A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced data
This addresses a common but understudied challenge in industries like finance, though it is incremental as it primarily compares existing methods.
The paper tackled the problem of applying deep learning to highly imbalanced credit scoring data with limited samples, finding that deep learning methods performed competitively but not always superior to traditional machine learning algorithms in this context.
Training models on highly unbalanced data is admitted to be a challenging task for machine learning algorithms. Current studies on deep learning mainly focus on data sets with balanced class labels or unbalanced data, but with massive amount of samples available, like in speech recognition. However, the capacities of deep learning on imbalanced data with little samples is not deeply investigated in literature, while it is a very common application context in numerous industries. To contribute to fill this gap, this paper compares the performances of several popular machine learning algorithms previously applied with success to unbalanced data set with deep learning algorithms. We conduct those experiments on a highly unbalanced data set, used for credit scoring. We evaluate various configuration including neural network optimization techniques and try to determine their capacities when they operate with imbalanced corpora.