성이 있다.

Keywords

References

  1. Altman, E., I., Haldeman, R. G., & Narasanan, P.(1977). Zeta Analysis: A New Model to Identity Bankruptcy Risk of Corporation. Journal of Banking and Finance, 1(1), 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
  2. Altman, E. I.(1983). Why Business Fail. Journal of Business Strategy, 3(4), 15-21. https://doi.org/10.1108/eb038985
  3. Back, K.(2015). Use of Fin-Tech for SMEs and Policy Tasks. Industrial Engineering Magazine, 22(3), 33-40.
  4. Berger, A. N. & Udell, G. F.(2002). Small business credit availability and relationship lending: The importance of bank organizational structure. Economic Journal, 112(2), 32-53. https://doi.org/10.1111/1468-0297.00682
  5. Bruton, G., Khavul, S., Siegel, D., & Wright, M.(2015). New financial alternatives in seeding entrepreneurship: Microfinance, crowdfunding, and peer-to-peer innovations. Entrepreneurship Theory and Practice, 39(1), 9-26. https://doi.org/10.1111/etap.12143
  6. Choi, B. J., Kim, H. J., Kim, J. H., & Jin, S. H.(2012). Data Analytics for CRM in the Age of Big Data. Entrue Journal of Information Technology, 11(1), 19-27.
  7. Choi, J. H., Kwon, G. M., & Kim, S. T.(2002). Credit Rating Model. Seoul: Sechang publish.
  8. Choi, S. M.(2018). Changes in Personal Credit Rating Model and Implications. CIS Issue Report 2018(4), Seoul: Korea Credit Information Services.
  9. Chris B., & Daniel G.(2014). Fintech: Building a 21st Century Regulator's Toolkit. Milken Institut.
  10. De Castro, J. O., Khavul, S., & Bruton, G. D.(2014). Shades of grey: How do informal firms navigate between macro and meso institutional environments?. Strategic Entrepreneurship Journal, 8(1), 75-94. https://doi.org/10.1002/sej.1172
  11. Don, H. L., & Back, K.(2016). Equity Financing for Entrepreneurial Ventures through Crowdfunding Platforms. Asia Pacific Journal of Small Business, 38(2), 67-85.
  12. Dong, H. L., & Kim, M. K.(2013). The Effect of Relationship Banking on SME Financing: Focusing on Credit Availability and Loan Rate. Asia Pacific Journal of Small Business, 35(3), 25-48.
  13. Fayyad, U., piatetsky-Shapiro, G., & Smyth, P.(1996). The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communication of the ACM, 39(11), 27-34. https://doi.org/10.1145/240455.240464
  14. Feng, Y., Fan, X., & Yoon, Y.(2015). Lenders and borrowers' strategies in online peer-to-peer lending market: an empirical analysis of ppdai.com. Journal of Electronic Commerce Research, 16(3), 242-260.
  15. Fraser, S., Sumon, K. B., & Wright, M.(2015). What do we know about entrepreneurial finance and its relationship with growth?. International Small Business Journal, 33(1), 70-88. https://doi.org/10.1177/0266242614547827
  16. Ha, J. C., & Kim, S. J.(2015). Comparisons of the corporate credit rating model power under various conditions, Journal of the Korean Data and Information Science Society, 26(6), 1207-1216 https://doi.org/10.7465/jkdi.2015.26.6.1207
  17. Han, I. G., Kwon, Y. S., & Lee, K. C.(1995). Development of Intelligent Corporate Credit Evaluation System: NICE-AI. korean management review, 24(4), 91-118.
  18. Horrigan, J. O.(1966). The Determination of Long-Term Credit Standing with Financial Ratios. Journal of Accounting Research, 4, 44-62. https://doi.org/10.2307/2490168
  19. IBK Economic Research Institute(2013). Examples of Foreign Relations Finance and Implications. Seoul: IBK Economic Research Institute.
  20. IBK Economic Research Institute(2016). SME Credit Rating Trends in US Online Loan Companies-Focusing on Data Use. Seoul:IBK Economic Research Institute.
  21. Jaffe, D. M., & Modigliani, F.(1969). A Theory and test of credit rationing. American Economic Review, 59(5), 850-872.
  22. Jang, Y. M., & Byun, J. K.(2010). The Effect of Macroeconomic Characteristics on Credit Risk: A Case of Small and Medium Sized Enterprises. Asian Review of Financial Research, 23(4), 327-366.
  23. Jung, H. Y.(2017). Future of Bank(4) Seventy-thousand variable grasps AI credit rating. Chosun Media, http://biz.chosun.com/site/data/html_dir/2017/03/21/2017032101045.html, (2017.Mar.23).
  24. Kim, C. T., & Min, G. S.(2013). A study on management performance factors of bedding stores. The Korea Institute of Electronic Communication Sciences, 8(1), 135-142. https://doi.org/10.13067/JKIECS.2013.8.1.135
  25. Kim, I. H.(2005). Understanding Credit Ratings. Seoul: Korea Institute of Finance.
  26. Lee, H. Y., & Hong, E. T.(1995). An Application of GIS Technique to Analyze the Location of Convenience Stores: The Case of Songpa Gu, Seoul. The Journal of GIS Association of Korea, 3(2), 103-121.
  27. Lee, I. D., Lee, C. H., & Kang, S. M.(2010). The Study of Site Factors Affecting the Sales of Convenience Stores. Journal of KREAA, 16(4), p53-77.
  28. Lee, I. H., Kang, K. H., & Bae, Y. S.(2009). Study on mitigation of system risk of financial information production. Financial Services Commission.
