Convolutional Neural Network based Real-Time Risk Management: Mitigating Business Threats with Instant Analytics

Real-time risk management is an essential key to businesses to anticipate risks before causing significant damage and maintaining the sustainability of their operations. This study demonstrates the Cascaded CNN with Self Attention Model (CCSAM) as a new technique for real-time analytics on business risk detection and handling. This approach involves collecting a wide range of data points from a number of business activities, cleaning them with very complex preparation steps in preprocessing and feature engineering to make them suitable for the models. The CCSAM combines Convolutional Neural Networks (CNNs) at different levels for hierarchical feature extraction and the effectiveness of the self-attention mechanisms in channel-specific attention. Using a comprehensive model design and evaluation, the CCSAM far outperformed the others in correctly predicting and then managing in real-time business risks with 97% accuracy. The capability of the new model is to handle complex data structures and analyze them in dynamic business ecosystem provide an opportunity for proactive risk management at an unprecedented scale.