Deep Learning in Risk Assessment
Credit evaluation models have been largely studied in the accounting and finance literature. With the support of such models, usually developed as part of a data mining process, it is possible to classify the credit applicants more accurately into ``good'' or ``bad'' risk groups. Despite many machine learning techniques have been extensively evaluated to this problem, deep learning models have been barely explored yet, although they have provided state-of-the-art results for a myriad of applications. In this paper, we propose deep learning models for the credit evaluation problem. To be specific, we investigate the abilities of deep neural networks (DNN) and convolutional neural networks (CNN) for such a problem and systematically compare their classification accuracy against five commonly adopted techniques on three real-world credit evaluation datasets. The results show that random forest, which is a state-of-the-art technique for such a problem, presented the most consistent performance, although CNN demonstrated a high potential to outperform it in bigger datasets.
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