AI Graph Search for Blockchain Money Flow Tracing
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Solution Overview
Problem
The decentralized nature of cryptocurrencies makes it difficult to trace the origin and destination of transactions, leading to challenges in identifying illicit activities such as money laundering and terrorism financing, which existing technologies have not adequately addressed.
Innovation Solution
A system and method utilizing artificial intelligence and machine learning to trace money flow between digital account addresses by receiving blockchain transaction data, applying intelligence labels, and using an AI graph search algorithm to determine suspicious transactions, thereby generating reports and taking appropriate actions such as alerting users or suspending transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If blockchain transactions are traced manually or using traditional methods, then the ability to identify suspicious transactions is limited, but the system complexity and cost increase significantly
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between raw blockchain transaction data and regulatory compliance requirements. This AI system processes and analyzes transaction patterns, automatically identifying suspicious activities without requiring complex manual analysis systems.
Solution Approach 2:
The patent replaces traditional mechanical/manual transaction analysis methods with AI-based automated analysis. Instead of relying on human analysts or simple rule-based systems, the invention uses machine learning algorithms to detect patterns and identify suspicious transactions, significantly improving accuracy while reducing system complexity.
2Reliability
If traditional transaction monitoring methods are used, then implementation cost is lower, but the ability to comply with AML and CFT regulations is insufficient
Solution Approach 1:
The patent implements a self-service system where the AI automatically monitors, analyzes, and flags suspicious transactions without requiring extensive human intervention. The system serves itself by continuously learning from new data and improving its detection capabilities, ensuring ongoing regulatory compliance while maintaining high investigative efficiency.
Solution Approach 2:
The patent incorporates feedback mechanisms where the AI system continuously learns from identified suspicious transactions and regulatory outcomes. This feedback loop improves the system's ability to detect compliance violations over time, enhancing both reliability and productivity without requiring proportional increases in resources.
3Measurement precision
If comprehensive transaction analysis is performed on all blockchain transactions, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing AI analysis only on transactions that exhibit certain risk indicators or patterns, rather than analyzing every single transaction comprehensively. This selective approach maintains high detection accuracy for suspicious activities while significantly reducing overall processing time and computational resource requirements.
Solution Approach 2:
The patent segments the transaction analysis process into multiple stages: initial filtering, risk assessment, and detailed analysis. Only transactions that pass through the filtering stages and show potential suspicious patterns undergo comprehensive AI analysis, thereby maintaining detection accuracy while minimizing time loss on benign transactions.
Data Source
AI summary
A method and apparatus that receive blockchain transaction data from a blockchain ledger to a transaction database, receive intelligence labels from a blockchain ecosystem intelligence database, select a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, receive input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred, apply the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, apply an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, and generate a report including tracing destination summary statistics of the auto-traced path.