Financial risk prediction framework in supply chain management using residual Conv-GRU and ensemble features

Rapid advancement in banking technology not only enhances productivity and improves people's lives but also introduces significant risks. This work proposes an effective framework to predict financial risk in Supply Chain Management (SCM). At first, the data needed to perform the process is taken from various data sources. Further, the feature extraction process with the help of Principal Component Analysis (PCA) and Restricted Boltzmann Machine (RBM), and also the statistical features are extracted from the inputted data. Moreover, the features PCA, RBM, and statistical features are fed to the weighted fused features phase, whereas the weights are tuned by Revised Archimedes Optimisation (RAO). Then, the fused features are inputted into the Residual Convolution Gated Recurrent Unit (Res-CGRU) for the prediction process. Finally, the suggested Res-CGRU model tends to show a predicted outcome. The effectiveness of designed approach is compared with several baseline systems to confirm its superiority over others.