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Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking

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Abstract

The Korea government offers technology credit guarantee service to many technology-based small and medium enterprises (SMEs) suffering from funding problems. Many advanced application credit scoring models have been developed based on technology to reduce the high default rates of this service. However, a credit scoring model which can reflect changes in firms after a loan has been granted has not yet been developed. In the study reported here, we propose a behavioral credit scoring model that reflects the debt-paying ability of recipient firms, which is observed as a time series of financial ratios of firms via the relationship banking activities. We utilize this time series, as well as missing patterns of financial information, as additional predictors of loan defaults. We compare our proposed behavioral credit scoring models, fitted at different points of elapsed time, to the application credit scoring model. Finally, we suggest the best behavioral credit scoring model for technology-based SMEs. Our study can contribute to the reduction of the risk involved in credit funding for technology-based SMEs.

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References

  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.

    Article  Google Scholar 

  • Atiya, A. F. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on Neural Networks, 12(4), 929–935.

    Article  Google Scholar 

  • Baas, T., & Schrooten, M. (2010). Relationship banking and SMEs: A theoretical analysis. Small Business Economics, 27(2–3), 127–137.

    Google Scholar 

  • Ballou, D. P., & Pazer, H. L. (1985). Modeling data and process quality in multi-input, multi-output information systems. Management Science, 31(2), 150–162.

    Article  Google Scholar 

  • Bastos, J. A. (2010). Forecasting bank loans loss-given-default. Journal of Banking & Finance, 34(10), 2510–2517.

    Article  Google Scholar 

  • Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111.

    Article  Google Scholar 

  • Becerra-Fernandez, I., Zanakis, S. H., & Walczak, S. (2002). Knowledge discovery techniques for predicting country investment risk. Computers and Industrial Engineering, 43, 787–800

    Google Scholar 

  • Becchetti, L., & Sierra, J. (2003). Bankruptcy risk and productive efficiency in manufacturing firms. Journal of Banking & Finance, 27(11), 2099–2120.

    Article  Google Scholar 

  • Berger, A. N., & Black, L. K. (2011). Bank size, lending technologies, and small business finance. Journal of Banking & Finance, 35(3), 724–735.

    Article  Google Scholar 

  • Berger, A. N., & Udell, G. F. (1995). Relationship lending and lines of credit in small firm finance. The Journal of Business, 68(3), 351–381.

    Article  Google Scholar 

  • Boot, A. W. A., & Thakor, A. V. (1994). Moral hazard and secured lending in an infinitely repeated credit market game. International Economic Review, 35(4), 899–920.

    Article  Google Scholar 

  • Cowling, M., & Mitchell, P. (2003). Is the small firms loan guarantee scheme hazardous for banks or helpful to small business? Small Business Economics, 21(1), 63–71.

    Article  Google Scholar 

  • Diamond, D. W. (1989). Reputation acquisition in debt markets. Journal of Political Economy, 97(4), 828–862.

    Article  Google Scholar 

  • Hernández Cánovas, G., & Martínez Solano, P. (2010). Relationship lending and SME financing in the continental European bank-based system. Small Business Economics, 34(4), 465–482.

    Article  Google Scholar 

  • Hsieh, N. C. (2004a). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27(4), 623–633.

    Article  Google Scholar 

  • Hsieh, N. C. (2004b). Hybrid mining approach in the design of credit scoring models. Expert Systems with Applications, 28(4), 655–665.

    Article  Google Scholar 

  • Jeon, H. J., & Sohn, S. Y. (2008). The risk management for technology credit guarantee fund. Journal of the Operational Research Society, 59(12), 1624–1632.

    Article  Google Scholar 

  • Kim, Y. S., & Sohn, S. Y. (2007). Technology scoring model considering rejected applicants and effect of reject inference. Journal of the Operational Research Society, 58(10), 1341–1347.

    Article  Google Scholar 

  • Matthai, A. (1951). Estimation of parameters from incomplete data with application to design of sample surveys. The Indian Journal of Statistics (1933–1960), 11(2), 145–152.

