---
license:
- cdla-sharing-1.0
- mit
license_link: https://cdla.dev/sharing-1-0/
task_categories:
- graph-ml
- text-classification
language:
- en
tags:
- anti-money-laundering
- aml
- fraud-detection
- finance
- transaction-graphs
- graph-ml
- llm-benchmark
- evaluation
- graph-reasoning
- chain-of-thought
- coreset
- horvitz-thompson
- importance-weighting
- synthetic
annotations_creators:
- machine-generated
language_creators:
- machine-generated
source_datasets:
- extended|other-amlworld
size_categories:
- 1K<n<10K
pretty_name: AMLworld-Compact
configs:
- config_name: HI-Small
  default: true
  data_files:
  - split: test
    path: data/HI-Small/test-*.parquet
- config_name: LI-Small
  data_files:
  - split: test
    path: data/LI-Small/test-*.parquet
---

# AMLworld-Compact

Paper: [arXiv:2610.13627](https://arxiv.org/abs/2610.13627) (under review) · [Code & evaluation](https://github.com/nat-nischw/AMLworld-Compact) · [Baseline results](https://github.com/nat-nischw/AMLworld-Compact#baselines)

AMLworld-Compact is an evaluation set of 6,021 cases for anti-money-laundering
research. HT-Coreset selects these cases from the HI-Small and LI-Small
file-order test partitions of IBM's synthetic AMLworld dataset. Each case
contains a local graph extracted from the full source graph and serialised
as text, a target transaction ID in metadata,
an illicit/benign label, an optional laundering typology, and an importance
weight for estimating full-split metrics.

![HT-Coreset selects fewer evaluation targets; the same predictions feed HT-weighted full-test estimates and unweighted subset diagnostics.](assets/readme_overview.png)

The graph inputs used in the reported LLM runs are described in
[Evaluation limitations](#evaluation-limitations).

| Configuration | Evaluation cases | Illicit cases retained | Full test edges | Reduction |
|---|---:|---:|---:|---:|
| `HI-Small` | 3,753 | 1,251 | 1,015,669 | 271× |
| `LI-Small` | 2,268 | 756 | 1,384,810 | 611× |

The source 60/20/20 partition follows released CSV row order without timestamp
sorting. Time ranges overlap across train, validation, and test. The released
coreset draw uses sampling seed 0.

Both configurations contain a single `test` split. All illicit edges are
retained; benign edges are sampled using supervised difficulty strata. The
released set has one illicit case for every two benign cases: the smallest
benign budget evaluated in the construction study, giving its largest tested
reduction.

Each row carries `case_id`, `center_edge_id`, `subset_index`, `label` (with the
equivalent `illicit` flag), `typology` and `typology_id` (0–7, or −1 when
unannotated), `ht_weight`, `n_transactions`, `n_tokens_approx`, and
`typed_graph_text`. Typology IDs 0–7 correspond to `fan-out`, `fan-in`,
`cycle`, `scatter-gather`, `gather-scatter`, `stack`, `bipartite`, and
`random`, in that order.

## Quick start

```bash
pip install datasets scikit-learn
```

```python
from datasets import load_dataset

ds = load_dataset("typhoon-ai/AMLworldCompactEval", "HI-Small", split="test")
case = ds[0]
print(case["case_id"])
print(case["typed_graph_text"])
```

Use `"LI-Small"` to load the other configuration.

The main LLM runs used `typed_graph_text` as the graph input; `label`,
`illicit`, `typology`, and `typology_id` are for scoring. Task instructions and
few-shot examples are in the code repository's
[prompt templates](https://github.com/nat-nischw/AMLworld-Compact/tree/main/prompts).
`ICL-FS` and `ICL-ZS` share task instructions and graph inputs; `ICL-FS` adds
eight illicit and four benign training demonstrations.
Graphs are extracted over two hops, with at most 50 neighbours per expanded
account per hop, counting incoming and outgoing neighbours together. For new
target-level evaluations, use the
[target-marked inputs](#target-marked-inputs-for-new-evaluations).

The dataset viewer may shorten long cells; loading the dataset returns the
complete text.

The supplementary [frontier-API probe](https://github.com/nat-nischw/AMLworld-Compact/tree/main/results/frontier_probe)
uses 198 HI-Small cases with separate instructions and condensed graph inputs.
Its `ZS-Graph` and `ZS-Base` variants both have no demonstrations; the few-shot
variants also add account roles and computed structural features.

## Target-marked inputs for new evaluations

`extras/targeted_prompts_v1/` provides a separate input version for the same
6,021 targets. Each `evaluation_prompt` explicitly identifies the target,
includes its transaction exactly once, and gives bank-qualified endpoints and
elapsed time over the included graph. Outcome labels are excluded from the
prompt. Target inclusion, transaction fields, endpoint presence, and time
summaries are checked against the source records.

```python
import pandas as pd
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="typhoon-ai/AMLworldCompactEval",
    repo_type="dataset",
    filename="extras/targeted_prompts_v1/HI-Small/test-00000-of-00001.parquet",
)
prompts = pd.read_parquet(path)
text_to_send = prompts.iloc[0]["evaluation_prompt"]
```

Use the complete `evaluation_prompt` directly. Join predictions to the original
scoring table by configuration and `case_id`; case order, target IDs, and weights
are retained. The original `data/` tables and published LLM predictions still
refer to the archived format. No model results are reported for this new input
version. Edge counts are preserved by replacing a non-target edge when the
target was absent; the revised text can have different token lengths. Context is still drawn from the full source graph, without a prospective
time cutoff. The new instructions, target fields, endpoint names, time summary,
and occasional edge replacements change the representation together.
The [manifest](extras/targeted_prompts_v1/manifest.json) and
[schema](extras/targeted_prompts_v1/schema.json) document the checks and provenance.

