Enterprise-Grade Synthetic Financial Data

11 process families. 4 subledgers. 42 country packs. 10 sectors. 100K+ rows/sec.

Three Steps to Synthetic Data

From sign-up to production-ready data in minutes

Step 1

Sign Up

Create an account and get 10,000 free credits instantly. Pick a sector preset or configure every parameter.

Step 2

Generate

The VynFi engine produces statistically faithful data at 100K+ rows/sec. Choose rule-based, fingerprint, or diffusion backends.

Step 3

Build & Ship

Download as JSON, CSV, or Parquet. Stream via webhook. SDKs for Python, TypeScript, Rust, and .NET.

Generation Backends

Choose the engine that fits your use case

Rule-Based

Available

Deterministic rule-based generation using configurable business rules, sector-specific templates, and statistical distributions. Every row is fully reproducible given the same seed.

SpeedFastest

FidelityGood

Credit Multiplier1.0x

Best For

Prototyping, CI/CD, regression testing

Fingerprint

Available

Upload a statistical fingerprint profile extracted from your real data. The engine reconstructs synthetic records matching the original distributions without ever seeing the raw data.

SpeedMedium

FidelityHigh

Credit Multiplier1.5x

Best For

Audit, production-like data, compliance

Diffusion

Coming Soon

ML-based diffusion models trained on anonymized financial datasets generate hyper-realistic records with complex, non-linear relationships for edge-case scenario generation.

SpeedSlower

FidelityHighest

Credit Multiplier3.0x

Best For

ML training, stress testing, research

Backend Comparison

Backend	Speed	Fidelity	Cost	Best For
Rule-Based	Fastest	Good	`1.0x`	Prototyping, CI/CD, regression testing
Fingerprint	Medium	High	`1.5x`	Audit, production-like data, compliance
Diffusion	Slower	Highest	`3.0x`	ML training, stress testing, research

9 Data Types

From simple journal entries to full end-to-end process cycles

Journal Entries

General ledger, P&L balancing, trial balances

1 credit/row

Chart of Accounts

GL structure with account hierarchies

0.5 credits/account

Master Data

Vendors, customers, materials

1 credit/record

Document Flow Chain

PO → GR → Invoice → Payment linked records

5 credits/chain

Intercompany Pairs

Multi-entity with FX and eliminations

8 credits/pair

Full P2P Cycle

End-to-end with subledger and 3-way match

10 credits/cycle

Banking/KYC Profile

AML, risk advisory, KYC profiles

3 credits/customer

OCEL 2.0 Event Log

Process mining format for audit analytics

2 credits/event

Audit Workpaper

Lead sheets, TB, JE testing package

15 credits/engagement

11 Process Families

Full ERP document flows — every record linked, every balance reconciled

Procure-to-Pay (P2P)

PO → GR → Vendor Invoice → Payment

End-to-end procurement cycle with 3-way match validation, payment scheduling, and discount optimization.

Order-to-Cash (O2C)

Sales Order → Delivery → Invoice → Receipt

Revenue cycle from order entry through cash application with ASC 606 revenue recognition hooks.

HR & Payroll

Employee → Payroll Run → Tax → Benefits

Employee lifecycle, payroll runs with jurisdiction-aware tax withholding, benefits accruals, and statutory reporting.

Manufacturing

BOM → Production Order → WIP → Finished Goods

BOM explosion, production order costing, WIP valuation, scrap rates, and co-product allocation.

Treasury

Cash Position → FX Hedge → Transfer → Settlement

Cash management, FX hedging with mark-to-market, interbank transfers, and investment portfolio tracking.

Project Accounting

WBS → Time & Expense → Milestone → Revenue

WBS structures, time and expense capture, milestone billing, and percentage-of-completion revenue recognition.

Intercompany

IC Transaction → FX Conversion → Elimination

Multi-entity transactions with automatic FX conversion, transfer pricing adjustments, and elimination entries.

Period Close

Accrual → Reclass → Consolidation → Close

Month-end accruals, reclassification entries, consolidation adjustments, and automated close checklists.

Accounting Standards

Rev Rec · Lease · Fair Value · Impairment

ASC 606 revenue recognition, IFRS 16 / ASC 842 leases, fair value measurement, and impairment testing.

Source-to-Contract (S2C)

RFx → Vendor Qualification → Contract

Vendor qualification, RFx management, contract lifecycle tracking, and spend analytics.

