Credit concession through credit scoringAnalysis and application proposal
- Oriol Amat 1
- Raffaele Manini 1
- Marcos Antón Renat 2
-
1
Universitat Pompeu Fabra
info
-
2
Universidad de Murcia
info
ISSN: 1697-9818
Year of publication: 2017
Issue Title: IV Jornada ACCID i APC
Volume: 13
Issue: 1
Pages: 51-70
Type: Article
More publications in: Intangible Capital
Abstract
Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model. Findings: Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors. Research limitations: This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations. Practical implications: Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom not to grant credit. Social implications: Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment’s. Originality/value: There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data. The application of the model to detect the probability of default in loans is original.
Bibliographic References
- Abdou, H.A., & Pointon, J. (2011). Credit Scoring, statistical techniques and evaluation criteria: A review of the literature. Intelligent System in Accounting, Finance and Management, 18, 59-88. https://doi.org/10.1002/isaf.325
- Arimany, N., & Viladecans, C. (2015). Analysis of the cash flow statement's usefulness: An empirical study. European Accounting and Management Review, 1, 75-100. https://doi.org/10.2139/ssrn.2737292
- Allen, L., De Long, G., & Saunders, A. (2004). Issues in credit risk modeling of retail markets. Journal of Banking and Finance, 28, 727-752. https://doi.org/10.1016/S0378-4266(03)00197-3
- Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate Bankruptcy. Journal of Finance, 23(4)., 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
- Amat, O., Pujadas, P., & Lloret, P. (2012). Análisis de Operaciones de Crédito. Barcelona: Profit Editorial.
- Anderson, R. (2007). The Credit Scoring Toolkit: Theory and practice for retail credit risk management and decision automation. New York: Oxford University Press.
- Antón, M. (2007). Una propuesta alternativa en la valoración del riesgo de fracaso empresarial mediante la elaboración y aplicación a priori de modelos de predicción de alerta de crisis. Revista de Contabilidad y Tributación CEF, (288), 111-162.
- Argenti, J. (1983). Predicting Corporate Failure, Institute of Chartered Accountants in English and Wales. Accountants Digest, 138, 1-25.
- Bardos, M. (1998). Detecting the risk of company failure at Banque de France. Journal of Banking and Finance, 10-11, 1405-1419. https://doi.org/10.1016/S0378-4266(98)00062-4
- Blanco, A., Pino-Mejías, R., Lara, J., & Rayo, S. (2013). Credit scoring models for the microfinance industry using neural networks: Evidence from Peru. Expert Systems with Applications, 40, 356-364. https://doi.org/10.1016/j.eswa.2012.07.051
- Blöchlinger, A., & Leippold, M. (2006). Economic benefit of powerful credit scoring. Journal of Banking and Finance, 30, 851-873. https://doi.org/10.1016/j.jbankfin.2005.07.014
- Bonilla, M, Olmeda, I., & Puertas, R. (2003). Modelos paramétricos y no paramétricos en problemas de Credit Scoring. Revista Española de Financiación y Contabilidad, 118(32), 833-869. https://doi.org/10.1080/02102412.2003.10779502
- Caudill, S., Gropper, D., & Hartarska, V. (2012). Microfinance institution costs: Effects of gender, subsidies and technology. Journal of Financial Economic Policy, 4(4), 292-304. https://doi.org/10.1108/17576381211279271
- Chuang, C., & Lin, R. (2009). Constructing a reassigning credit scoring model. Expert Systems with Applications, 36, 1685-1694. https://doi.org/10.1016/j.eswa.2007.11.067
- Conan, J., & Holder, M. (1979). Variables explicatives de performance et controle de gestion dans les P.M.I. Tesis, CERG, Université Paris Dauphine.
- Crook, J. N. (1996). Credit scoring: An overview. Working paper series No. 96/13, British Association, Festival of Science. University of Birmingham, The University of Edinburgh.
- De Young, R., Glennon, D., & Nigro, P. (2008). Borrower-lender distance, credit scoring, and loan performance: Evidence from International-opaque small business borrowers. Journal of Financial Intermediation, 17, 113-143. https://doi.org/10.1016/j.jfi.2007.07.002
- Dinh, T. H. T., & Kleimeier, S. (2007). A credit scoring model for Vietnam's retail banking market. International Review of Financial Analysis, 16, 471-495. https://doi.org/10.1016/jirfa.2007.06.001
- Dryver, A. L., & Sukkasem, J. (2009). Validating risk models with a focus on credit scoring models. Journal of Statistical Computation and Simulation, 2(79), 181-193. https://doi.org/10.1080/00949650701684678
- Edminster, R. O. (1972). An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction. Journal of financial and Quantitative analysis, 2, 1477-1493. https://doi.org/10.2307/2329929
- Fair Isaac Company (2015). Available online at: http://www.myfico.com/CreditEducation/articles/
- Grablowsky, B.J., & Talley, W.K. (1981). Probit and discriminant functions for classifying credit applicants: A comparison. Journal of Economic and Business, 33(3), 254-261.
