| --- |
| license: apache-2.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| - graph-ml |
| - other |
| tags: |
| - synthetic |
| - financial-data |
| - vynfi |
| - group-audit |
| - consolidation |
| - intercompany |
| - audit-analytics |
| - ifrs |
| - ias-21 |
| - ifrs-10 |
| - ias-28 |
| - cta |
| - nci |
| - method-a |
| - accounting-network |
| - graph-neural-network |
| - enterprise-scale |
| size_categories: |
| - 100M<n<1B |
| language: |
| - en |
| pretty_name: VynFi Group Audit — ACME Enterprise 2000-Entity Archive |
| --- |
| |
| # VynFi Group Audit — ACME Enterprise 2000-Entity Archive |
|
|
| A **2,000-entity multinational consolidation** generated by the VynFi |
| DataSynth group-audit simulation engine. ACME Inc. is a fictitious |
| US-domiciled holding with subsidiaries across North America, Europe, |
| APAC, and emerging markets, demonstrating every consolidation |
| primitive at enterprise scale: manifest-driven IC matching, IAS 21 |
| translation + CTA, IFRS 10 NCI, IAS 28 equity-method investments — |
| and from v5.10 the consolidated **Method-A accounting-network edge |
| list** spanning all 2,000 entities and their intercompany flows. |
|
|
| Generated with **DataSynth v5.10.0** · |
| [GitHub](https://github.com/mivertowski/SyntheticData) · |
| [Companion paper (SSRN)](https://ssrn.com/abstract=6538639) · |
| [Generation config](https://github.com/mivertowski/SyntheticData/blob/main/configs/examples/group/enterprise_2000.yaml). |
|
|
| This is the canonical enterprise-grade reference archive — small |
| enough to download (~3 GB compressed / ~71 GB uncompressed), structured |
| enough to navigate, and heterogeneous enough to exercise every |
| consolidation code path *plus* a 5.6 M-edge accounting network for |
| graph-ML benchmarking. |
|
|
| ## What's in the archive |
|
|
| ``` |
| enterprise_2000/ |
| ├── manifest.json # canonical group manifest (deterministic from config + seed) |
| ├── shard_summary.json # per-shard generation summary (4 shards) |
| ├── entities/ # 2 000 entity sub-trees (sizes vary by scoping profile) |
| │ ├── ACME_HQ/ # parent (US, USD, "flagship" profile) |
| │ │ ├── ... (full single-entity output: master_data/, document_flows/, subledger/, audit/, …) |
| │ │ └── graphs/ |
| │ │ ├── je_network.csv # NEW v5.10 — per-entity Method-A edges + ic_pair_id + ic_partner_entity |
| │ │ └── je_network.parquet # NEW v5.10 — Zstd-compressed parquet |
| │ ├── ACME_EUR/ # 100% EUR sub (DE, "flagship" profile) |
| │ ├── ACME_UK/ # 100% GBP sub ("significant" profile) |
| │ ├── ACME_JP/ # 85% JPY sub ("significant" profile) |
| │ ├── ACME_NA_SIG0000001 … 50/ # 50 N. American "significant" subs |
| │ ├── ACME_EU_SIG0000001 … 25/ # 25 European "significant" subs |
| │ ├── ACME_NA0000001 … 700/ # 700 N. American "material" subs |
| │ ├── ACME_EU0000001 … 350/ # 350 European "material" subs |
| │ ├── ACME_AS0000001 … 200/ # 200 APAC "material" subs |
| │ ├── ACME_SMALL0000001 … 471/ # 471 emerging-market "immaterial" subs |
| │ └── ACME_JV0000001 … 200/ # 200 equity-method joint ventures |
| ├── consolidated/ # group-level outputs |
| │ ├── consolidated_financial_statements.json # BS + IS + CF + Statement of Changes in Equity |
| │ ├── consolidation_schedule.json # per-account pre/elim/post + per-entity contributions |
| │ ├── notes_to_consolidated_fs.json # 8-note disclosure set |
| │ ├── nci_rollforward.json # 1 502 NCI rollforwards (one per fully-consolidated sub <100% owned) |
| │ ├── cta_rollforward.json # CTA per non-presentation-currency entity |
| │ ├── translation_worksheet.json # IAS 21 line-by-line worksheet across all entities |
| │ ├── equity_method_investments.json # 200 JV investment carrying values |
| │ ├── je_network.csv # NEW v5.10 — 5,604,445 consolidated Method-A edges |
