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The significant development of credit industry led to growing interest in sophisticated methods which can support making more accurate and more rapid credit decisions. The parametric statistical methods such as linear discriminant analysis and logistic regression were soon followed up by nonparametrical methods and other techniques: neural networks, decision trees, and genetic algorithms. This paper investigates the affinity set – a new concept in data mining field. The affinity set model was applied to credit applications database from Poland. The results are compared to those received by Rosetta (the rough sets and genetic algorithm procedure) and logistic regression.

CREDIT SCORING MODEL BASED ON THE AFFINITY SET