Synthetic Cryptocurrency Transaction Generation for AI Fraud Detection
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Solution Overview
Problem
The massive and ever-increasing size of Bitcoin blockchain data poses a significant challenge for collecting, processing, and handling transactions, particularly for identifying suspicious and illicit activities due to the pseudo-anonymity of entities involved.
Innovation Solution
A processor-implemented method and system for generating behavior embedded entity-specific cryptocurrency transactions, which involves receiving a transaction schema, transforming it into a data frame, and generating sets of cryptocurrency transactions by parsing the data frame to identify unique entities and transaction types, creating addresses, initializing outer layer addresses, and performing iterative steps to check availability, compute values, and distribute them among output addresses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If real Bitcoin blockchain data is collected and processed, then training data for AI models can be obtained, but enormous infrastructure and computational resources are required
Solution Approach 1:
The patent generates synthetic cryptocurrency transaction data that copies the structural and behavioral characteristics of real Bitcoin blockchain transactions. By creating artificial transaction records with embedded entity behaviors and patterns, the system produces training data without needing to collect and process actual blockchain data, thereby reducing computational resource requirements while maintaining data quality for AI model training
Solution Approach 2:
The system pre-generates comprehensive training datasets with embedded entity behaviors and transaction patterns before AI model training begins. By creating synthetic transaction data in advance with realistic behavioral characteristics, the system eliminates the need for resource-intensive real-time data collection and processing during model development
2Loss of information
If real Bitcoin blockchain data is collected, then meaningful insights can be obtained, but the data collection and processing becomes a major challenge due to huge size
Solution Approach 1:
The patent creates synthetic transaction data that replicates the essential informational characteristics of real Bitcoin transactions, including entity behaviors, transaction patterns, and cryptographic structures. This copying approach preserves information quality needed for meaningful insights while avoiding the complexity of handling actual massive blockchain datasets
Solution Approach 2:
The system extracts only the essential behavioral patterns and structural characteristics from real transaction data to create synthetic representations. By taking out only the critical informational elements needed for analysis rather than processing complete raw blockchain data, the system maintains information quality while reducing data handling complexity
3Measurement precision
If more cryptocurrency transaction data is generated for training AI models, then detection accuracy improves, but data collection and management becomes increasingly difficult
Solution Approach 1:
The patent generates synthetic transaction data with embedded entity behaviors that improve AI model detection accuracy. By creating artificial datasets with controlled behavioral patterns and characteristics, the system achieves high measurement precision for illicit activity detection without the increasing difficulty of collecting and managing larger volumes of real transaction data
Data Source
AI summary
The huge size of ever-increasing cryptocurrency data makes investigating transactions for identifying fraudulent activities challenging. Conventional Artificial Intelligence (AI) models trained on a sample cryptocurrency transaction dataset are not scalable or efficient and getting required labelled data for training the AI models is a challenge due to the pseudo-anonymity of entities in cryptocurrency transactions. The present disclosure enables generating of patterned transactions pertaining to different entities using an input specification in the form of a transaction schema that describes one or more parameters including one or more entities, a quantity of cryptocurrency transactions, time frame; and a pattern describing a typology for the cryptocurrency transactions to be generated. The input specification is processed to simulate customizable and scalable training data characterized by the behavior or nature of entities seen in the real world and associated with different patterns including the money laundering patterns described in the input specification.