Skip to main content
SpringerLink
Log in
Book cover

International Conference on Web Engineering

ICWE 2020: Web Engineering pp 113–129Cite as

A Credit Scoring Model for SMEs Based on Social Media Data

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12128))

Abstract

Credit scoring is an important tool to assess the solidity of small and medium-sized enterprises (SMEs), and to unlock for them new options for credit and improvement of cash flow. Credit scoring is, in its most common form, used by (potential) creditors to predict the probability of SMEs to default in the future, as an inverse measure of creditworthiness. The majority of existing credit scoring methods for SMEs are solely based on the analysis of SMEs’ financial data. While straightforward, these methods have major limitations: they may rely on very incomplete or outdated data, and fail to capture the very dynamic environment in which the business of SMEs evolves. In this paper, we propose an alternative approach to credit scoring for SMEs by enriching traditionally used financial data with social media data. We carried out our analysis on 25654 SMEs in the Netherlands, using 20 traditional financial indicators and 35 social media features. Experimental results suggest that the use of social media data in addition to traditional data significantly improves the quality of the credit scoring model for SMEs. Furthermore, we analyze the most important factors from social media data influencing the credit scoring.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    The information made available by Exact for this research is provided for use for this research only and under strict confidentiality.

  2. 2.

    www.kvk.nl.

  3. 3.

    https://www.seleniumhq.org/.

  4. 4.

    https://pypi.org/project/beautifulsoup4/.

References

  1. Altman, E.I.: An emerging market credit scoring system for corporate bonds. Emerg. Markets Rev. 6(4), 311–323 (2005)

    Article  Google Scholar 

  2. Altman, E.I., Esentato, M., Sabato, G.: Assessing the credit worthiness of Italian SMEs and mini-bond issuers. Glob. Finance J. (2018)

    Google Scholar 

  3. Altman, E.I., Sabato, G.: Modelling credit risk for SMEs: evidence from the US market. Abacus 43(3), 332–357 (2007)

    Article  Google Scholar 

  4. Altman, E.I., Sabato, G., Wilson, N.: The value of non-financial information in small and medium-sized enterprise risk management. J. Credit Risk 6(2), 1–33 (2010)

    Article  Google Scholar 

  5. Angilella, S., Mazzù, S.: The financing of innovative SMEs: a multicriteria credit rating model. Eur. J. Oper. Res. 244(2), 540–554 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bensic, M., Sarlija, N., Zekic-Susac, M.: Modelling small-business credit scoring by using logistic regression, neural networks and decision trees. Intell. Syst. Acc. Finance Manag. 13(3), 133–150 (2005)

    Article  Google Scholar 

  7. Bocconi, S., Bozzon, A., Psyllidis, A., Titos Bolivar, C., Houben, G.J.: Social glass: a platform for urban analytics and decision-making through heterogeneous social data. In: Proceedings of the 24th International Conference on World Wide Web. WWW 2015 Companion, pp. 175–178. Association for Computing Machinery, New York, NY (2015). https://doi.org/10.1145/2740908.2742826

  8. Bonsón, E., Ratkai, M.: A set of metrics to assess stakeholder engagement and social legitimacy on a corporate Facebook page. Online Inf. Rev. 37(5), 787–803 (2013)

    Article  Google Scholar 

  9. Brown, I., Mues, C.: An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst. Appl. 39(3), 3446–3453 (2012)

    Article  Google Scholar 

  10. Ciampi, F., Gordini, N.: Small enterprise default prediction modeling through artificial neural networks: an empirical analysis of Italian small enterprises. J. Small Bus. Manag. 51(1), 23–45 (2013)

    Article  Google Scholar 

  11. European Commission: 2018 SBA fact sheet Netherlands. European Commission (2018)

    Google Scholar 

  12. De Cnudde, S., Moeyersoms, J., Stankova, M., Tobback, E., Javaly, V., Martens, D.: Who cares about your Facebook friends? Credit scoring for microfinance (2015)

    Google Scholar 

  13. De Cnudde, S., Moeyersoms, J., Stankova, M., Tobback, E., Javaly, V., Martens, D.: What does your Facebook profile reveal about your creditworthiness? Using alternative data for microfinance. J. Oper. Res. Soc. 70(3), 353–363 (2019)

    Article  Google Scholar 

  14. Edmister, R.O.: An empirical test of financial ratio analysis for small business failure prediction. J. Finan. Quant. Anal. 7(2), 1477–1493 (1972)

    Article  Google Scholar 

  15. Feldman, R.J.: Small business loans, small banks and big change in technology called credit scoring. Reg. (Sep), 19–25 (1997)

    Google Scholar 

  16. Fridson, M.S., Alvarez, F.: Financial Statement Analysis: A Practitioner’s Guide, vol. 597. Wiley, Hoboken (2011)

    Google Scholar 

  17. He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 9, 1263–1284 (2008)

    Google Scholar 

  18. Khemakhem, S., Boujelbene, Y.: Predicting credit risk on the basis of financial and non-financial variables and data mining. Rev. Acc. Finance 17(3), 316–340 (2018)