| │ └── je_network.parquet # NEW v5.10 — Zstd-compressed parquet (~210 MB) |
| └── ic_eliminations/ |
| └── ic_matching_coverage.json # diagnostic histogram of matched/unmatched IC pairs |
| ``` |
|
|
| **Banking / KYC / AML data is NOT included.** The companion banking |
| showcase lives at |
| [VynFi/vynfi-aml-100k](https://huggingface.co/datasets/VynFi/vynfi-aml-100k); |
| this dataset is focused on group-audit specifics. |
|
|
| ## What changed in v5.10.0 |
|
|
| * **Per-entity `je_network.{csv,parquet}`** at `entities/{code}/graphs/` |
| — the same Method-A 13-column edge list shipped on |
| [`VynFi/vynfi-journal-entries-1m`](https://huggingface.co/datasets/VynFi/vynfi-journal-entries-1m) |
| v5.9.0, plus `ic_pair_id` + `ic_partner_entity` columns so the |
| inter-entity flows can be joined into pairs (one edge per side). |
| * **Consolidated `consolidated/je_network.{csv,parquet}`** — every |
| entity's edges concatenated, plus the **368-strong** elimination |
| JE set (flagged `is_eliminated=true`), with `entity_code` as a |
| partition column. Total 5,604,445 edges (≈ 1.26 GB CSV / 211 MB |
| parquet). |
| * **Apache 2.0 license + graph-ml task category** added so the |
| dataset surfaces in HF graph-ML searches. |
| * The underlying group-audit simulation engine (manifest, shard, |
| aggregate phases, IC matching, NCI, equity-method, IAS 21 |
| translation) is byte-identical to v5.0 — IC matching coverage |
| is **91.59 %** (4,359 / 4,759 pairs matched) in both releases. |
|
|
| See the [v5.10.0 release notes](https://github.com/mivertowski/SyntheticData/releases/tag/v5.10.0) |
| for the full change list. |
|
|
| ## Generated under |
|
|
| | | | |
| |---|---| |
| | **Engine** | VynFi DataSynth `datasynth-group` v5.10.0 | |
| | **Determinism seed** | `0xCAFEBABEDEADBEEF` | |
| | **Config** | [`configs/examples/group/enterprise_2000.yaml`](https://github.com/mivertowski/SyntheticData/blob/main/configs/examples/group/enterprise_2000.yaml) | |
| | **Wall-clock** | 8 min 5 sec for the full pipeline (manifest + 4 shards + aggregate) | |
| | **Peak RSS** | 69.4 GiB across the rayon-parallel shard runner | |
| | **Hardware** | Azure `Standard_NC40ads_H100_v5` (40 vCPU, 314 GiB RAM) in `westeurope` | |
| | **Output** | 186,369 files / 71 GiB uncompressed | |
| | **Reproducibility** | Bit-for-bit from the [pinned config](https://github.com/mivertowski/SyntheticData/blob/main/configs/examples/group/enterprise_2000.yaml) | |
|
|
| ## Standards compliance |
|
|
| The consolidation follows IFRS-equivalent treatment: |
| - **IAS 21** — functional-currency translation with closing/average/historical |
| rates; CTA accumulated to OCI. Non-USD entities use their declared |
| functional currency (EUR/GBP/JPY for the explicit subs, USD pegged |
| for the rest). See `consolidated/translation_worksheet.json` for the |
| line-by-line worksheet. |
| - **IFRS 10** — fully-consolidated entities (1,800 of them) aggregated |
| at 100 % with NCI separately presented for any sub <100 % owned. |
| See `consolidated/nci_rollforward.json` (1,502 entries). |
| - **IAS 28 / IFRS 11** — 200 equity-method joint ventures carried as |
| single-line investments with share-of-profit pickup. The IAS 28.38 / |
| ASC 323-10-35-20 carrying-amount-clamped-at-zero rule is applied. |
| See `consolidated/equity_method_investments.json`. |
| - **IAS 1** — consolidated balance sheet identity (Assets = Liabilities + |
| Equity + NCI). Note: the v5.0 fixture deliberately injects fraud / |
| anomaly entries with mismatched debits and credits, so the literal |
| identity does NOT hold to the cent on this archive — the imbalance |
| IS the ground-truth signal for fraud detection. |
|
|
| ## IC matching coverage |