    Google Scholar 

  • Moon, T. H., Kim, Y., & Sohn, S. Y. (2011). Technology credit rating system for funding SMEs. Journal of the Operational Research Society, 62, 608–615.

    Article  Google Scholar 

  • Moon, T. H., & Sohn, S. Y. (2008a). Technology scoring model for reflecting evaluator’s perception within confidence limits. European Journal of Operational Research, 184(3), 981–989.

    Article  Google Scholar 

  • Moon, T. H., & Sohn, S. Y. (2008b). Case based reasoning for predicting multi-period financial performances of technology-based SMEs. Applied Artificial Intelligence, 22(7), 1–14.

    Google Scholar 

  • Moon, T. H., & Sohn, S. Y. (2010). Technology credit scoring model considering both SME characteristics and economic conditions: The Korean case. Journal of the Operational Research Society, 61(4), 666–675.

    Article  Google Scholar 

  • Nakamura, L. I. (1993). Recent research in commercial banking: Information and lending. Financial Markets, Institutions, and Instruments, 2, 73–88.

    Google Scholar 

  • Niemann, M., Schmidt, J. H., & Neukirchen, M. (2008). Improving performance of corporate rating prediction models by reducing financial ratio heterogeneity. Journal of Banking & Finance, 32(3), 434–446.

    Article  Google Scholar 

  • Oba, S., Sato, M., Takemasa, I., Monden, M., Matsubara, K., & Ishii, S. (2003). A Bayesian missing value estimation method for gene expression profile data. Bioinformatics, 19(16), 2088–2096.

    Google Scholar 

  • Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131.

    Article  Google Scholar 

  • Ortiz-Molina, H., & Penas, M. F. (2008). Lending to small businesses: The role of loan maturity in addressing information problems. Small Business Economics, 30(4), 361–383.

    Article  Google Scholar 

  • Petersen, M., & Rajan, R. (1993). The effect of credit market concentration on lending relationships. Working paper. Chicago: University of Chicago.

  • Royston, P. (2004). Multiple imputation of missing values. The Stata Journal, 4(3), 227–241.

    Google Scholar 

  • Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.

    Book  Google Scholar 

  • Sarlija, N., Bensic, M., & Zekic-Susac, M. (2006). Modeling customer revolving credit scoring using logistic regression, survival analysis and neural networks. In Proceedings of the 7th WSEAS international conference on neural networks. Cavtat, Croatia, June 12–14, pp. 164–169.

  • Schafer, J. L., & Olsen, M. K. (1998). Multiple imputation for multivariate missing-data problems: A data analyst’s perspective. Multivariate Behavioral Research, 33(4), 545–571.

    Article  Google Scholar 

  • Sohn, S. Y., & Kim, H. S. (2007). Random effects logistic regression model for default prediction of technology credit guarantee fund. European Journal of Operational Research, 183(1), 427–478.

    Google Scholar 

  • Sohn, S. Y., Kim, H. S., & Moon, T. H. (2007). Predicting the financial performance index of technology fund for SME using structural equation model. Expert Systems with Applications, 32(3), 890–898.

    Article  Google Scholar 

  • Sohn, S. Y., & Moon, T. H. (2003). Structural equation model for predicting technology commercialization success index (TCSI). Technological Forecasting and Social Change, 70(9), 885–899.

    Article  Google Scholar 

  • Sohn, S. Y., Moon, T. H., & Kim, H. S. (2005). Improved technology scoring model for credit guarantee fund. Expert Systems with Applications, 28(2), 327–331.

    Article  Google Scholar 

  • Thomas, L. C. (2000). A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting, 16(2), 149–172.

    Article  Google Scholar 

  • Zecchini, S., & Ventura, M. (2009). The impact of public guarantees on credit to SMEs. Small Business Economics, 32(2), 191–206.

    Article  Google Scholar 

  • Zhang, D., Chen, Q., & Wei, L. (2007). Building behavior scoring model using genetic algorithm and support vector machines. Computational Science, 4488, 482–485.

    Google Scholar 

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Correspondence to So Young Sohn.

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Sohn, S.Y., Kim, Y.S. Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking. Small Bus Econ 41, 931–943 (2013). https://doi.org/10.1007/s11187-012-9457-5

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