    Article  Google Scholar 

  19. Lee, B.K., Sohn, S.Y.: A credit scoring model for SMEs based on accounting ethics. Sustainability 9(9), 1588 (2017)

    Article  Google Scholar 

  20. Marqués, A.I., García, V., Sánchez, J.S.: On the suitability of resampling techniques for the class imbalance problem in credit scoring. J. Oper. Res. Soc. 64(7), 1060–1070 (2013)

    Article  Google Scholar 

  21. Masyutin, A.: Credit scoring based on social network data. Bus. Inform. 3(33), 15–23 (2015)

    Google Scholar 

  22. McCann, M., Barlow, A.: Use and measurement of social media for SMEs. J. Small Bus. Enterp. Dev. 22(2), 273–287 (2015)

    Article  Google Scholar 

  23. Michaelidou, N., Siamagka, N.T., Christodoulides, G.: Usage, barriers and measurement of social media marketing: an exploratory investigation of small and medium B2B brands. Ind. Mark. Manag. 40(7), 1153–1159 (2011)

    Article  Google Scholar 

  24. Neiger, B.L., et al.: Use of social media in health promotion: purposes, key performance indicators, and evaluation metrics. Health Promot. Pract. 13(2), 159–164 (2012)

    Article  Google Scholar 

  25. Öztamur, D., Karakadılar, İ.S.: Exploring the role of social media for SMEs: as a new marketing strategy tool for the firm performance perspective. Procedia-Soc. Behav. Sci. 150, 511–520 (2014)

    Article  Google Scholar 

  26. Pederzoli, C., Thoma, G., Torricelli, C.: Modelling credit risk for innovative smes: the role of innovation measures. J. Financ. Serv. Res. 44(1), 111–129 (2013). https://doi.org/10.1007/s10693-012-0152-0

    Article  Google Scholar 

  27. Silvestri, G., Yang, J., Bozzon, A., Tagarelli, A.: Linking accounts across social networks: the case of StackOverflow, Github and Twitter. In: Armano, G., Bozzon, A., Giuliani, A. (eds.) Proceedings of the 1st International Workshop on Knowledge Discovery on the WEB, KDWeb 2015, Cagliari, Italy, 3–5 September 2015, vol. 1489, pp. 41–52. CEUR-WS.org (2015). http://ceur-ws.org/Vol-1489/paper-05.pdf

  28. Sohn, S.Y., Moon, T.H., Kim, S.: Improved technology scoring model for credit guarantee fund. Expert Syst. Appl. 28(2), 327–331 (2005)

    Article  Google Scholar 

  29. Tan, T., Phan, T.: Social media-driven credit scoring: the predictive value of social structures (2016)

    Google Scholar 

  30. Xia, Y., Liu, C., Li, Y., Liu, N.: A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring. Expert Syst. Appl. 78, 225–241 (2017)

    Article  Google Scholar 

  31. Yuan, H., Lau, R.Y., Xu, W., Pan, Z., Wong, M.C.: Mining individuals’ behavior patterns from social media for enhancing online credit scoring. In: PACIS, p. 163 (2018)

    Google Scholar 

  32. Zhang, Y., Jia, H., Diao, Y., Hai, M., Li, H.: Research on credit scoring by fusing social media information in online peer-to-peer lending. Procedia Comput. Sci. 91, 168–174 (2016)

    Article  Google Scholar 

  33. Zhu, Y., Zhou, L., Xie, C., Wang, G.J., Nguyen, T.V.: Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. Int. J. Prod. Econ. 211, 22–33 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Exact Software, Delft, Netherlands

    Septian Gilang Permana Putra, Bikash Joshi & Judith Redi

  2. Delft University of Technology, Delft, Netherlands

    Alessandro Bozzon

Authors
  1. Septian Gilang Permana Putra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bikash Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Judith Redi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alessandro Bozzon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alessandro Bozzon .

Editor information

Editors and Affiliations

  1. Slovak University of Technology, Bratislava, Slovakia

    Maria Bielikova

  2. University of Helsinki, Helsinki, Finland

    Tommi Mikkonen

  3. University of Lugano (USI), Lugano, Switzerland

    Cesare Pautasso

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Putra, S.G.P., Joshi, B., Redi, J., Bozzon, A. (2020). A Credit Scoring Model for SMEs Based on Social Media Data. In: Bielikova, M., Mikkonen, T., Pautasso, C. (eds) Web Engineering. ICWE 2020. Lecture Notes in Computer Science(), vol 12128. Springer, Cham. https://doi.org/10.1007/978-3-030-50578-3_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50578-3_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50577-6

  • Online ISBN: 978-3-030-50578-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation