---
title: README
emoji: 📊
colorFrom: indigo
colorTo: purple
sdk: static
pinned: false
license: apache-2.0
---

# Synthetic data for audit, fraud & financial-network ML

**Privacy-preserving, fully synthetic financial datasets and ML demos** — generated by a
deterministic engine that produces balanced double-entry general ledgers, multi-entity group
consolidations, AML/banking transactions, and OCEL process logs, all carrying ground-truth
fraud / anomaly labels and grounded in real accounting standards (IFRS · US/French/German GAAP ·
ISA) and the ISO 21378 audit-data model.

Everything here is **synthetic** — no client or real-world data — so it can be used freely to
train, benchmark, and stress-test audit, fraud-detection, and graph-ML systems.

## 🚀 Interactive demos

| Space | What it does |
|---|---|
| 🔍 [**Inverse-Audit Detector**](https://huggingface.co/spaces/VynFi/inverse-audit-demo) | Label-free anomaly detection on a synthetic GL — fit the *normal-system manifold*, then flag journal entries by deviation via two fit-on-self residual arms (per-JE **density** + **relational** account-flow-graph) routed into one risk score. Pick a fraud scenario, see per-arm ROC, recall @ audit budget, and the top suspicious entries. |
| 🔀 [**Counterfactual GL Explorer**](https://huggingface.co/spaces/VynFi/counterfactual-gl-explorer) | Seed-locked **baseline vs counterfactual** ledgers from a causal-DAG intervention — pick a scenario (control-stress / SoD breakdown), see the effect-field distribution shift, the intervention trace, and the exact changed lines. Byte-deterministic generation, so the diff is *signal, not noise*. |
| 💸 [**Financial Sankey Explorer**](https://huggingface.co/spaces/VynFi/financial-sankey-explorer) | Multi-step **funds-flow** view of synthetic financial statements — the income-statement waterfall (Revenue → Gross Profit → Operating Income → Net Income, with operating-expense sub-bands) and the cash-flow flow (Operating/Investing/Financing → Net change in cash), per entity / period. |
| 🛡️ [**Fraud-GNN Demo**](https://huggingface.co/spaces/VynFi/fraud-gnn-demo) | Graph-neural-network fraud detection on the JE network — edge fraud predictor, node anomaly explorer, and a live check with confusion matrix + ROC. |
| 🔗 [**Accounting Network Explorer**](https://huggingface.co/spaces/VynFi/accounting-network-explorer) | Interactive ISO 21378 account-class flow graph — filter by business process, fraud, anomaly, amount, top-N; drill from Level-2 classes into Level-3 sub-classes. |
| 📊 [**Process Mining Demo**](https://huggingface.co/spaces/VynFi/process-mining-demo) | pm4py directly-follows graphs, variants, and statistics on the supply-chain OCEL 2.0 event log. |
| 🗂️ [**Data Explorer**](https://huggingface.co/spaces/VynFi/data-explorer) | Browse and inspect the VynFi synthetic datasets. |
| 🕵️ [**Perfect Audit Crime Challenge**](https://huggingface.co/spaces/VynFi/perfect-audit-crime-challenge) | Two-track community leaderboard — flag the planted fraud in synthetic GLs and help map the *detectability frontier*. **Track A** (ledger only): the *mimetic perfect crime* — fraud drawn from the ledger's own normal distribution — is provably uncatchable. **Track B** (ledger + ISA-520/505 evidence): it becomes catchable. Upload a submission → PR-AUC + per-observability recall on held-out labels. |

## 📦 Datasets

**ERP showcase / financial statements**
| Dataset | Highlights |
|---|---|
| [vynfi-sap-showcase](https://huggingface.co/datasets/VynFi/vynfi-sap-showcase) | "All capabilities" SAP-style ERP + audit bundle — per-period financial statements (BS / IS / CF) with prior-period comparatives, **operating-expense breakout**, and **Sankey funds-flow exports**, plus subledgers, FX, intercompany + consolidation, audit (ISA/SOX) + COSO controls, HR / manufacturing / treasury / tax / ESG, and ERP-coupled banking. Explore the flows in the [Financial Sankey Explorer](https://huggingface.co/spaces/VynFi/financial-sankey-explorer). |

