An Evolutionary Algorithm For Imbalanced Credit Risk Evaluation Using Neural Network

Abstract

Credit risk management is a method of removing all possible risk factors affecting transactions of some kind. It is a widespread trend which inability to recognize relationships among customer groups contributes to elevated credit risks from financial institutions, includes multi-end or surplus credit, or wrongly distributes credit line volumes to the customer community. The proposed neural network is of very small scale because of its bi-projection form. In the supervised learning method, the imbalanced data set also becomes an obstacle. The imbalance is the situation in that the portrayal of training data belonging to one class outweighs the other class cases. Synthetic Minority Oversampling Technique (SMOTE) is a well-known over-sampling method that addresses imbalances in the data level. SMOTE synthetically contrasts two closely related vectors. Optimization is constrained by the lack of full knowledge and by the optimization phase lack of time to determine what information is available. In this paper, we have used an evolutionary technique named Adaptive differential evolution which replaces the PSO algorithm due to some limitations and henceforth we achieved higher accuracy than that of PSO. We used MATLAB as our simulation tool.