Variational Neural Network Optimized with Lyrebird Optimization Algorithm for Analysis and Empirical Evidence of Using Data Mining in Financial Risk Prevention Research

Abstract

In the contemporary landscape of global finance, the effective prevention and mitigation of financial risks have become pivotal for the stability, resilience, and sustainable growth of financial institutions and markets. As financial instruments and markets continue to evolve in complexity, the need for a thorough analysis of risk prevention strategies has become increasingly apparent. In this manuscript, Variational Neural Network (VNN) optimized with lyrebird optimization algorithm (VNN-LOA) is proposed. Initially data is taken from machinehack-financial risk prediction. Afterward the data is fed to Adaptive Variational Bayesian Filter (VBF) based pre-processing process. The pre-processing output is provided to the Variational Neural Network (VNN) to effectively classify financial risk prevention as either involving risk or no risk. The learnable parameters of the VNN are optimized using lyrebird optimization algorithm (LOA). The proposed method is implemented in MATLAB and the efficiency of the proposed method DM-FRP-VNN-COA is estimated with the help of several performances evaluating metrics like, accuracy, precision, recall, f1-score, sensitivity, specificity, computational time, and ROC are analyzed. The proposed DM-FRP-VNN-LOA method attains 38.88%, 35.75%, and 33.16% higher accuracy for risk classification; 34.31%, 38.47% and 37.23% higher accuracy for no risk classification; 22.63%, 33.27% and 21.49% high precision for risk classification; 22.63%, 33.27% and 21.49% high precision for no risk; 35.136%, 39.04% and 38.81% lower computation Time compared with the existing method like data mining using financial risk prevention based back propagation neural network (DM-FRP-BPNN), data mining using financial risk prevention based machine learning (DM-FRP-ML), and data mining using financial risk prevention based convolutional neural network (DM-FRP-CNN) respectively.