Towards a Framework for Supply Chain Financing for Order-Level Risk Prediction: An Innovative Stacked A-GRU Based Technique

Order financing is changing the game in the banking and financial supply chain industry. It's great for SMEs and opens up new revenue streams for logistics and finance companies. But in order to find the weak spots offered by banks and other financial institutions, companies need to undertake thorough risk assessments right now. Careful timing is crucial for training the model, extracting features, and preprocessing. Outlier identification and missing value handling are the first steps in preprocessing, which also includes normalization and standardization to improve data integrity and reduce unit discrepancies. Principal component analysis makes use of multivariate statistics to aid in feature extraction, guaranteeing effective data representation. Careful consideration of every detail is required during the training of a Stacked-A-GRU model, which follows attribute selection. Impressively outperforming state-of-the-art algorithms SAFE and GRU, the suggested solution achieves a remarkable correctness rating of 97.34%, indicating notable progress in predicting accuracy.