Loan classification using a deep feed-forward neural network

A deep feed-forward neural network model is developed and analyzed in this article to solve the financial loan classification problem. Using this model, based on historical data on previously issued loans, the values of the following traditional machine learning metrics that determine the quality of forecasting are calculated: cost function, truth, accuracy, completeness and F 1 measure. In order to obtain greater forecasting accuracy, optimization methods of mini-batch gradient descent, gradient descent with momentum, adaptive momentum estimation, and zero-level elimination method were used. An improved structure of the proposed neural network was determined, the impact of the so-called He initialization on the final result was analyzed, as well as the efficiency of using specific optimization algorithms. The study showed that the use of deep feed-forward neural network is reasonable in developing loan classifiers.