A Comprehensive Risk Nonlinear Relationship Mining Algorithm for Commercial Banks Based on Multi task Learning

This study proposes a comprehensive risk mining algorithm based on multi task learning to address the multidimensional and nonlinear risk relationships faced by commercial banks in their operational processes. This algorithm constructs a multi task learning framework to jointly learn different types of risks faced by commercial banks as multiple related tasks. Using a BP neural network, all standard values are used as input nodes for the model, and the data is normalized. Using the linear proportional transformation method to convert attribute information into dimensionless information, actively adding input vector complements through the network input layer to improve the shortcomings of the initial BP algorithm, a three-layer BP neural network comprehensive risk nonlinear relationship mining model for commercial banks is obtained. The experimental results show that the algorithm has achieved good predictive performance on multiple risk tasks, with higher accuracy and efficiency. This algorithm not only provides comprehensive and accurate risk assessment results for commercial banks, but also helps banks better avoid risks, optimize resource allocation, and improve operational efficiency, which has important practical application value.