Abstract
In this paper we study personal credit scoring using several machine learning algorithms: Multilayer Perceptron, Logistic Regression, Support Vector Machines, AddaboostM1 and Hidden Layer Learning Vector Quantization. The scoring models were tested on a large dataset from a Portuguese bank. Results are benchmarked against traditional methods under consideration for commercial applications. A measure of the usefulness of a scoring model is presented and we show that HLVQ-C is the most accurate model. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Vieira, A., Duarte, J., Ribeiro, B., & Neves, J. C. (2009). Improving Personal Credit Scoring with HLVQ-C. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5507 LNCS, pp. 97–103). https://doi.org/10.1007/978-3-642-03040-6_12
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