Transaction Graph Node Embedding via Directed Acyclic Subgraphs

The patent replaces manual mechanical analysis with automated machine learning models. The system uses trained embedding models to automatically convert transaction composition graphs into feature vectors and perform risk assessment, eliminating the need for manual analysis while maintaining high accuracy through sophisticated neural network architectures.

The system enables self-service automated analysis where the machine learning models independently process transaction composition graphs without human intervention. The models automatically learn from training data, generate feature vectors, and produce risk assessments, allowing the system to serve itself and eliminate dependency on manual AI assistance.

The patent segments the complex transaction composition graph into individual nodes and edges, processing them separately through the embedding model. Each node is converted into a feature vector independently, and relationships are captured through edge features. This segmentation reduces computational complexity by breaking down the overall complex transformation into manageable smaller operations.

The system transforms complex transaction data from graph structure into vector space representations, adding a mathematical dimension for computation. By mapping nodes and edges to feature vectors in multidimensional space, the system enables efficient similarity calculations and risk assessments using standard vector operations, simplifying the overall computational process.

The patent designs embedding models with universal applicability across different transaction types and domains. The models learn general patterns from training data that can be applied to various transaction composition graphs, reducing the need for domain-specific training data. The same model architecture and training approach can be reused across different applications, minimizing overall training data requirements.

The system performs preliminary training of embedding models on representative transaction data before deployment. By pre-training the models with sufficient data upfront, the system achieves high analysis efficiency during operation without requiring continuous large-scale training. The preliminary action of model training consolidates the data requirement into an initial phase, allowing efficient automated analysis thereafter.