CREDIT SCORING WITH AN ENSEMBLE DEEP LEARNING CLASSIFICATION METHODS – COMPARISON WITH TRADITIONAL METHODS
CREDIT SCORING WITH AN ENSEMBLE DEEP LEARNING CLASSIFICATION METHODS – COMPARISON WITH TRADITIONAL METHODS

Summary/Abstract: Credit scoring attracts special attention of financial institutions. In recent years, deep learning methods have been particularly interesting. In this paper, we compare the performance of ensemble deep learning methods based on decision trees with the best traditional method, logistic regression, and the machine learning method benchmark, support vector machines. Each method tests several different algorithms. We use different performance indicators. The research focuses on standard datasets relevant for this type of classification, the Australian and German datasets. The best method, according to the MCC indicator, proves to be the ensemble method with boosted decision trees. Also, on average, ensemble methods prove to be more successful than SVM.

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