- Gutiérrez, M. A. (2008). Anatomía de losmodelos de credit scoring. Ensayos Económicos BCRA, 50, 61-96.
- Hand, D. J., & Jacka, S. D. (1998). Statistics in Finance. London: Arnold.
- Hsieh, N., & Hung, L. (2010). A data driven ensemble classifier for credit scoring analysis. Expert Systems with Applications, 37, 534-545. https://doi.org/10.1016/j.eswa.2009.05.059
- Hu, Y., & Ansell, J. (2007). Measuring retail company performance using credit scoring techniques. European Journal of Operational Research, 183, 1595-1606. https://doi.org/10.1016/j.ejor.2006.09.101
- Jacobson, T., & Roszbach, K. (2003). Bank lending policy, credit scoring and value-at-risk. Journal of Banking and Finance, 27, 615-633. https://doi.org/10.1016/S0378-4266(01)00254-0
- Kim, Y. S., & Sohn, S. Y. (2004). Managing loan customers using misclassification patterns of credit scoring model. Expert Systems with Applications, 26, 567-573. https://doi.org/10.1016/j.eswa.2003.10.013
- Marín, S., Antón, M., & Mondragón, Z. (2011). Crisis bancarias, información financiera y modelos de predicción: Estudio de un caso. GCG: Revista de Globalización, Competitividad y Gobernabilidad, 5, 32-41.
- Marshall, A., Tang, L., & Milne, A. (2010). Variable reduction, simple selection bias and bank retail credit scoring. Journal of Empirical Finance, 17, 501-512. https://doi.org/10.1016/j.jempfin.2009.12.003
- Ochoa, J.C., Galeano, W., & Agudelo, L.G. (2010). Construcción de un modelo de scoring para el otorgamiento de crédito en una entidad financiera. Perfil de Coyuntura Económica, 16, 191-222.
- Paleologo, G., Elisseeff, A., & Antonini, G. (2010). Subagging for credit scoring models. European Journal of Operational Research, 201, 490-499. https://doi.org/10.1016/j.ejor.2009.03.008
- Rayo, S., Lara, J., & Camino, D. (2010). Un Modelo de Credit Scoring para instituciones de microfinanzas en el marco de Basilea II. Journal of Economics, Finance and Administrative Science, 28(15), 91-124.
- Saladrigues, R., & Grañó, M. (2014). Audit Expectation Gap: Fraud detection and other factors. European Accounting and Management Review, 2, 120-142.
- Schreiner, M. (2002). Ventajas y desventajas del scoring estadístico para las microfinanzas. Microfinance Risk Management, Washington University in St. Louis, 1-40.
- Schreiner, M. (2004). Scoring arrears at a Microlender in Bolivia. Journal of Microfinance, 6(2), 65-88.
- Shu-Ting, L., Cheng, B., & Hsieh, C. (2009). Prediction model building with clustering-launched classification and support vector machines in credit scoring. Expert Systems with Applications, 36, 7562-7566. https://doi.org/10.1016/j.eswa.2008.09.028
- Tascón, M., & Castaño, F. (2012). Variables y modelos para la identificación y predicción del fracaso empresarial: Revisión de la investigación empírica reciente. Revista de Contabilidad, 15(1), 7-58. https://doi.org/10.1016/S1138-4891(12)70037-7
- Thomas, L. C. (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. International Journal of Forecasting, 16, 149-172. https://doi.org/10.1016/S0169-2070(00)00034-0
- Van Gool, J., Verbeke, W, Sercu, P., & Baesens, B. (2012). Credit scoring for microfinance: Is it worth it?. International Journal of Finance and Economics, 17, 103-123. https://doi.org/10.1002/ijfe.444
- Viganó, L. (1993). A credit scoring model for development banks: An African case study. Savings and Development, 4, 441-482.
- Wall, A. (1928). Ratio method and statement analysis. Nueva York: Harper.
- Wang, G., Hao, J., Ma, J., & Jiang, H. (2011). A comparative assessment of ensemble learning for credit scoring. Expert System with Applications, 38, 223-230. https://doi.org/10.1016/j.eswa.2010.06.048
- Wang, G., Ma, J., Huang, L., & Xu, K. (2012). Two credit scoring models based on dual strategy ensemble trees. Knowledge-Based Systems, 26, 61-68. https://doi.org/10.1016/j.knosys.2011.06.020
- Zhou, X., Zhang, D., & Jiang, Y. (2008). A New Credit Scoring Method Based on Rough Sets and Decision Tree. Advances in Knowledge Discovery and Data Mining, 5012, 1081-1089. https://doi.org/10.1007/978-3-540-68125-0_117
- Zhou, L., Lai, K., & Yen, J. (2009). Credit scoring models with AUC maximization based on weighted SVM. International Journal of Information and Decision Making, 8(4), 677-696. https://doi.org/10.1142/S0219622009003582