Computational Experiment to Compare Techniques in Large Datasets to Measure Credit Banking Risk in Home Equity Loans

Computational Experiment to Compare Techniques in Large Datasets to Measure Credit Banking Risk in Home Equity Loans

A. Pérez-MartÍn M. Vaca

Department of Economics and Finance. Miguel Hernandez University of Elche, Spain

| |
| | Citation



In the 1960s, coinciding with the massive demand for credit cards, financial companies needed a method to know their exposure to risk insolvency. It began applying credit-scoring techniques. In the 1980s credit-scoring techniques were extended to loans due to the increased demand for credit and computational progress. In 2004, new recommendations of the Basel Committee (as called Basel II) on banking supervision appeared. With the ensuing global financial crisis, a new document, Basel III, appeared. It introduced more demanding changes on the control of borrowed capital.

Nowadays, one of the main problems not addressed is the presence of large datasets. This research is focused on calculating probabilities of default in home equity loans, and measuring the computational efficiency of some statistical and data mining methods. In order to do these, some Monte Carlo experiments with known techniques and algorithms have been developed.

These computational experiments reveal that large datasets need BigData techniques and algorithms that yield faster and unbiased estimators.


bigdata, credit scoring, monte carlo, discriminant analysis, support vector machine


[1] Fisher, R., The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, pp. 179-188, 1936.

[2] Durand, D., Risk Elements in Consumer Instalment Financing. National Bureau of Economic Research: Massachusetts, 1941.

[3] Escalona Cortes, A., Uso de los Modelos Credit Scoring en Microfinan-zas. Ph.D. thesis, Institución de Enseñanza e Investigación en Ciencias Agrícolas, Montecillo, Texcoco, Mexico, 2011.

[4] Gutierrez, M., Modelos de credit scoring: que, cómo, cuando y para que. MPRA Paper, 16377, pp. 1-30, 2007.

[5] Trias, R., Carrascosa, F., Fernandez, D., Pares, L. & Nebot, G., Riesgo de cróeditos: Conceptos para su medicióon, basilea ii, herramientas de apoyo a la gestióon. AIS Group - Financial Decisions, 2005.

[6] Hand, D. & Henley, W.E., Statistical classification methods in consumer credit scoring: a review. Royal Statistical Society, 160(3), pp. 523-541, 1997.

[7] Ochoa, J.C., Galeano, W. & Agudelo, L., Construcción de un modelo de scoring para el otorgamiento de crédito en una entidad financiera. Perfil de Coyuntura Económica, 16, pp. 191-222, 2010.

[8] Canton, S.R., Rubio, J.L. & Blasco, D.C., Un modelo de credit scoring para instituciones de microfinanzas en el marco de basilea ll. Journal of Economics, Finance and Administrative Science, 15(28), 2010.

[9] Srinivasan, V. & Kim, Y.H., Credit granting: a comparative analysis of classification procedures. Journal of Finance, 42, pp. 665-683, 1987.

[10] Boj, E., Claramunt, M.M. & Fortiana, J., Selection of predictors in distance-based regression. Communications in Statistics-Simulation and Computation, 36(1), pp. 87-98, 2007.

[11] Boj, E., Claramunt, M. & Esteve, J., A.and Fortiana, Criterios de se-leccióon de modelo en credit scoring, aplicacióon del anóalisis discriminante basado en distancias. En Anales del Instituto de Actuarios Españoles, 15, pp. 833-869, 2009.

[12] Tam, K. & Kiang, M., Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38, pp. 926-947, 1992.

[13] Desai, V., Crook, J. & Overstreet, G., A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95, pp. 24-37, 1996.

[14] Yobas, J., M.B.and Crook & Ross, P., Credit scoring using neural and evolutionary techniques. IMA Journal of Management Mathematics, 11, pp. 111-125, 2000.

[15] Boj, E., Claramunt, M.M., Esteve, A. & Fortiana, J., Credit scoring basado en distancias: coeficientes de influencia de los predictores. Investigaciones en Seguros y Gestión de riesgos: RIESGO 2009, ed. F.M. Estudios, Cuadernos de la Fundacióon MAPFRE: Madrid, pp. 15-22, 2009.

