VynFi/vynfi-journal-entries-10m

VynFi Journal Entries — 10M (v5.29 SOTA mode, research scale)

Research-scale synthetic GL dataset. ~10.9 M journal-entry lines from a 10-company multinational, generated by DataSynth v5.29.0 with the full SOTA-N structural-fidelity round + the central concentration abstraction (ConcentrationPipeline, #143). Companion to VynFi/vynfi-journal-entries-1m (same generator, same lever stack, same fraud-typology + anomaly-type columns) but ~10× scale, for behavioural-fidelity research, GNN training at scale, and head-to-head benchmarks against learned tabular generators (CTGAN / TVAE / GaussianCopula / TabularARGN).

Why a 10M variant exists alongside the 1M one

• 1M is the canonical showcase — small enough to download (~70 MB compressed) and benchmark on a laptop, used for the published BF numbers + dataset cards.
• 10M is the same data shape at 10× volume — adequate for training GraphSAGE / TabularARGN / diffusion models without the per-source IET / autocorrelation undersampling that 1M exhibits at the corpus's wider source vocabulary. ML research scale.

Both use the same v5.29 SOTA-mode config (10 companies × Custom(N) × 12 months); the 10M variant just bumps the per-company custom volume from 250 K to 2.5 M.

Structural metrics (vs reference)

Cross-industry manufacturing GL, comparison reference at 300k-JE deterministic sample. From experiments/ml/FINDINGS.md §10:

Metric (reference target)	v1 (v5.27 baseline)	v5.29 SOTA	reference
account top-10% line share	0.16	0.946	0.95
recurring-archetype share	0.131	0.885	0.967
top-50 archetype coverage	0.030	0.480	0.651
reversal proxy	0.0015	0.034	0.100
flow-graph edge entropy (lower=more templated)	10.75	7.95	5.95
AB allocation lines/JE	absent	55.7	52.2
distinct source codes	429	405	46
Business Unit dimension	absent	23.5% / 11 BUs, coherent	82% / 11
multi-currency lines (SAP DMBTR/WRBTR)	absent	present (3.2%)	~3.5%
blank-source rate (SOTA-7)	0%	20.6%	~21%
trading-partner pool size	~40	12	~12
amount distribution p99	16× reference	reference-match	—
lines-per-JE mean	11	4.6	4.5

Behavioral fidelity (Sajja 2026 P1-P4 framework)

Composite DRs over 26 P1-P4 sub-metrics, computed against a single GL reference shard with datasynth-data behavioral score --profile gl-source-tp. Lower is better; 1.0 = real-data 50/50-split noise floor.

Generator	Paradigm	Composite mean	vol-corrected
DataSynth v5.29 SOTA — 10M (this dataset)	rule + process + post-process	251.3×	65.8×
DataSynth v5.27 (v1 baseline)	rule + process (no concentration)	63.1×	109.3×
TabularARGN (Sajja paper)	learned autoregressive (single-row)	36.3×	n/a
CTGAN (Sajja paper)	learned GAN	32.2×	n/a
TVAE (Sajja paper)	learned VAE (post conditional sampling)	24.4×	n/a
GaussianCopula (Sajja paper)	learned copula	39.0×	n/a
Real-data noise floor	—	1.0×	1.0×

• v5.29 vs v5.27 vol-corrected composite: -40% improvement.
• The Sajja paper's row-independent paradigm is structurally bound by Propositions 1 & 2 (cannot reproduce P3 graph motifs or positive within-entity IET autocorrelation). DataSynth's rule-based joint JE generation does not have that bottleneck.

Quick start

from datasets import load_dataset
ds = load_dataset("VynFi/vynfi-journal-entries-10m")
print(ds["train"].column_names)        # 52 columns
print(ds["train"].num_rows)            # 10,915,403 lines

Aux artefacts in the same repo:

• chart_of_accounts.parquet
• je_network.parquet (Method A; ~1 edge / 2-line JE)
• cost_centers.parquet, profit_centers.parquet

Generation config

configs/examples/hf/journal_entries_1m_sota.yaml (with per-company volume bumped to Custom(2_500_000)) in mivertowski/SyntheticData @ v5.29.0.

datasynth-data validate --config journal_entries_1m_sota.yaml
datasynth-data generate --config journal_entries_1m_sota.yaml

Citation

@dataset{vynfi_je_10m_2026,
  author    = {Ivertowski, Michael and DataSynth contributors},
  title     = {VynFi Journal Entries 10M (v5.29 SOTA mode)},
  year      = {2026},
  publisher = {VynFi / Hugging Face},
  url       = {https://huggingface.co/datasets/VynFi/vynfi-journal-entries-10m},
  version   = {v5.29.0},
}

Companion P1-P4 framework:

@article{sajja2026behavioral,
  author = {Sajja, Bhavana},
  title  = {Synthetic Tabular Generators Fail to Preserve Behavioral Fraud
            Patterns: A Benchmark on Temporal, Velocity, and Multi-Account
            Signals},
  year   = {2026},
  eprint = {arXiv:2604.13125v1}
}

Reproducibility

artefact	path / hash
Generator binary	datasynth-data 5.29.0 (release tag v5.29.0)
Config	configs/examples/hf/journal_entries_1m_sota.yaml @ v5.29.0 (volume bumped to 2.5M)
BF score script	datasynth-data behavioral score --profile gl-source-tp
Run seed	20260526
BF reports	docs/baselines/2026-05-26-v5.29.0-10m/je10m/
Trained GNN on this data	VynFi/je-fraud-gnn (test AUC 0.919)