DataSynth

High-Performance Synthetic Enterprise Financial Data Generator

Developed by Michael Ivertowski, Zurich, Switzerland

What is DataSynth?

DataSynth is a high-performance, configurable synthetic data generator that produces realistic, interconnected enterprise financial data at scale. It generates coherent General Ledger journal entries, Chart of Accounts, SAP HANA-compatible ACDOCA event logs, document flows, subledger records, banking/KYC/AML transactions, OCEL 2.0 process mining data, audit workpapers, ML-ready graph exports, and complete enterprise process chains covering 20+ process families.

All generated data respects accounting identities (debits = credits, Assets = Liabilities + Equity), follows empirical distributions (Benford’s Law, log-normal mixtures), and maintains referential integrity across 100+ output tables.

What’s New in v1.3.0

Enterprise Group Audit Simulation — DataSynth now generates complete audit simulation datasets covering the ISA lifecycle from engagement to opinion, with ISA 600 group audits, 10+ accounting standards (IFRS 3/8/9, IAS 12/19/21/37, ASC 326/718/740/805), and a full audit methodology framework (CRA, materiality, sampling, SCOTS, analytical procedures).

Use --preset audit-group to generate 113+ interconnected files ready for ML training, AI agent interaction, and auditor training.

Quick Links

Section	Description
Getting Started	Installation, quick start guide, and demo mode
User Guide	CLI reference, server API, desktop UI, Python SDK
Configuration	Complete YAML schema and industry presets
Architecture	System design, data flow, resource management
Crate Reference	Detailed documentation for all 16 crates
Advanced Topics	Anomaly injection, graph export, fingerprinting, standards
Deployment	Docker, Kubernetes, bare metal, security hardening
Use Cases	Fraud detection, audit, AML/KYC, compliance, ESG
Changelog	Release history and version details

Key Features

Core Data Generation

Feature	Description
Statistical Distributions	Log-normal mixtures, Gaussian mixtures, Pareto, Weibull, Beta, zero-inflated with configurable components
Copula Correlations	Cross-field dependencies via Gaussian, Clayton, Gumbel, Frank, and Student-t copulas
Benford’s Law	First and second-digit compliance with configurable deviation for anomaly injection
Temporal Patterns	Month-end/quarter-end/year-end volume spikes, intraday segments, business day calendars (15 regions), processing lags
Regime Changes	Economic cycles, acquisition effects, and structural breaks in time series
Industry Presets	Manufacturing, Retail, Financial Services, Healthcare, Technology
Chart of Accounts	Small (~100), Medium (~400), Large (~2500) account structures
Country Packs	Pluggable JSON packs (US, DE, GB + 7 more) with holidays, names, tax, addresses, payroll

Enterprise Process Simulation

DataSynth covers the full enterprise process landscape:

Process Family	Scope
General Ledger	Journal entries, chart of accounts, ACDOCA event logs
Procure-to-Pay	Purchase requisitions, POs, goods receipts, vendor invoices, payments, three-way match
Order-to-Cash	Sales orders, deliveries, customer invoices, receipts, dunning
Source-to-Contract	Spend analysis, sourcing projects, supplier qualification, RFx, bids, contracts, scorecards
Hire-to-Retire	Payroll, tax/deduction calculations, time & attendance, expense reports, benefit enrollment
Manufacturing	Production orders, BOM explosion, routing, WIP costing, quality inspections, cycle counts
Financial Reporting	Balance sheet, income statement, cash flow, changes in equity, KPIs, budget variance
Tax Accounting	Multi-jurisdiction, VAT/GST returns, ASC 740/IAS 12 provisions, FIN 48, withholding
Treasury	Cash positioning, forecasts, cash pooling, hedging (ASC 815/IFRS 9), debt covenants, netting
Project Accounting	WBS hierarchies, cost lines, PoC revenue, earned value (SPI/CPI/EAC), change orders
ESG / Sustainability	GHG Scope 1/2/3, energy/water/waste, diversity, safety, GRI/SASB/TCFD disclosures
Intercompany	IC matching, transfer pricing, consolidation eliminations, currency translation
Subledgers	AR, AP, Fixed Assets, Inventory with GL reconciliation
Period Close	Monthly close engine, depreciation, accruals, year-end closing entries
Banking / KYC / AML	Customer personas, KYC profiles, AML typologies (structuring, layering, mule, funnel)
Audit	Complete ISA lifecycle: engagements, workpapers, evidence, risk assessments, findings, opinions (ISA 700), KAMs (ISA 701), SOX 302/404
Group Audit (ISA 600)	Component auditors, materiality allocation, scope assignment, component instructions/reports, consolidation
Sales	Quote-to-order pipeline with win rate modeling
Bank Reconciliation	Statement matching, outstanding checks, deposits in transit