**Group audit & consolidation**
| Dataset | Highlights |
|---|---|
| [vynfi-group-audit-enterprise-2000](https://huggingface.co/datasets/VynFi/vynfi-group-audit-enterprise-2000) | End-to-end 2 000-entity group: matched intercompany pairs, eliminations, IFRS-consolidated financial statements + schedules + notes + CTA/NCI/equity-method rollforwards. |
| [vynfi-group-audit-3yr-medium](https://huggingface.co/datasets/VynFi/vynfi-group-audit-3yr-medium) | Multi-period (3-year) group-audit bundle — period N+1 opens from period N's closing trial balance. |
| [vynfi-je-network-2k](https://huggingface.co/datasets/VynFi/vynfi-je-network-2k) | 68.5 M-edge consolidated journal-entry network from the 2 000-entity group — drop-in for GNN training (PyG / DGL), with `is_fraud`, `ic_pair_id`, `is_eliminated`. |

**General ledger / journal entries**
| Dataset | Highlights |
|---|---|
| [vynfi-journal-entries-1m](https://huggingface.co/datasets/VynFi/vynfi-journal-entries-1m) | ~1 M-entry manufacturing GL with ISA 240 manual flags, fraud labels, and chart of accounts. |
| [vynfi-journal-entries-10m](https://huggingface.co/datasets/VynFi/vynfi-journal-entries-10m) | Research-scale ~10.9 M-entry synthetic GL. |
| [vynfi-audit-p2p](https://huggingface.co/datasets/VynFi/vynfi-audit-p2p) | Procure-to-Pay document chain (PO/GR/VI/Payment) with fraud labels — audit-engagement grade. |

**Causal / counterfactual**
| Dataset | Highlights |
|---|---|
| [vynfi-counterfactual-gl](https://huggingface.co/datasets/VynFi/vynfi-counterfactual-gl) | Seed-locked, **byte-deterministic** baseline ↔ counterfactual GL pairs under named causal-DAG interventions (control-environment, SoD) — each pair differs *only* by the intervention's effect; the `diff` split isolates the changed lines. A clean treatment/control substrate for causal ML, treatment-effect estimation, and residual-based audit analytics. |

**AML / banking**
| Dataset | Highlights |
|---|---|
| [vynfi-aml-100k](https://huggingface.co/datasets/VynFi/vynfi-aml-100k) | 748 K banking transactions with AML/SAR-style labels and velocity features. |
| [vynfi-sar-narratives](https://huggingface.co/datasets/VynFi/vynfi-sar-narratives) | 156 K transactions paired with suspicious-activity-report narratives + AML labels. |

**Process mining (OCEL 2.0)**
| Dataset | Highlights |
|---|---|
| [vynfi-ocel-manufacturing](https://huggingface.co/datasets/VynFi/vynfi-ocel-manufacturing) | Manufacturing OCEL 2.0 event log — production-order lifecycle + quality inspections. |
| [vynfi-supply-chain-ocel](https://huggingface.co/datasets/VynFi/vynfi-supply-chain-ocel) | 5-company manufacturing supply-chain OCEL 2.0 event log for cross-process mining. |

**Challenge**
| Dataset | Highlights |
|---|---|
| [perfect-audit-crime-data](https://huggingface.co/datasets/VynFi/perfect-audit-crime-data) | The ledgers behind the [Perfect Audit Crime Challenge](https://huggingface.co/spaces/VynFi/perfect-audit-crime-challenge) — 3 multi-entity GLs across two tracks (ledger / ledger + ISA-520/505 evidence) with a planted *mimetic perfect-crime* family; labels held out for scoring. |

## 🤖 Models

| Model | What it is |
|---|---|
| [je-fraud-gnn](https://huggingface.co/VynFi/je-fraud-gnn) | GraphSAGE 2-layer journal-entry fraud classifier (test AUC **0.914**) + attribute-reconstruction GAE node-anomaly scorer (per-edge AUC **0.654**, unsupervised). Includes weights, preprocessor, and full metrics. |

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*All datasets and demos are synthetic and contain no client or real-world data.*