Blockchain Risk Scoring Engine for Open-Participation Security
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Solution Overview
Problem
Current blockchain systems lack effective checks on member participation, allowing fraudulent users to join and potentially control the blockchain, leading to risks in transactions and contracts.
Innovation Solution
Implement a machine learning and artificial intelligence scoring engine within the blockchain to dynamically assess risk by analyzing user metadata, historical data, and external factors, providing a customized risk score for each transaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If blockchain allows open participation without identity verification, then decentralization and accessibility are improved, but security and fraud risk worsen
Solution Approach 1:
The patent introduces an analysis element as an intermediary component that sits between the open blockchain network and the transaction verification process. This analysis element receives transaction data, performs risk assessment using machine learning models, and provides security verification without blocking open participation. The intermediary enables both decentralization and security by adding a layer of intelligent analysis that operates independently from the core blockchain protocol.
Solution Approach 2:
The system performs preliminary risk assessment and analysis before transactions are fully processed and added to the blockchain. The analysis element evaluates transaction data, assesses potential fraud risks, and generates risk scores in advance of final transaction confirmation. This preliminary action allows the blockchain to maintain open participation while preventing fraudulent transactions from being committed.
2Reliability
If blockchain verifies transactions through multiple parties, then transaction security is improved, but system complexity worsens
Solution Approach 1:
The patent transforms the complexity of multi-party verification into a manageable parameter by introducing risk score thresholds. Instead of managing complex interactions between multiple verification parties, the system converts security assessment into a quantitative risk score parameter. Transactions are verified by comparing their risk scores against predetermined thresholds, simplifying the verification process while maintaining high security standards through automated, data-driven decision-making.
3Loss of information
If blockchain allows anonymous participation, then privacy and decentralization are improved, but fraud detection capability worsens
Solution Approach 1:
The patent replaces traditional mechanical identity verification systems with machine learning-based behavioral analysis. Instead of requiring identifiable information about participants, the system uses AI models to analyze transaction patterns, data characteristics, and behavioral indicators. This substitution enables fraud detection in anonymous environments by focusing on what transactions reveal about intent and risk rather than who initiates them, maintaining privacy while improving fraud detection capability.
Data Source
AI summary
A first node on the blockchain storage system node may include data from other blocks on the blockchain used for blockchain verification and an additional node which may include an analysis element. The analysis element may include computer executable code for receiving data added to the blockchain, determining a risk score for the data added to the blockchain based on past performance and in response to the risk score being over a threshold, alerting members of blockchain of the risk score.