Accounting & Audit Standards

Accounting frameworks: US GAAP, IFRS, French GAAP (PCG), German GAAP (HGB/SKR04), dual reporting
Revenue recognition: ASC 606 / IFRS 15 with performance obligations and SSP allocation
Leases: ASC 842 / IFRS 16 with ROU assets and lease liabilities
Fair value: ASC 820 / IFRS 13 Level 1/2/3 hierarchy
Impairment: ASC 360 / IAS 36 testing with fair value estimation
Audit standards: ISA (34 standards), PCAOB (19+ standards), SOX 302/404 compliance
COSO 2013: 5 components, 17 principles, maturity levels
Localized exports: FEC (French) and GoBD (German) audit file formats
Enterprise Group Audit (ISA 600): Component auditor assignment, group materiality allocation, scope assignment (full/specific/analytical), component instructions and reports
Audit Opinion (ISA 700/705/706/701): Opinion derived from findings severity and going concern, Key Audit Matters, PCAOB ICFR opinion
Audit Methodology: Combined Risk Assessment (ISA 315), materiality calculations (ISA 320), sampling methodology (ISA 530), SCOTS classification, unusual item detection, analytical relationships (ISA 520)
Deferred Tax (IAS 12 / ASC 740): Temporary differences, ETR reconciliation, rollforward schedules, valuation allowances
Business Combinations (IFRS 3 / ASC 805): Purchase price allocation, fair value step-ups, goodwill, contingent consideration
Segment Reporting (IFRS 8 / ASC 280): Operating segments with reconciliation to consolidated totals
Expected Credit Loss (IFRS 9 / ASC 326): Provision matrix by aging bucket, forward-looking scenarios, ECL movements
Pensions (IAS 19 / ASC 715): DBO rollforward, plan assets, pension expense, OCI remeasurements
Consolidated Financial Statements: Standalone + consolidated with elimination schedules and going concern assessment

Fraud, Anomalies & Data Quality

ACFE-aligned fraud taxonomy: Asset misappropriation, corruption, financial statement fraud
60+ anomaly types with full labeling for supervised ML
Collusion modeling: 9 ring types with role-based conspirators and escalation dynamics
Management override: Fraud triangle modeling (pressure, opportunity, rationalization)
Red flag generation: 40+ probabilistic fraud indicators with Bayesian calibration
Industry-specific patterns: Manufacturing yield manipulation, retail sweethearting, healthcare upcoding
Data quality variations: Missing values (MCAR/MAR/MNAR), format variations, typos, duplicates

Machine Learning & Graph Export

Graph formats: PyTorch Geometric, Neo4j, DGL, RustGraph JSON
Multi-layer hypergraph: 3-layer (Governance, Process Events, Accounting Network)
Train/val/test splits with configurable partitioning
Anomaly, fraud, quality, and drift labels in standardized format
Evaluation framework: Auto-tuning with quality gate enforcement

Advanced Generation

Capability	Description
LLM enrichment	Pluggable providers (mock/OpenAI-compatible) for vendor names, descriptions, anomaly explanations
Diffusion models	Statistical diffusion with Langevin reverse process and hybrid blending
Causal models	Structural causal models with do-calculus interventions and counterfactual generation
Natural language config	Generate YAML configurations from plain English
Scenario engine	Built-in fraud packs: revenue_fraud, payroll_ghost, vendor_kickback, management_override
Process mining	OCEL 2.0 + XES 2.0 with 101+ activity types across 12 process families

Production Features

REST / gRPC / WebSocket APIs with streaming and backpressure handling
Authentication: API key (Argon2id), JWT/OIDC (RS256), RBAC (Admin/Operator/Viewer)
Resource guards: Memory, disk, CPU monitoring with graceful degradation
Deterministic generation: Seeded ChaCha8 RNG for reproducible output
Desktop UI: Cross-platform Tauri/SvelteKit with 40+ configuration pages
Python SDK: Programmatic access with blueprints and DataFrame loading
Docker & Kubernetes: Distroless containers, Helm chart with HPA/PDB
Observability: OpenTelemetry traces, Prometheus metrics, structured JSON logging
Data lineage: Per-file checksums, lineage graph, W3C PROV-JSON export
Privacy-preserving fingerprinting: Differential privacy, k-anonymity, federated extraction
Ecosystem integrations: Apache Airflow, dbt, MLflow, Apache Spark

Quick Start

# Install from source
git clone https://github.com/mivertowski/SyntheticData.git
cd SyntheticData
cargo build --release

# Demo mode
./target/release/datasynth-data generate --demo --output ./output

# Custom configuration
./target/release/datasynth-data init --industry manufacturing --complexity medium -o config.yaml
./target/release/datasynth-data generate --config config.yaml --output ./output

Performance

Metric	Value
Single-threaded throughput	200,000+ journal entries/second
Parallel scaling	Linear with available CPU cores
Memory model	Streaming generation with configurable backpressure
Determinism	Fully reproducible via seeded ChaCha8 RNG

Architecture

DataSynth is organized as a Rust workspace with 16 modular crates:

datasynth-cli            CLI binary (generate, validate, init, info, fingerprint, scenario)
datasynth-server         REST / gRPC / WebSocket server with auth and rate limiting
datasynth-ui             Tauri + SvelteKit desktop application
                │
datasynth-runtime        Generation orchestrator (parallel execution, resource guards, streaming)
                │
datasynth-generators     50+ data generators across all process families
datasynth-banking        KYC / AML banking transaction generator
datasynth-ocpm           OCEL 2.0 / XES 2.0 process mining
datasynth-fingerprint    Privacy-preserving fingerprint extraction and synthesis
datasynth-standards      Accounting and audit standards (IFRS, US GAAP, ISA, SOX, PCAOB)
                │
datasynth-graph          Graph export (PyTorch Geometric, Neo4j, DGL, RustGraph, Hypergraph)
datasynth-graph-export   Unified graph export pipeline with 78 entity types
datasynth-eval           Statistical evaluation, quality gates, auto-tuning
                │
datasynth-config         Configuration schema, validation, industry presets
                │
datasynth-core           Domain models, traits, distributions, resource guards
                │
datasynth-output         Output sinks (CSV, JSON, NDJSON, Parquet + Zstd) with streaming
datasynth-test-utils     Test utilities, fixtures, mocks

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.

Support

Commercial support, custom development, and enterprise licensing are available upon request. Open an issue on GitHub.

DataSynth is provided “as is” without warranty of any kind. It is intended for testing, development, and research purposes. Generated data should not be used as a substitute for real financial records.

Keyboard shortcuts

DataSynth Documentation