Core Engine: Gamification & Anti-Fraud Logic

1. Credit Allocation Algorithm

The platform utilizes Digital Credits (aka Engagement Points) to drive User Engagement Metrics. Credits are minted programmatically when users complete Micro-Interactions that successfully pass our multi-layer verification thresholds.

Scoring Formula:

Credit_Grant = Base_Rate * Difficulty_Weight * Quality_Score * Reputation_Multiplier * Time_Decay

Parameters:

  • Difficulty_Weight: interaction type + proof strictness.
  • Quality_Score: evidence confidence from verification.
  • Reputation_Multiplier: trust tier based on completion history.
  • Time_Decay: penalizes abnormal latency or burst patterns.

This model keeps the credit economy stable while scaling engagement.

2. Automated Verification Layer

The Automated Verification Worker validates evidence via AI/OCR and rule-based checks, enforcing deterministic gating before credits are released.

Evidence -> OCR -> Normalize -> Validate -> Confidence Score -> Credit Grant

Core controls:

  • OCR extraction on screenshots and UI artifacts.
  • Semantic validation of platform identifiers.
  • Cross-check against API telemetry when available.

3. Fraud Detection Protocol

Anomaly & Fraud Detection Algorithms detect spam, click farms, and replay attacks:

  • Rate limiting per device/account.
  • Cooldown windows to prevent burst submissions.
  • Reputation scoring tied to dispute ratio.
  • Post-audit sweeps to revoke invalid credits.
Signals -> Anomaly Scoring -> Quarantine -> Post-Audit -> Finalize

This protocol protects campaign integrity without degrading real-time UX.