Research on the Prediction of Nonbreakeven Financial Products' Yield of Commercial Banks Based on Machine Learning

Abstract

Bank wealth management solutions have now become one of the most important components of the financial industry after nearly two decades of continuous development. However, there are still problems such as an imperfect pricing model and an ambiguous pricing mechanism. In this paper, we use machine learning to predict the yield of nonguaranteed financial products, and after model training and prediction, both the random forest model and the LightGBM model have high applicability; that is, machine learning can be effectively used in the yield forecasting process.