Cryptocurrency Forensic Analysis System for Fraud Detection
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Solution Overview
Problem
Current systems lack effective methods to correlate fiat currency transactions with cryptocurrency transactions to prevent fraud, illicit activities, and regulatory compliance, particularly in identifying users involved in illicit cryptocurrency usage.
Innovation Solution
A method and system that obtain fiat-based transaction data, cryptocurrency exchange trade history data, and downstream cryptocurrency transaction data to identify matches and assign a risk score to users, utilizing linkages between fiat payments and cryptocurrency transactions to detect illicit activities and enforce regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fiat-based transaction data and cryptocurrency transaction data are correlated through forensic analysis, then the ability to detect fraudulent and illicit activities is improved, but the complexity of the system increases due to integrating multiple data sources and analysis methods
Solution Approach 1:
The patent combines multiple data sources (fiat-based transaction data, cryptocurrency exchange trade history data, and downstream cryptocurrency transaction data) into a unified forensic analysis system. This merging of disparate data types enables comprehensive fraud detection by correlating transactions across different financial systems, directly improving detection accuracy while managing system complexity through integrated processing.
Solution Approach 2:
The forensic analysis system performs multiple functions: it correlates fiat and cryptocurrency transactions, identifies illicit activities, assigns risk scores, and generates compliance reports. This multi-functionality allows a single system to address various fraud detection needs across different transaction types and regulatory requirements, improving reliability without proportionally increasing complexity.
2Measurement precision
If comprehensive transaction data from multiple sources is analyzed, then the precision of identifying illicit activities is improved, but the time and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing transaction data from multiple sources before actual forensic analysis. Cryptocurrency exchange trade history data and downstream transaction data are prepared in advance with key identifiers and relationships established, enabling faster correlation and identification during compliance investigations without sacrificing precision.
Solution Approach 2:
The system creates simplified representations or copies of complex transaction data structures that preserve essential identification information. By working with these streamlined data copies rather than full transaction datasets, the system maintains high identification precision for illicit activities while reducing computational overhead and analysis time.
Data Source
AI summary
Systems and methods of forensic analysis of cryptocurrency transactions are described herein. A method can include obtaining fiat-based transaction data from a bank account, identifying a purchase of a cryptocurrency from fiat-based transaction data and cryptocurrency exchange trade history data from a cryptocurrency exchange where the cryptocurrency was purchased, obtaining cryptocurrency-based transaction data that identifies downstream cryptocurrency transaction data where the cryptocurrency was transferred out of the cryptocurrency exchange; and scoring a user who purchased or used the cryptocurrency based on the fiat-based transaction data, the cryptocurrency exchange trade history data, and the cryptocurrency-based transaction data.