What is Synthetic Financial Data?
Statistically faithful financial records that contain zero personally identifiable information — built for audit testing, ML training, compliance validation, and research.
Definition
Synthetic financial data is artificially generated data that mirrors the statistical properties, structure, and business rules of real financial records — without containing any real customer or transaction information. Unlike anonymized or masked data, synthetic data is created from scratch using statistical models calibrated against real-world distributions.
A properly generated synthetic general ledger is indistinguishable from a real one in statistical tests: Benford's Law holds, amounts follow sector-appropriate distributions, and the double-entry accounting invariant is preserved across every journal entry.
Why Real Data Falls Short
Four reasons production data can't be used for development and testing
PII & Privacy Risk
Production financial records contain customer names, account numbers, and transaction histories. Using them outside controlled environments violates GDPR, CCPA, and SOX.
Compliance Barriers
Copying production data for dev/test requires legal review, anonymization pipelines, and audit trails. Most teams wait weeks before they can begin work.
Scarcity of Fraud Labels
Real fraud is rare (< 0.1% of transactions). ML models need balanced training data with known ground-truth labels that production data cannot provide.
Scale Limitations
Stress-testing at 10x or 100x production volume is impossible with real data. Synthetic generation removes the ceiling on dataset size.
How VynFi Generates Synthetic Financial Data
Four pillars of the DataSynth engine
Corpus-grounded realism
DataSynth is calibrated against real audit corpora — recurring posting templates (top-50 archetypes cover ~65% of JEs), Pareto account activity (top-10% of accounts carry ~95% of lines), reversal and allocation processes, and Benford's Law compliance. Structurally indistinguishable from production audit data.
Double-Entry Balance
Every generated journal entry balances debits and credits. Trial balances prove. Financial statements reconcile across income statement, balance sheet, and cash flow.
Cross-Layer Coherence
Transactions propagate through sub-ledger, general ledger, and financial statements. An AP invoice creates a GL entry, hits the balance sheet, and flows through cash flow — just like real ERP data.
Quality Validation
Every dataset passes 32+ consistency checks: Benford MAD < 0.006, trial balance proof, FG rollforward, cash flow reconciliation, and segment-to-consolidated reconciliation.
Use Cases
Who uses synthetic financial data and why
Audit Testing
Generate general ledger data with known anomalies for training internal audit teams. Configurable anomaly injection rates from beginner to expert difficulty.
ML Model Training
Balanced datasets with ground-truth fraud labels for training classification models. 130+ labeled anomaly subtypes across 14 AML typologies.
Compliance Validation
Test SOX, Basel III, and IFRS reporting pipelines with data that triggers the same validation rules as production — without exposing real customer information.
Academic Research
Unlimited volumes of realistic financial data without licensing restrictions. Reproducible seed-based generation ensures results can be independently verified.
200K+
Rows per second
< 0.006
Benford MAD score
8
Industry sectors
32+
Consistency checks
Start generating synthetic financial data
5,000 free credits to start. No credit card required.