2009 | OriginalPaper | Buchkapitel
Improving Personal Credit Scoring with HLVQ-C
verfasst von : Armando Vieira, João Duarte, Bernardete Ribeiro, Joao Carvalho Neves
Erschienen in: Advances in Neuro-Information Processing
Verlag: Springer Berlin Heidelberg
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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.