|
|
| ACME's IC relationships expand to **4,759 planned pairs** under the |
| v5.0 manifest-driven matching strategy. **4,359 (91.59 %) match** in |
| this archive. The 400 unmatched pairs are pattern-derived relationships |
| where shard-runner injection didn't produce both sides — usually |
| because one side's entity hit anomaly-injection's "skip this JE" |
| branch. Unchanged from v5.0. |
|
|
| See `ic_eliminations/ic_matching_coverage.json` for the full histogram. |
|
|
| ## Quick start (consolidated edge list — graph-ML ready) |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import pandas as pd |
| |
| # Pull just the 211 MB consolidated edge parquet — no full download |
| edges_path = hf_hub_download( |
| repo_id="VynFi/vynfi-group-audit-enterprise-2000", |
| filename="enterprise_2000/consolidated/je_network.parquet", |
| repo_type="dataset", |
| ) |
| df = pd.read_parquet(edges_path) |
| print(df.shape) # (5_604_445, 18) |
| print(df["entity_code"].nunique()) # 2000+ entity codes |
| print(df["is_eliminated"].sum()) # 368 elimination edges |
| print(df["ic_pair_id"].notna().sum()) # ~8K seller+buyer IC edges |
| ``` |
|
|
| > **Note:** the consolidated edge list lives inside the tarball |
| > `enterprise_2000.tar.zst` at the path |
| > `enterprise_2000/consolidated/je_network.parquet`. See the per-entity |
| > walkthrough below for partial-download examples. |
| |
| ## Quick start (full archive) |
| |
| ```python |
| from huggingface_hub import snapshot_download |
| import json, pathlib |
| |
| # Note: tarball is ~3 GB compressed; uncompressed is ~71 GB |
| local = pathlib.Path(snapshot_download( |
| repo_id="VynFi/vynfi-group-audit-enterprise-2000", |
| repo_type="dataset")) |
| |
| # Extract: |
| import subprocess |
| subprocess.run(["tar", "-I", "zstd", "-xf", str(local / "enterprise_2000.tar.zst"), "-C", str(local)]) |
| root = local / "enterprise_2000" |
|
|
| cfs = json.loads( |
| (root / "consolidated/consolidated_financial_statements.json") |
| .read_text()) |
| print("Group:", cfs["balance_sheet"]["group_id"]) |
| print("Total assets (USD):", cfs["balance_sheet"]["total_assets"]) |
| print("Total L+E+NCI (USD):", cfs["balance_sheet"]["total_liabilities_plus_equity_plus_nci"]) |
| print("NCI separately presented:", cfs["balance_sheet"]["total_nci"]) |
| ``` |
| |
| ## Quick start (per-entity walkthrough) |
|
|
| ```python |
| import json, tarfile |
| from huggingface_hub import hf_hub_download |
| |
| # Download just the tarball, then extract one entity's slice in-memory |
| tar_path = hf_hub_download( |
| repo_id="VynFi/vynfi-group-audit-enterprise-2000", |
| filename="enterprise_2000.tar.zst", |
| repo_type="dataset", |
| ) |
| # (Use 'tar -I zstd -xf' on disk for full-archive extraction) |
| ``` |
|
|
| ## Schema highlights |
|
|
| **Per-entity** (`entities/{code}/`) carry the v5.x single-entity output |
| shape unchanged. See the [VynFi DataSynth README](https://github.com/mivertowski/SyntheticData) |
| for the ~20 typed-snapshot subdirectories. |
|
|
| **Per-entity `graphs/je_network.{csv,parquet}`** (NEW v5.10) — 15 columns: |
| |
| ``` |
| edge_id, document_id, posting_date, from_account, to_account, |
| from_line_id, to_line_id, amount, confidence, predecessor_edge_id, |
| business_process, is_fraud, is_anomaly, |
| ic_pair_id, ic_partner_entity |
| ``` |
| |
| **Consolidated `consolidated/je_network.{csv,parquet}`** (NEW v5.10) — 18 columns: |
|
|
| ``` |
| edge_id, document_id, entity_code, posting_date, from_account, to_account, |
| from_line_id, to_line_id, amount, confidence, predecessor_edge_id, |
| business_process, is_fraud, is_anomaly, |
| ic_pair_id, ic_partner_entity, is_eliminated, eliminates_ic_pair_id |
| ``` |
|
|
