6533b838fe1ef96bd12a3b1a

RESEARCH PRODUCT

An Application of Hybrid Models in Credit Scoring

Ignacio OlmedaRosa PuertasMaría Bonilla

subject

Artificial neural networkComputer sciencebusiness.industryNonparametric statisticsMachine learningcomputer.software_genreCredit cardEmpirical researchHybrid systemBankruptcy predictionBond credit ratingArtificial intelligencebusinesscomputerParametric statistics

description

The predictive capability of parametric and non-parametric models in solving problems related to financial classification has been widely proved in empirical research carried out in the financial field, particulary in problems like bond rating, bankruptcy prediction and credit scoring. However, recently, it has been shown that a combination of different models generally reduces the prediction error, so that the best alternative to consider may not be a specific model but a combination of them. In this paper, we study hybrid systems based on the aggregation of individual (parametric and nonparametric) models. Our hybrids are built by using both parametric and non parametric models as the system aggregation. We present an example of this procedure on the problem of classifying credit card applicants.

yearjournalcountryeditionlanguage
2000-01-01
https://doi.org/10.1007/978-3-642-57652-2_5