Graph Feature System for Cash Flow Prediction Accuracy
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Solution Overview
Problem
Current methods for predicting cash flow in banks face challenges due to the dynamic nature of transaction graphs, scalability issues, and the difficulty in handling large, scale-free graphs with domain-specific attributes, leading to inaccurate predictions and increased costs.
Innovation Solution
A graph feature-based system using a shared-memory graph analysis platform that generates snapshot graphs, computes internal flow probabilities, and applies a Markov process approximation to improve cash flow prediction accuracy, integrating with time series analysis and supporting vertex-centric, bulk synchronous parallel programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional time series analysis is used for cash flow prediction, then the method is simple to implement, but the prediction accuracy is insufficient due to inability to capture complex transaction patterns
Solution Approach 1:
The patent introduces graph neural networks as an intermediary between traditional time series analysis and cash flow prediction. The GNN models transform transaction data into graph representations where nodes represent accounts and edges represent transactions, enabling the system to capture complex relational patterns while maintaining a structured prediction framework that builds upon traditional time series methods
Solution Approach 2:
The patent creates a composite prediction system that combines multiple approaches: graph neural networks for capturing transaction patterns, attention mechanisms for weighting important features, and traditional time series analysis for temporal modeling. This composite approach leverages the strengths of each method to achieve superior prediction accuracy while managing system complexity through modular architecture
2Measurement precision
If graph-based analysis is applied to capture complex transaction patterns, then prediction accuracy improves, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the transaction graph into subgraphs based on account relationships and transaction types. The graph neural network processes these smaller subgraphs independently and aggregates the results, reducing the computational burden of analyzing the entire graph while preserving important local patterns that affect cash flow predictions
Solution Approach 2:
The patent performs preliminary processing of transaction data by pre-computing graph representations, pre-training the graph neural network on historical data, and pre-identifying important account relationships. This preliminary action reduces the computational complexity during actual prediction operations, enabling faster processing while maintaining high accuracy
3Measurement precision
If detailed transaction data is analyzed to improve prediction accuracy, then the prediction model becomes more accurate, but data privacy risks and security concerns increase
Solution Approach 1:
The patent extracts only the essential features needed for prediction from detailed transaction data, such as account relationship structures, transaction frequency patterns, and flow directions. By taking out only these critical elements and discarding sensitive details like specific amounts and personal identifiers, the system maintains prediction accuracy while reducing privacy risks
Solution Approach 2:
The patent creates anonymized copies of transaction data for analysis, where sensitive information is replaced with pseudonyms or aggregated statistics. These synthetic representations preserve the structural patterns and relationships needed for accurate prediction while eliminating direct links to individual customers, thereby protecting privacy during the modeling process
Data Source
AI summary
Input data is obtained and converted into a graph. Based on the converted input data, a time series of snapshot graphs is generated by selecting edges whose timestamps are in a given time window for each of a plurality of time windows, each edge having a corresponding attribute. Internal flow probabilities are computed for each snapshot graph of the time series of snapshot graphs and a system is controlled based on the internal flow probabilities.