v5.9.0 fleet alignment: standardize README structure (frontmatter, sections, citation)

README.md

@@ -2,6 +2,10 @@
 license: apache-2.0
 task_categories:
   - tabular-classification
+language:
+  - en
+size_categories:
+  - 100K<n<1M
 tags:
   - synthetic
   - financial-data
@@ -9,37 +13,92 @@ tags:
   - aml
   - banking
   - fraud-detection
-size_categories:
-  - 100K<n<1M
+  - velocity-features
 ---
 
-# VynFi AML: 748K Banking Transactions with AML Labels
+# VynFi AML — 748 K Banking Transactions with AML Labels
+
+748 869 synthetic banking transactions with AML/SAR-style labels and
+14 pre-computed velocity features.  Designed for tabular AML
+classification benchmarks, threshold-calibration studies, and
+balance-sheet / counterparty-risk analytics.
+
+Generated with **DataSynth** (banking module, KYC/AML typologies) ·
+[GitHub](https://github.com/mivertowski/SyntheticData) ·
+[Companion paper (SSRN)](https://ssrn.com/abstract=6538639).
+
+> **Provenance note.**  This dataset was last refreshed under
+> DataSynth v5.0.  The v5.6 → v5.9 release line ships fixes
+> centred on the journal-entry / accounting-network generators;
+> the banking module producing this dataset was untouched, so the
+> data here is unaffected by those changes and a regeneration is
+> not in scope for the v5.9.0 fleet refresh.
+
+## What's included
+
+| Config | Rows | Columns | What it is |
+|---|---|---|---|
+| (default) | 748,869 | 59 | One row per banking transaction with counterparty, channel, amount, FX, velocity-window aggregates, and AML labels (`is_suspicious`, `typology`, `false_positive`). |
 
-748,869 synthetic banking transactions. Financial services sector, 5 companies, 6 monthly periods. 59 columns including 14 pre-computed velocity features.
+## Schema highlights
 
-- 411 suspicious transactions (0.05%)
-- 37,419 false positives (5% injection rate)
-- 11 transaction channels (ACH, ATM, card, cash, check, mobile, online, P2P, SWIFT, wire)
-- Counterparty details, velocity windows (1h/24h/7d/30d), amount z-scores
+* **Channels (11)** — `ACH`, `ATM`, `card`, `cash`, `check`, `mobile`, `online`, `P2P`, `SWIFT`, `wire`, plus an `internal` transfer category.
+* **Counterparty** — `counterparty_id`, `counterparty_name`, `counterparty_country`, `counterparty_account_type`.
+* **Velocity windows** — 1 h, 24 h, 7 d, 30 d aggregates of incoming + outgoing amounts and counts, plus `amount_zscore_*` and `velocity_zscore_*` features.
+* **AML labels** — 411 suspicious transactions (0.05 %), 37 419 false-positives (5 %).
+* **Typologies covered** — structuring / smurfing, layering, mule activity, round-tripping, fraud, market spoofing.
 
-The suspicious rate is low by design. Production AML systems see comparable rates. The false-positive injection provides realistic noise for threshold calibration.
+## Quick start
+
+```python
+from datasets import load_dataset
+
+txns = load_dataset("VynFi/vynfi-aml-100k", split="train")
+print(txns.features)
+
+# Suspicious-only slice
+suspicious = txns.filter(lambda r: r["is_suspicious"])
+```
+
+## Generation
+
+| | |
+|---|---|
+| **DataSynth release** | v5.0 (banking module — unchanged in v5.6–v5.9) |
+| **Industry** | Financial services |
+| **Period** | 6 monthly periods |
+| **Companies** | 5 |
+| **Suspicious rate** | 0.05 % (matches production AML transaction rates) |
+| **False-positive injection** | 5 % (for threshold calibration) |
+| **Reproducibility** | Determined by the embedded ChaCha8 seed; regenerable from a future v5.9-aware banking config (not yet pinned in `configs/examples/hf/`). |
 
 ## Limitations
 
-- Labels are generated, not from real SARs. Typology patterns are rule-based, not learned from case data.
-- Velocity features are computed per-account over the synthetic timeline. They reflect the generation model's temporal logic, not real transaction behavior.
-- The 100K row parameter produced 748K banking transactions due to the engine's expansion factor. This is a property of the generation, not a data error.
+* Labels are generated, not from real SARs.  Typology patterns are
+  rule-based, not learned from case data.
+* Velocity features are computed per-account over the synthetic
+  timeline.  They reflect the generator's temporal model, not real
+  transaction behaviour.
+* The "100k" in the dataset name refers to the row-count parameter
+  passed to the engine; the banking module's expansion factor
+  produces 748 869 rows.  This is a property of the generation
+  model, not a data error.
+
+## License
+
+Apache-2.0.
 
 ## Citation
 
 ```bibtex
-@dataset{ivertowski_vynfi_aml_2026,
-  title     = {VynFi AML: 748K Banking Transactions with AML Labels},
-  author    = {Michael Ivertowski},
-  year      = {2026},
-  url       = {https://huggingface.co/datasets/VynFi/vynfi-aml-100k},
-  note      = {Generated with VynFi (https://vynfi.com)}
+@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}
 }
 ```
-
-License: Apache 2.0. Entirely synthetic.