Internet Financial Risk Assessment Based on Hybrid Neural Network Algorithm

High occurrence rate, complex types, powerful contagion, and considerable risk repercussions are characteristics of internet financial risks. One of the most important aspects of financial risk control is building an online system for assessing financial risk. The current body of assessment research only offers a restricted mix of quantitative evaluation models and a broad and varied indicator system, which is unable to satisfy both the objective standards of the models and the comprehensiveness of the indicators at the same time. Through empirical analysis, this study shows that the particle swarm optimization convolutional neural network model has the highest predictive accuracy and can effectively capture the dynamics of Internet financial risks. It also completely considers the risk manifestations of both Internet characteristics and financial nature.