An Artificial Neural Network-Based Intelligent Prediction Model for Financial Credit Default Behaviors

Abstract

With the rapid development of intelligent techniques, smart finance has become a hot topic in daily life. Currently, financial credit is facing increasing business volume, and it is expected that investigating the intelligent algorithms can help reduce human labors. In this area, the prediction of latent credit default behaviors can help deal with loan approval affairs, and it is the most important research topic. Machine learning-based methods have received much attention in this area, and they can achieve proper performance in some scenarios. However, machine learning-based models cannot have resilient objective function, which can cause failure in having stable performance in different problem scenarios. This work introduces deep learning that has the objective function with high freedom degree, and proposes an artificial neural network-based intelligent prediction model for financial credit default behaviors. The whole technical framework is composed of two stages: information encoding and backbone network. The former makes encoding toward initial features, and the latter builds a multi-layer perceptron to output prediction results. Finally, the experiments are conducted on a real-world dataset to evaluate the efficiency of the proposed approach.