Bank Reconciliation

Statement → Matching → Exception → Clearing

Automated statement matching with configurable rules, exception handling, and clearing workflows.

Every document in a process family is linked via referential keys — trace any transaction from purchase order to bank statement in a single query.

Subledger Intelligence

Production-grade subsidiary ledgers with reconciliation, aging, and audit-ready detail

Accounts Receivable

Customer invoices, receipts, credit memos, and aging schedules with automated dunning and configurable escalation levels.

Invoice generation with payment terms
Cash receipt application
Credit memo processing
Aging analysis (30/60/90/120 days)
Dunning automation with escalation

Accounts Payable

Vendor invoices, payments, and debit memos with 3-way matching (PO ↔ GR ↔ Invoice) and payment scheduling.

3-way match validation
Payment run scheduling
Debit memo processing
Early-pay discount optimization
Cash flow forecasting

Fixed Assets

Asset lifecycle from acquisition through disposal with 5 depreciation methods and revaluation support.

Straight-line depreciation
Declining balance / Double declining
Units of production
Sum-of-years digits
Asset disposal & revaluation

Inventory

INV

Position tracking and stock movements with 4 valuation methods. Full cycle from receipt to consumption.

FIFO valuation
LIFO valuation
Moving average costing
Standard cost accounting
Cycle counting & adjustments

All subledgers auto-reconcile to the General Ledger. Document flow linking traces every subledger entry back to its originating transaction.

Sector Intelligence

Purpose-built data models for 10 industry verticals

Retail & Consumer

POS transactions, inventory, e-commerce mix, seasonality

12 tables97%

Manufacturing (Discrete)

BOM complexity, WIP valuation, scrap rates, 3-way match

14 tables96%

Manufacturing (Process)

Batch yield, COS profiles, co-product allocations

11 tables95%

Financial Services (Banking)

Loan books, NPL ratios, LGD/PD curves, KYC profiles

18 tables98%

Financial Services (Insurance)

Premium distributions, claims curves, reserve triangles

13 tables97%

Healthcare

DRG distributions, clinical trial costs, revenue cycle

10 tables95%

Technology / SaaS

ARR/MRR curves, deferred revenue, churn curves

11 tables97%

Energy

Commodity-linked revenue, depletion curves, ARO distributions

9 tables94%

Real Estate

Lease terms, cap rates, IFRS 16 / ASC 842 portfolios

8 tables95%

Public Sector

Fund accounting, grant lifecycle, budget-to-actual variance

7 tables93%

42 Country Packs

Localized synthetic data that respects regional regulations, naming conventions, and financial standards

Americas

7 packs

US, CA, BR, MX, AR, CL, CO

EMEA

15 packs

GB, DE, FR, IT, ES, NL, CH, DK, NO, SE, FI, BE, PT, AT, IE

APAC

14 packs

IN, AU, JP, KR, CN, SG, HK, TH, ID, PH, VN, TW, NZ, MY

MEA

5 packs

AE, SA, IL, ZA, TR

Each pack includes locale configuration, multi-cultural naming, regional holidays, tax frameworks, banking standards, and accounting frameworks.

View full catalog

Quality Guarantees

Every dataset is validated against rigorous statistical benchmarks

< 0.006

Benford MAD

Mean Absolute Deviation rated 'excellent conformity' per Nigrini's criteria. Chi-squared test plus automatic correction.

0.95

Correlation Score

5 copula families (Gaussian, Clayton, Gumbel, Frank, Student-t) with Cholesky decomposition for correlated sampling.

~3%

ML F1 Delta

Target: fraud detectors trained on synthetic data within 3% of real-data F1 baselines (Isolation Forest, XGBoost, GCN).

Anomaly Types

Across 5 categories: timing, amount, relationship, pattern, and structural. Each tagged with difficulty, severity, and confidence.

Anomaly Scoring Framework

Every injected anomaly is tagged with three scoring dimensions for ML training

Difficulty

How hard the anomaly is to detect. Ranges from obvious (round-number fraud) to subtle (gradual threshold creep). Enables curriculum-based ML training from easy to hard samples.

1-5 (Trivial to Expert)

Severity

Financial impact level of the anomaly. Low-severity items may be process inefficiencies; high-severity items represent material misstatement or fraud risk.

1-5 (Minor to Critical)

Confidence

Certainty that the record is truly anomalous vs. a legitimate edge case. Allows models to learn from ambiguous examples and calibrate detection thresholds.

