A Optimal Subclass Detection Method for Credit Scoring

Authors: Luciano Nieddu, Giuseppe Manfredi, Salvatore D'Acunto, Katia La Regina

Abstract:

In this paper a non-parametric statistical pattern recognition algorithm for the problem of credit scoring will be presented. The proposed algorithm is based on a clustering k- means algorithm and allows for the determination of subclasses of homogenous elements in the data. The algorithm will be tested on two benchmark datasets and its performance compared with other well known pattern recognition algorithm for credit scoring.

Keywords: Constrained clustering, Credit scoring, Statistical pattern recognition, Supervised classification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1327539

References:

[1] L. J. Meswter, What's the point of credit scoring?, Business Review, Federal Reserve Bank of Philadelphia, 1997
[2] C. Cifarelli, L. Nieddu, O. Seref, and P. Pardalos. K-t.r.a.c.e.: A kernel k-means procedure for classification. Computers and Operations Research, 34(10): 3154-3161, 2007.
[3] A. D. Gordon. Classification. Chapman & Hall Ltd, London; New York, 1999.
[4] G. Grimaldi, C. Manna, L. Nieddu, G. Patrizi, and P. Simonazzi. A diagnostic decision support system and its applications of the choice of suitable embryos in human assisted reproduction. Central European journal of Operations Research, 10(1):29-44, April 2002.
[5] I. T. Jolliffe. Principal Component Analysis. Springer Verlag, New York, 2002.
[6] A. Lozano, G. Manfredi, and L. Nieddu. An algorithm for the recognition of levels of congestion in road traffic problems. Mathematics and Computers in Simulation, 2007.
[7] L. Nieddu and G. Patrizi. Formal properties of pattern recognition algorithms: A review. European Journal of Operational Research, 120:459-495, 2000.
[8] L. Nieddu and G. Patrizi. Optimization and algebraic techniques for image analysis. In M. Lassonde, editor, Approximation, Optimization and Mathematical Economics, pages 235-242. Physica-Verlag, 2001.
[9] S. Watanabe. Pattern Recognition: Human ans MEchanical. Wiley, New York, 1985.
[10] H. S. Konjin. Statistical Theory of Sample Design and Analysis. North Holland, Amsterdam, 1973.
[11] G. J. McLachlan. Discriminant Analysis and Statistical Pattern Recognition. John Wiley & Sons 1992.
[12] S. Watanabe. Karhunen-Loéve expansion and factor analysis. Theoretical remarks and applications. Transactions of the fourth Prague conference on Information Theory, Statistical Decision functions, Random Processes, pp. 635-660, 1965.
[13] R. O. Duda and P. E. Hart. Pattern classification and scene analysis Wiley, New York, 1973.
[14] S. Watanabe. Pattern recognition: human and mechanical Wiley, New York 1985.
[15] P. Mccullagh and J. A. Nelder. Generalized Linear Models, second edition., Chapman and Hall, 1989
[16] T. Hastie, R. Tibshirani, J. H. Friedman. The elements of statistical Learning: Data mining, inference, and prediction. Second Edition., Springer, 2009
[17] V. Desai, J. Crook and G. Overstreet. Credit Scoring Models in the Credit Union Environment Using Neural Networks and Genetic Algorithms. Computer Journal of Mathematics Applied in Business and Industry, vol. 8 no. 4, pp. 324-346, 1997.
[18] R. Quinlan. Simplifying Decision Trees. International Journal of Man Machine Studies, vol. 27 no. 6, pp. 221-234, 1977.
[19] C. Thomas. A survey of credit and behavioral scoring: Forecasting financial risks of lending to customers. International Computer Journal of Forecasting, vol. 16, no. 2, pp. 149-172, 2000.
[20] A. Vellido, G. Lisbo and J. Vaughan. Neural Networks in Business: a Survey of Applications. In proceedings of Expert Systems with Applications, Australia, pp. 51-70, 1999.