| **Group-level files** under `consolidated/` and `ic_eliminations/` |
| match the v5.0 spec §9 schema. Field-by-field documentation lives |
| in the engine's `crates/datasynth-group/src/aggregate/` modules at |
| the v5.10.0 release tag. |
|
|
| ## What this dataset is good for |
|
|
| - **Audit ML benchmarks** — large-scale group-audit simulation with |
| known ground truth (every fraud / anomaly / IC pair labelled). |
| - **Graph-ML benchmarks (NEW)** — 5.6 M-edge accounting network |
| spanning 2,000 entities with explicit IC pair linkage and |
| elimination-edge labels. See companion model |
| [`VynFi/je-fraud-gnn`](https://huggingface.co/VynFi/je-fraud-gnn) |
| for a single-entity baseline. |
| - **Consolidation engine validation** — drop-in reference for testing |
| custom IFRS / ASC 810 consolidation logic. |
| - **Education** — concrete example of a 2 000-entity multinational |
| consolidation for accounting / audit pedagogy. |
| - **Performance benchmarking** — the engine's published 69 GiB |
| peak / 8-minute wall-clock profile on `Standard_NC40ads_H100_v5` |
| is reproducible against this exact fixture. |
|
|
| ## What this dataset is NOT |
|
|
| - Real-world data. Every value is synthetic and deterministically |
| generated. Statistical distributions are approximate models, not |
| samples from any specific company. "ACME" is a fictitious name and |
| has no relationship to any real entity. |
| - A research-grade fraud benchmark. Fraud labels are *injected by |
| construction*, not discovered via investigation. |
| - A regulatory filing. The IFRS treatment is faithful to the |
| published standards but the underlying numbers are fictitious; |
| do not use for any compliance purpose. |
|
|
| ## License |
|
|
| Apache 2.0. Free for commercial use, modification, distribution, |
| private use; see `LICENSE` for the full terms. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{ivertowski2026datasynth, |
| author = {Ivertowski, Michael}, |
| title = {{DataSynth}: Reference Knowledge Graphs for Enterprise |
| Audit Analytics through Synthetic Data Generation |
| with Provable Statistical Properties}, |
| year = {2026}, |
| month = {April}, |
| howpublished = {SSRN Working Paper}, |
| url = {https://ssrn.com/abstract=6538639} |
| } |
| ``` |
|
|
| ## Related VynFi datasets |
|
|
| - [`VynFi/vynfi-journal-entries-1m`](https://huggingface.co/datasets/VynFi/vynfi-journal-entries-1m) — single-entity 1 M JE lines + COA + TB + cost / profit centres + Method-A accounting-network edge list |
| - [`VynFi/je-fraud-gnn`](https://huggingface.co/VynFi/je-fraud-gnn) — trained GraphSAGE fraud + GAE anomaly model (companion to the journal-entries dataset) |
| - [`VynFi/vynfi-audit-p2p`](https://huggingface.co/datasets/VynFi/vynfi-audit-p2p) — P2P document-flow corpus |
| - [`VynFi/vynfi-supply-chain-ocel`](https://huggingface.co/datasets/VynFi/vynfi-supply-chain-ocel) — Native OCEL 2.0 event log |
| - [`VynFi/vynfi-aml-100k`](https://huggingface.co/datasets/VynFi/vynfi-aml-100k) — Banking + AML labels |
| - [`VynFi/vynfi-sar-narratives`](https://huggingface.co/datasets/VynFi/vynfi-sar-narratives) — Banking + AML labels + SAR narratives |
| - [`VynFi/vynfi-ocel-manufacturing`](https://huggingface.co/datasets/VynFi/vynfi-ocel-manufacturing) — Lightweight reconstructed-events prototyping companion |
|
|
| ## Related VynFi Spaces |
|
|
| - 🔗 [`VynFi/accounting-network-explorer`](https://huggingface.co/spaces/VynFi/accounting-network-explorer) — Interactive ISO 21378 account-class graph |
| - 🛡️ [`VynFi/fraud-gnn-demo`](https://huggingface.co/spaces/VynFi/fraud-gnn-demo) — Gradio fraud-GNN inference demo |
| - 📊 [`VynFi/process-mining-demo`](https://huggingface.co/spaces/VynFi/process-mining-demo) — pm4py process-mining showcase |
|
|