0.0 - 1.0 (Uncertain to Certain)

All 33 anomaly types across 5 categories (timing, amount, relationship, pattern, structural) carry these three scores, enabling fine-grained control over training dataset composition.

AML Banking Typologies

5 built-in anti-money laundering patterns for compliance testing and detection model training

Structuring

Breaking large transactions into smaller amounts below reporting thresholds (e.g., just under $10K). Generates realistic deposit patterns with variable amounts, timing, and branch distribution.

Funnel Accounts

Multiple source accounts feeding into a single consolidation account. Simulates aggregation patterns with varying inflow frequencies, amounts, and dormancy periods.

Layering

Complex chains of transactions across multiple accounts and entities to obscure fund origins. Generates multi-hop transfer sequences with interleaved legitimate activity.

Mule Networks

Coordinated movement of funds through networks of seemingly unrelated accounts. Creates graph-structured transaction data with detectable relationship patterns.

Round Tripping

Circular fund flows where money returns to the originator through intermediate entities. Generates closed-loop transaction chains with varying path lengths and timing.

All AML typologies are available in the Financial Services (Banking) sector pack. Combine with the anomaly scoring framework for labeled training data.

Subledger Reconciliation & Multi-Entity Consolidation

Every generated dataset maintains provable consistency across ledgers and entities

Subledger Reconciliation

All 4 subledgers (AR, AP, FA, INV) auto-reconcile to their corresponding GL control accounts. The engine ensures:

AR subledger aging totals match the AR control account balance
AP open items sum equals the AP control account in the GL
Fixed asset net book values reconcile to FA control accounts
Inventory position values match INV control accounts after all movements
Document flow linking traces every subledger entry to its originating transaction

Multi-Entity Consolidation

Generate data for multi-entity corporate structures with provable intercompany elimination and consolidation accuracy:

Intercompany transaction pairs balance across all entity combinations
FX conversion entries are generated with consistent exchange rate snapshots
Elimination entries zero out IC balances in the consolidated trial balance
Transfer pricing adjustments maintain arm's-length consistency
15 coherence validators run across the entire multi-entity dataset

Privacy & Compliance

Enterprise-grade security and regulatory alignment

Differential Privacy

Our fingerprint backend uses differential privacy guarantees to ensure that no individual record from your source data can be reverse-engineered. Configurable epsilon values let you balance fidelity and privacy for your compliance requirements.

C2PA Watermarking

Every generated dataset is embedded with C2PA content credentials to meet EU AI Act Article 50 transparency obligations. Machine-readable provenance metadata ensures synthetic data is always identifiable as AI-generated.

W3C PROV-JSON Lineage

Full data lineage tracking using the W3C PROV standard. Every dataset carries a provenance graph documenting the generation parameters, backend version, seed values, and quality scores for complete auditability.

Compliance Ready

VynFi is built to meet the most demanding regulatory frameworks. Our infrastructure and processes are aligned with:

EU AI Act

GDPR-Ready

AES-256

Learn more about our security

Coming Q2 2026

Counterfactual Simulations

Ask "what if?" — generate paired baseline and counterfactual datasets to model the financial impact of events that haven't happened yet.

Define Intervention

Choose from 7 intervention types — entity events, parameter shifts, control failures, macro shocks, and more. Chain them into composite scenarios.

Paired Generation

The engine generates a baseline dataset and a counterfactual in parallel, using a causal DAG to propagate intervention effects through 47 interconnected nodes.

Analyze Impact

Get record-level diffs, aggregate impact summaries, and full intervention traces. Export for ML training, stress testing, or audit analysis.

7 Intervention Types

Entity Event

Vendor bankruptcy, customer default, employee departure

Parameter Shift

Interest rates, FX rates, commodity prices, discount rates

Control Failure

Disabled approvals, bypassed segregation of duties

Process Change

Modified payment terms, alternate procurement routes

Macro Shock

Recession, supply chain disruption, market crash

Regulatory Change

New tax rules, reporting mandates, compliance shifts

Composite

Chain multiple interventions for complex scenarios

Built For

Reverse Stress TestingFraud Scenario ModelingRegulatory Impact AnalysisML Training Data AugmentationAudit What-If AnalysisRisk Model Validation

Ready to generate enterprise-grade synthetic data?

10,000 free credits every month. No credit card required.

Enterprise Plans →Read the Methodology →