A reference guide covering 50 essential terms at the intersection of synthetic data generation, financial compliance, and regulatory technology. Each entry explains the concept, its relevance to synthetic data, and how Born Synthetic approaches handle it differently.
Synthetic Data Fundamentals
- Synthetic Data — Data generated algorithmically rather than collected from real events
- Born Synthetic Data — Synthetic data created from mathematical models with zero real data input
- Data Lineage — The origin, movement, and transformation history of a dataset
- Zero Lineage — Data with no traceable connection to real-world records
- Math-First Generation — Generating data from statistical distributions before AI enrichment
- AI Enrichment — Using language models to add contextual detail to synthetic profiles
- FORGE Mode — Zero-AI generation using only mathematical models and rules
- Synthetic Data Quality — Metrics and methods for evaluating generated data reliability
- Certificate of Origin — Documentation proving synthetic data provenance and generation method
Privacy & Data Protection
- Re-identification Risk — The probability of linking anonymized data back to real individuals
- K-Anonymity — A privacy model ensuring each record matches at least k-1 others
- Differential Privacy — Mathematical framework adding calibrated noise to protect individual records
- Data Masking — Replacing sensitive data with realistic but fictional values
- Pseudonymization — Replacing identifiers with artificial ones while maintaining reversibility
- Anonymization — Irreversible removal of identifying information from datasets
- Privacy Budget — The cumulative privacy cost of queries against a protected dataset
- Data Protection by Design — Building privacy safeguards into systems from inception
Financial Compliance (KYC/AML)
- KYC (Know Your Customer) — Regulatory process for verifying client identity and risk
- AML (Anti-Money Laundering) — Regulations and procedures to prevent financial crime
- EDD (Enhanced Due Diligence) — Elevated verification for high-risk customers
- CDD (Customer Due Diligence) — Standard process for assessing customer risk profiles
- PEP (Politically Exposed Person) — Individuals in prominent public positions requiring enhanced scrutiny
- SAR (Suspicious Activity Report) — Regulatory filing for potentially illegal financial transactions
- Beneficial Ownership — Identifying the real person who ultimately controls an entity
- Transaction Monitoring — Automated surveillance of financial transactions for suspicious patterns
- Sanctions Screening — Checking customers against government sanctions and watchlists
- Risk Scoring — Quantitative assessment of customer or transaction risk level
Wealth & Financial Profiles
- UHNWI (Ultra-High Net Worth Individual) — Individuals with investable assets exceeding $30 million
- Pareto Distribution — Power-law distribution modeling wealth concentration
- Wealth Distribution — Statistical patterns of how assets are distributed across populations
- Net Worth Identity Check — Verification that synthetic profile financials are internally consistent
- Archetype — A representative profile pattern used in synthetic data generation
- Cultural Onomastics — Name generation reflecting geographic and cultural origins
- Algebraic Constraints — Mathematical rules ensuring financial data internal consistency
- Offshore Wealth Structures — Multi-jurisdictional entities for wealth management and tax planning
- Private Banking — Personalized financial services for high-net-worth clients
- Family Office — Private wealth management firm serving ultra-wealthy families
- Sovereign Wealth — State-owned investment funds managing national reserves
Regulations
- GDPR Article 25 — EU requirement for data protection by design and by default
- EU AI Act Article 10 — Training data governance requirements for AI systems
- DORA — Digital Operational Resilience Act for EU financial entities
- PCI DSS 4.0 — Payment card industry standard prohibiting real card data in testing
- AMLD6 — Sixth EU Anti-Money Laundering Directive
- Training Data Governance — Frameworks for managing AI training data quality and compliance
Industry & Applications
- Compliance Testing — Validating systems meet regulatory requirements using test data
- Stress Testing — Evaluating system resilience under extreme financial scenarios
- Model Validation — Verifying AI/ML model accuracy and regulatory compliance
- Onboarding Simulation — Testing customer registration flows with synthetic profiles
- Fraud Detection — Systems identifying unauthorized or suspicious financial activity
- RegTech — Technology solutions for regulatory compliance management
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50 terms. Updated March 2026. New terms added as regulations evolve.
Learn more about synthetic data glossary and how Born Synthetic data addresses this in our glossary and comparison guides.
