6.3 Zesh AI ID

Zesh AI ID is the backbone of Zesh AI’s fraud prevention and participant validation system. Designed to ensure authenticity and trust in Web3 campaigns, Zesh AI ID leverages advanced AI technologies to detect and eliminate fraudulent activity, such as bots and Sybil attacks.

By scoring participants and providing transparent fraud reports, Zesh AI ID creates a secure and equitable environment for projects, KOLs, and communities.

Core Features

1. Fraud Detection & Sybil Protection:

• Real-Time Analysis: Continuously monitors participant activity during campaigns to identify anomalies indicative of fraudulent behavior.

• Behavioral Pattern Analysis: AI evaluates engagement consistency, task completion times, and interaction patterns to flag suspicious accounts.

• Network Mapping: Identifies clusters of wallets with interconnected behavior, signaling potential Sybil networks.

• Automated Response: Removes or restricts flagged accounts, ensuring campaign integrity without manual intervention.

2. Participant Scoring:

• ZeshScore for Participants:

  • Assigns a dynamic score to each participant based on authenticity, engagement quality, and contribution value.

  • Scores evolve in real-time, reflecting changes in behavior and activity.

• Scoring Metrics Include:

  • Task completion consistency.

  • Contribution quality (e.g., original versus copy-paste responses).

  • Frequency and diversity of interactions.

• High-Value Contributor Highlighting:

  • Identifies top-performing participants for additional rewards or future ambassador programs.

3. Fraud Monitoring & Reporting:

• Comprehensive Fraud Reports:

  • Details of flagged accounts and the nature of their suspicious activity.

  • Overview of fraud prevention metrics, such as the percentage of accounts removed.

  • Impact analysis showing how fraud prevention improves campaign engagement.

• Customizable Alerts:

  • Projects receive real-time alerts for high-risk activity.

  • Configurable thresholds for fraud detection based on campaign needs.

How Zesh AI ID Works

• Data Collection: Gathers data from participant interactions across platforms (e.g., X, Telegram, Discord) and on-chain activity.

• AI Analysis: Uses Machine Learning algorithms to identify patterns indicative of bot activity, Sybil accounts, and low-quality engagement.

• Analyzes historical and real-time data for comprehensive fraud prevention. Validation & Scoring:

• Validates participants by cross-referencing behavior with authenticity markers. Assigns ZeshScores and highlights top contributors.

• Fraud Prevention Actions: Automatically restricts or removes suspicious accounts.

• Provides transparent reports to ensure project confidence and community trust.

Use Cases

For Web3 Projects:

• Secure Campaigns: Ensure only genuine participants are rewarded, maintaining campaign integrity.

• Transparency: Gain access to detailed reports on participant authenticity and engagement quality.

• Enhanced ROI: Prevent fraud-related resource wastage by focusing on authentic contributors.

For KOLs:

• Authenticity Validation: Ensure followers engaging in campaigns are genuine.

• Credibility Enhancement: Demonstrate the quality of referred participants through fraud-free campaigns.

For Communities:

• Fair Rewards: Receive recognition and rewards based on authentic participation.

• Safe Environment: Participate in a secure ecosystem free from bots and fraudulent accounts.

Benefits of Zesh AI ID

• Fraud-Free Engagement: Detects and eliminates fraudulent activity, ensuring campaigns are authentic and effective.

• Dynamic Participant Scoring: Highlights high-value contributors, enabling projects to incentivize genuine participants effectively.

• Enhanced Trust: Builds confidence in campaigns by providing transparent fraud prevention metrics.

• Time Efficiency: Automates fraud detection and participant validation, saving time for project teams.

• Seamless Integration: Works across all Zesh AI platforms, ensuring a unified and secure ecosystem.

Technical Details

AI Technologies Used:

• Machine Learning for anomaly detection and pattern recognition.

• Network Graph Analysis for identifying Sybil networks.

• Natural Language Processing (NLP) for detecting spam or copy-paste responses.

Data Sources:

• Social platforms (e.g., X, Telegram, Discord).

• On-chain data for wallet interactions.

• Historical campaign data for comparative analysis.

Real-Time Monitoring:

• Continuous analysis ensures that fraud is detected and addressed immediately.

Zesh AI ID ensures that every campaign within the Zesh AI ecosystem is authentic, secure, and equitable. By combining advanced AI-driven fraud detection with dynamic participant scoring, it sets a new standard for trust and transparency in Web3 engagement.