Compliance Hybrid Licensing and Ownership Model Detailed Execution Plan for Phase 0
Objective
Establish the AI-driven compliance, fraud detection, and CaaS (Compliance-as-a-Service) backbone that powers all future phases of CHLOM™.
Core Components
1. AI & ML Models
- Fraud Detection Algorithms:
- Transactional anomaly detection.
- Behavioral anomaly mapping.
- Identity spoofing prevention.
- Compliance Scoring Algorithms:
- Jurisdictional regulatory match.
- Entity reputation tracking.
- Renewal timeliness & compliance streaks.
- Predictive Analytics:
- License risk assessment.
- Preemptive compliance breach alerts.
2. Compliance-as-a-Service (CaaS) Framework
- Core Infrastructure:
- Standardized compliance API endpoints.
- Microservices architecture for verification, scoring, and enforcement.
- Multi-tenant architecture for regulator, auditor, and enterprise access.
- Access Controls:
- Role-based permissioning.
- Regulator dashboard.
- Enterprise compliance monitoring portal.
3. Data Strategy
- Data Acquisition:
- Global regulatory datasets.
- Sanctions & watchlists.
- Industry-specific licensing databases.
- Data Governance:
- Bias detection and mitigation protocols.
- Continuous dataset validation.
- Secure, privacy-first storage.
4. Fraud Algorithm Stack
- Pattern Recognition:
- Graph-based network mapping for fraud rings.
- Relationship & transaction clustering.
- License Duplication Prevention:
- Cryptographic signature checks.
- AI license fingerprinting.
- Risk-Based Authentication:
- Multi-factor triggers based on risk score.
5. Testing Infrastructure
- Compliance Sandbox:
- Model validation in simulated environments.
- Fraud Injection Testing:
- Controlled anomaly creation to train and stress-test AI.
- Feedback Loop:
- Automated retraining from real-world incidents.
Workstreams Required for Phase 0 Completion
- AI/ML Model Development: Build, train, and optimize at least 3–5 specialized models.
- Compliance Framework Engineering: Develop core CaaS infrastructure.
- Fraud Algorithm Development: Implement full fraud detection stack.
- Data Acquisition & Governance: Curate, clean, and maintain all datasets.
- Sandbox & Testing Environment Setup: Deploy simulation and QA pipelines.
- API Development: Create integration points for future blockchain and enterprise use.
Phase 0 Outputs
- Fully functional AI/ML compliance intelligence layer.
- CaaS platform with documented API endpoints.
- Fraud detection and compliance scoring models deployed.
- Compliance sandbox ready for Phase 1 blockchain integration.
Contact: Kavonte Jones Sr. — Founder, CHLOM™ Email: [email protected] Website: CHLOM.io