Research: System Improvements for Enhanced Realism
This research document series analyzes the current SyntheticData system and proposes comprehensive improvements across multiple dimensions to achieve greater realism, statistical validity, and domain authenticity.
Document Index
| Document | Focus Area | Priority |
|---|---|---|
| 01-realism-names-metadata.md | Names, descriptions, metadata realism | High |
| 02-statistical-distributions.md | Numerical and statistical distributions | High |
| 03-temporal-patterns.md | Temporal correctness and distributions | High |
| 04-interconnectivity.md | Entity relationships and referential integrity | Critical |
| 05-pattern-drift.md | Process and pattern evolution over time | Medium |
| 06-anomaly-patterns.md | Anomaly detection and injection patterns | High |
| 07-fraud-patterns.md | Fraud typologies and detection scenarios | High |
| 08-domain-specific.md | Industry-specific enhancements | Medium |
Executive Summary
Current State Assessment
The SyntheticData system is a mature, well-architected synthetic data generation platform with strong foundations in:
- Deterministic generation via ChaCha8 RNG with configurable seeds
- Domain modeling with 50+ entity types across accounting, banking, and audit domains
- Statistical foundations including Benford’s Law, log-normal distributions, and temporal seasonality
- Referential integrity through document chains, three-way matching, and intercompany reconciliation
- Standards compliance with COSO 2013, ISA, SOX, IFRS, and US GAAP frameworks
Key Improvement Themes
After comprehensive analysis, we identify eight major improvement themes:
1. Realism in Names & Metadata
Current Gap: Generic placeholder names, limited cultural diversity, simplistic descriptions Impact: Immediate visual detection of synthetic nature Effort: Medium | Value: High
2. Statistical Distribution Enhancements
Current Gap: Single-mode distributions, limited correlation modeling, no regime changes Impact: ML models trained on synthetic data may not generalize Effort: High | Value: Critical
3. Temporal Pattern Sophistication
Current Gap: Static multipliers, no business day calculations, limited regional calendars Impact: Unrealistic transaction timing patterns Effort: Medium | Value: High
4. Interconnectivity & Relationship Modeling
Current Gap: Shallow relationship graphs, limited network effects, no behavioral clustering Impact: Graph-based analytics yield unrealistic structures Effort: High | Value: Critical
5. Pattern & Process Drift
Current Gap: Limited drift types, no organizational change modeling, static processes Impact: Temporal ML models overfit to stable patterns Effort: Medium | Value: High
6. Anomaly Pattern Enrichment
Current Gap: Limited anomaly correlation, no multi-stage anomalies, binary labeling Impact: Anomaly detection models lack nuanced training data Effort: Medium | Value: High
7. Fraud Pattern Sophistication
Current Gap: Isolated fraud events, limited collusion modeling, no adaptive patterns Impact: Fraud detection systems miss complex schemes Effort: High | Value: Critical
8. Domain-Specific Enhancements
Current Gap: Generic industry modeling, limited regulatory specificity Impact: Industry-specific use cases require extensive customization Effort: Medium | Value: Medium
Implementation Roadmap
Phase 1: Foundation (Q1)
- Culturally-aware name generation with regional distributions
- Enhanced amount distributions with mixture models
- Business day calculation utilities
- Relationship graph depth improvements
Phase 2: Statistical Sophistication (Q2)
- Multi-modal distribution support
- Cross-field correlation modeling
- Regime change simulation
- Network effect modeling
Phase 3: Temporal Evolution (Q3)
- Organizational change events
- Process evolution modeling
- Adaptive fraud patterns
- Multi-stage anomaly injection
Phase 4: Domain Specialization (Q4)
- Industry-specific regulatory frameworks
- Enhanced audit trail generation
- Advanced graph analytics support
- Privacy-preserving fingerprint improvements
Metrics for Success
Realism Metrics
- Human Detection Rate: % of samples correctly identified as synthetic by domain experts
- Statistical Divergence: KL divergence between synthetic and real-world distributions
- Temporal Correlation: Autocorrelation alignment with empirical baselines
ML Utility Metrics
- Transfer Learning Gap: Performance delta when models trained on synthetic data are applied to real data
- Feature Distribution Overlap: Overlap coefficient for key feature distributions
- Anomaly Detection AUC: Baseline AUC on synthetic vs. improvement after enhancements
Technical Metrics
- Generation Throughput: Records/second with enhanced features
- Memory Efficiency: Peak memory usage for equivalent dataset sizes
- Configuration Complexity: Lines of YAML required for common scenarios
Next Steps
- Review individual research documents for detailed analysis
- Prioritize improvements based on use case requirements
- Create implementation tickets for Phase 1 items
- Establish baseline metrics for tracking progress
Research conducted: January 2026 System version analyzed: 0.2.3