Cryptocurrency Asset Classification Using Spanning Tree Fraud Metrics
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Solution Overview
Problem
Current methods for detecting cryptocurrency pyramid schemes are ineffective due to asset issuers' deceptive practices and lack of specificity in differentiating between pyramid and Ponzi schemes, leading to unsatisfactory detection and a lack of nuanced understanding of pyramid schemes.
Innovation Solution
A computer-implemented method using a classification model that analyzes transaction records to generate a spanning tree and calculate metrics, distinguishing between pyramid and non-pyramid schemes by leveraging blockchain data analytics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If financial data-based detection methods are used to identify pyramid schemes, then detection coverage is increased, but detection precision deteriorates due to sophisticated data falsification by asset issuers
Solution Approach 1:
The patent introduces blockchain transaction records as an intermediary data source that is difficult to falsify, serving as a mediator between the detection system and the asset issuance process. By analyzing actual on-chain transactions rather than relying solely on self-reported financial data, the system achieves more precise detection while maintaining broad coverage.
Solution Approach 2:
The patent replaces traditional financial data analysis methods with blockchain-based detection mechanisms. Instead of relying on manual examination of financial reports that can be manipulated, the system uses automated analysis of immutable blockchain transaction records, fundamentally changing how pyramid schemes are detected and improving both precision and coverage.
2Difficulty of detecting and measuring
If text mining methods are used to analyze ICO white papers, then detection capability is enhanced, but effectiveness deteriorates due to targeted deception and identity-based false information
Solution Approach 1:
The patent uses blockchain transaction records as an intermediary that provides objective, unverifiable evidence of actual money flow and participant behavior. This intermediary data source is not susceptible to the same deception tactics as text-based white papers, as it records actual transactions rather than claimed intentions.
Solution Approach 2:
The patent creates a digital copy of the actual transaction history on the blockchain, which serves as an immutable record of what actually happened during the ICO. This copy cannot be altered or deceived, providing reliable data for detection regardless of how the original white paper or financial reports are manipulated.
3Speed
If Ponzi scheme detection methods are applied to identify pyramid schemes, then detection speed is improved, but accuracy deteriorates due to fundamental differences between Ponzi and pyramid scheme structures
Solution Approach 1:
The patent segments the detection approach by separately analyzing the hierarchical distribution structure characteristic of pyramid schemes, rather than applying a one-size-fits-all Ponzi scheme detection method. By segmenting the analysis to focus on multi-level marketing patterns, the system achieves both speed and accuracy specific to pyramid scheme detection.
Solution Approach 2:
The patent applies local quality by tailoring the detection metrics and analysis methods specifically to the hierarchical structure of pyramid schemes, rather than using generic fraud detection approaches. This localized approach ensures that the detection system is optimized for the specific characteristics of pyramid schemes, improving both accuracy and speed.
4Reliability
If manual examination and regulatory oversight are used to detect pyramid schemes, then detection reliability is maintained, but productivity deteriorates due to time-consuming analysis processes
Solution Approach 1:
The patent enables the blockchain network itself to provide detection capabilities through automated analysis of its own transaction records. The system serves itself by using the inherent data structure of blockchain to automatically identify pyramid scheme patterns, eliminating the need for manual examination while maintaining high reliability through the immutable nature of the data.
Solution Approach 2:
The patent replaces manual examination processes with automated computational analysis of blockchain data. By substituting human review with algorithmic detection, the system dramatically improves productivity while maintaining or enhancing reliability through consistent, objective analysis of transaction patterns.
Data Source
AI summary
A computer-implemented method of classifying a cryptocurrency asset, comprising receiving a plurality of sample transaction records for the asset; generating a spanning tree representing connections between users in the transaction records; calculating a plurality of metrics relating to the generated spanning tree; and using a classification model to analyze the calculated metrics and assign the asset to first classification which indicates the asset is a suspected pyramid scheme or a second classification which indicates the asset is not a suspected pyramid scheme.