[16] Thomas, L.C., A survey of credit and behavioral scoring: forecasting financial risk of lending to consumers. International Journal of Forecasting, 16, pp. 149-172, 2000.

[17] Boj, E., Claramunt, M.M., Granóe, A. & Fortiana, J., Projection error term in gower’s interpolation. Journal of Statistical Planning and Inference, 139, pp. 1867-1878, 2009.

[18] Galindo, J. & Tamayo, P., Credit risk assessment using statistical and machine learning: Basic methodology and risk modeling applications. Computational Economics, 15, pp. 107-143, 2000.

[19] Altman, E., The importante and subtlety of credit rating migration. Journal of Banking and Finance, 22, pp. 1231-1247, 1998.

[20] Artóís, M., Guillóen, M. & Martóínez, J.M., A model for credit scoring: an application of discriminant anlysis. QUESTIIO, 18(3), pp. 385-395, 1994.

[21] Gordy, M.B., A comparative anatomy of credit risk models. Journal of Banking and Finance, 24, pp. 119-149, 2000.

[22] Cheung, S., Provincial credit ratings in canada, an ordered probit analysis. Bank of Canada, 6, 1996.

[23] West, D., Neural network credit scoring models. Computers and Operations Research, 27, pp. 1131-1152, 2000.

[24] Bonilla, M., Olmeda, I. & Puertas, R., Modelos paramóetricos y no paramóetricos en problemas de credit scoring. Revista Espanñola de Fi-nanciacion y Contabilidad, 32(118), pp. 833-869, 2003.

[25] Liu, C., Frazier, P. & Kumar, L., Comparative assessment of the measures of thematic classification accuracy. Remote Sens Environ, 107, pp. 606-616, 2007.

[26] Falkenstein, E., Risk calc for private companies: Moody’s default model. rating methodology. Moody’s Investor Service, Global Credit Research,, 2000.

[27] Moses, D. & Liao, S., On developing models for failure prediction. Journal of Commercial Bank Lending, 69, pp. 27-38, 1987.

[28] Wiginton, J.C., A note on the comparison of logit and discriminant models of consumer credit behavior. Journal ofFinancial and Quantitative Analysis, 15(03), pp. 757-770, 1980.

[29] Van Gestel, T., Baesens, B., Garcia, J. & Van Dijcke, P., A support vector machine approach to credit scoring. Bank en Financiewezen, 2, pp. 73-82, 2003.

[30] Yu, L., Yao, X., Wang, S. & Lai, K.K., Credit risk evaluation using a weighted least squares svm classifier with design of experiment for parameter selection. Expert Systems with Applications, 38(12), pp. 1539215399, 2011.

[31] Morales Gonzalez, D., Perez Martin, A. & Vaca Lamata, M., Monte carlo simulation study under regression models to estimate credit banking risk in home equity loan. Data Management and Security Applications in Medicine, Science and Engineering, 45, pp. 141-152, 2013.

[32] Baesens, B., Van Gestel, T., Viaene, S., Stepanova, J., M. andSuykens & Vanthienen, J., Benchmarking state-of-the-art classification algorithms for credit scoring. Journal ofthe Operational Research Society, 54(6), pp. 1082-1088, 2003.

[33] Seber, G., Multivariate Observations. Wiley series in probability and mathematical statistics. John Wiley and Sons, Inc.: New-York, 1938.

[34] Marks, S. & Dunn, O.J., Discriminant functions when covariance matrices are unequal. Journal of the American Statistical Association, 69(346), pp. 555?-559, 1974.

[35] Venables, W.N. & Ripley, B.D., Modern Applied Statistics with S. Springer: New York, 4th edition, 2002. ISBN 0-387-95457-0.

[36] R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2015.

[37] Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A. & Leisch, F., e1071: Misc Functions of the Department of Statistics (e1071), TU Wien, 2014. R package version 1.6-4.