  29. Lee, J. S., & Han, J. H.(1995). Usability Test of Non-Financial Information in Bankruptcy Prediction using Artificial Neural Network-The Case of Small and Medium-Sized Firms. Journal of Intelligence and Information Systems, 1(1), 123-134.
  30. Lee, J. H.(2009). A Study on the Feasibility Analysis of Franchisee Case and Independent Entrepreneurship Case, Asia-Pacific Journal of Business Venturing and Entrepreneurship, 4(4), 27-43.
  31. Lee, J. M., Kim, S. Y., Ha, E. H., & Roh, T. H.(2007). The Credit Evaluation System for Micro-small Sized Individual Firms Using the Analytic Hierarchy Process. The Journal of Information Systems, 16(3), 109-132.
  32. Lee, J. J., & Hwang, S. Y.(2014). Why Do Firms Switch Main Banks?. Journal of Money & Finance, 27(4), 97-138.
  33. Lee, S. Y.(2014). A Study on the Commercial District Information System Application Possibility in the SSM Location Decision. Journal of the Korean Society of Cadastre, 30(1), 63-72.
  34. Lee, S. I.(2017). FinTech to showcase a personal credit rating service based on a robot-based machine. Digital daily, http://www.ddaily.co.kr/news/article.html?no=153491, (2017.Mar.6).
  35. Lee, S. B.(2015). Impact of FinTech Innovation on the Financial Industry. Financial Risk Review, Seoul: Korea Capital Market Institute.
  36. Lee, S. L.(2013). The Effect of Bank Market Power on Korean Manufacturing SMEs: Focusing on Cash Holding and Fixed Investment. Asia Pacific Journal of Small Business, 35(1), 67-84.
  37. Mester, L.(1997). What's the point of credit scoring?, Business Review. Federal Reserve Bank of Philadelphia, 3-16.
  38. Oh, S. K., & Han, S. M.(2016). FinTech and SME financing. The Korean Finance Association 2016, 1011-1037.
  39. Oh, S. K., Choi, J. W., & Han, S. M.(2017). An Empirical Study on the Determinants of Investment in Small Business and Small Business P2P Loans. The Korean Finance Association 2017, 1-21.
  40. Park, C. S., & Kim, M. S.(2011). Credit Evaluation Model for Medical Venture Business By the Analytic Hierarchy Process. Asia-Pacific Journal of Business Venturing and Entrepreneurship, 6(2), 133-147.
  41. Park, J. G.(2001). Study on the Credit Evaluation Model of the Support System for the Small Merchants and Industrialists. Master's Thesis, Department of Business Administration The Graduate School of Business Administration Catholic University of Daegu.
  42. Queen, M., & Roll, R.(1987). Firm Mortality: Using Market Indicators to Predict Survival. Financial Analysts Journal, 43(3), 9-26. https://doi.org/10.2469/faj.v43.n3.9
  43. Rajan, R.(1992). Insiders and outsiders: The choice between informed and arm's length debt. Journal of Finance, 47, 1367-1406. https://doi.org/10.1111/j.1540-6261.1992.tb04662.x
  44. Seong, S. J.(2014). The Financial Problems of Small and Medium-Sized Enterprises about legal aspects. Korea Economic Law Association, 13(3), 279-310.
  45. Shin, C. H., Kim, Y. S., Kim, C. M.(2000). An Empirical Analysis on the Influence of Re-contract Intention in a Franchise Business. Journal of Marketing Management Research, 5(2), 79-101.
  46. Son, S. H.(2013), Challenges for restructuring the SME financial system. Korea Institute of Finance, Financial Brief, 22(21), 3-9.
  47. Stiglitz, J. E., & Weiss, A.(1981), Credit rationing in markets with imperfect information. American Economic Review, 71(3), 393-410.
  48. Ventura, A., Koenitzer, M., Stein, P., Tufano, P., & Drummer, D.(2015). The Future of FinTech: A Paradigm Shift in Small Business Finance, In Global Agenda Council on the Future of Financing and Capital, World Economic Forum. http://www3.weforum.org/docs/IP/2015/FS/GAC15_The_Future_of_FinTech_Paradigm_Shift_Small_Business_Finance_report_2015.pdf
  49. Won, S. H., & Chun, B. G.(2013). Analysis of the Relationship between Credit Ratings and Firm's Performance. Review of Financial Information Studies, 2(1), 37-60.
  50. Yang, J. H.(2003). The development of the credit evaluation model for small-micro business using AHP. Master's Thesis, Korea Advanced Institute of Science and Technology (KAIST).
  51. Yoo, W. J., & Lee, C. G.(2015). The Impact of Non-Financial Factors in SME and Venture Business Credit Evaluation. Korean journal of business administration, 28(12), 3191-3210. https://doi.org/10.18032/kaaba.2015.28.12.3191
  52. Yoon, J. S.(2007). GIS based Credit Evaluation Model for Small and Micro Business with Sales Analysis and Trade Analysis. Doctoral dissertation, Graduate school of Dongguk University.
  53. Yoon, J. S., Roh, T. H., & Young S. K.(2007). Development of the Credit Evaluation Model for Small and Micro Business Using Credit Card Sales Information. The Korean Small Business Review, 29(2), 73-98.
  54. Yun, S. Y.(2015). Comparative analysis of security of major simple settlement service. e-Finance and Financial Security vol2., Financial security institute.
  55. Yun, S. Y., Kang, M. S., Lee, H. T.(2016). Is Non-financial Data Important for Credit-rating of Micro-Enterprises?. Korean Management Consulting Review, 16(2), 37-46.