Application of VUCA in Business Transactions

Abstract

Business transactions are a fundamental part of any business or organization, and they play a crucial role in tracking and managing financial activities, maintaining transparency, and ensuring compliance with legal and regulatory requirements. The previous work suggests four stages. The relevant data is collected in this stage. This procedure enhances integrity in the system. It aims to predict the model's accuracy. The collected details are considered as training information. The pre-processing phase does the feature extraction, based on what is expected as the outcome. The feature set is created considering the problem to be addressed. The errors like inconsistencies, redundancy, and missing data are removed. Model building is constructed using machine learning techniques. Model training and testing is divided into two sets. The training dataset is constructed by analyzing the preliminary data input. The test dataset is fed into the system, and the training dataset is compared with the test data to make predictions. The suggestion uses a backpropagation algorithm to make the prediction.