## Evaluate predictions

Report Horvitz–Thompson (HT)-weighted precision, recall, and F1 as the primary
detection metrics.
Given one binary `predictions` array in dataset row order (`1` = illicit,
`0` = benign):

```python
from sklearn.metrics import precision_recall_fscore_support

precision, recall, f1, _ = precision_recall_fscore_support(
    ds["label"],
    predictions,
    average="binary",
    sample_weight=ds["ht_weight"],
    zero_division=0,
)
print(f"P={precision:.4%}  R={recall:.4%}  F1={f1:.4%}")
```

Join external predictions by `(configuration, case_id)` before scoring:
case IDs repeat across configurations. Choose any decision threshold using
separate validation data.

Both summaries use the same targets selected by HT-Coreset and the same predictions:

| Summary | What it measures |
|---|---|
| HT-weighted (full test) | Estimates for the full source test partition, using `ht_weight` |
| Unweighted (subset) | Scores on the same released 1:2 subset with every weight set to one, used for diagnosis |

For fixed predictions and positive inclusion probabilities, weighted confusion
counts are unbiased estimates of full-test counts. Precision and F1 are ratios
of those counts and can have sampling bias and variance. Recall is unchanged
by weighting because every illicit edge is retained with weight one.
Predicting every case illicit gives 50% unweighted F1, so that value alone
does not establish useful detection.

The reported typology macro-F1 (TF1) covers 791 HI-Small and 174 LI-Small
illicit cases with a known ground-truth typology, including missed detections.
A benign verdict or missing predicted typology is scored as `none`. The macro
average uses the union of true and predicted labels, including `none` or
`legitimate` when present. An unannotated reference typology does not mean
benign; those illicit cases remain in detection evaluation but are excluded
from TF1.
Published TF1 values were computed from archived, postprocessed labels; the
companion code's current live parser does not reproduce every historical
free-text fallback.

The [evaluation guide](https://github.com/nat-nischw/AMLworld-Compact#evaluation)
provides commands to score the released ensemble, run LLMs, and evaluate saved
predictions.

The code also provides a
[sampling-uncertainty analysis](https://github.com/nat-nischw/AMLworld-Compact/blob/main/results/analysis/sampling_uncertainty/README.md)
and a
[held-out-family study](https://github.com/nat-nischw/AMLworld-Compact/blob/main/results/analysis/heldout_sampling/README.md).
The latter uses cached full-test GFP-booster predictions over 500 draws per
configuration; it does not compare full-test and coreset LLM predictions.

## Files

`load_dataset()` loads only the evaluation tables, about 31.7 MiB across
both configurations.

| Directory | Contents |
|---|---|
| `data/<configuration>/` | Test Parquet files with graph text, labels, and weights |
| `extras/<configuration>/` | Aligned scoring arrays, graph features, and case indices, in evaluation-table row order |
| `extras/targeted_prompts_v1/` | Separately versioned target-marked prompts, schema, and integrity manifest; no new model predictions |
| `ml_baselines/` | Supervised checkpoints under `weights/` and full-test predictions under `test_probs/`, five seeds each |

The code repository provides
[loaders and scoring examples](https://github.com/nat-nischw/AMLworld-Compact#score-the-released-supervised-ensemble)
for the arrays and checkpoints.

## Models

GFP means Graph Feature Preprocessor. Each booster uses 73 GFP signals and
6 raw transaction attributes (79 inputs). GCPAL adds five line-graph-derived
features, giving 84 inputs for that construction scorer.
The primary ML reference averages LightGBM+GFP and XGBoost+GFP across
five training seeds. Both are trained on the training portion of the file-order
partition. Their probabilities are in `extras/*/ensemble_probs_coreset.npy`.
The inherited thresholds are 0.80 for HI-Small and 0.48 for LI-Small; they were
selected on the full test set for the original construction scorer and have
not been retuned.

The frozen construction ensemble also included GCPAL+GFP, whose random
fine-tuning split overlaps roughly 60% of the test edges. The original
three-model probabilities remain in `construction_probs_coreset.npy` to
reproduce the sampling design;
GCPAL is excluded from the primary task comparisons. Each configuration's
`scoring_metadata.json` distinguishes the two roles. The seven evaluated LLMs
are listed in the code repository's
[baselines table](https://github.com/nat-nischw/AMLworld-Compact#baselines).

## Evaluation limitations

The reported LLM runs used the original graph text for all 6,021 cases.
Target IDs are stored in `center_edge_id` metadata. The graph text does not
explicitly mark the target, and the task template retains the literal `<ID>`.
In 324/3,753 HI-Small and 297/2,268 LI-Small cases,
the target transaction itself is absent after neighbour capping, including
128 and 181 illicit targets respectively. The `Time span` field counts distinct
timestamps rather than elapsed time.

The original graph strings are retained to reproduce the reported runs.
The LLM scores and trace analyses describe model behaviour under this input
format and do not isolate input effects from reasoning errors. A separate
[target-marked version](#target-marked-inputs-for-new-evaluations) is available
for new evaluations.

## Uses

This synthetic benchmark supports research on transaction classification,
graph-to-text prompting, and error analysis. Use the released test split for
evaluation and separate data for training and tuning. Results describe the
released AMLworld splits and do not establish performance on real banking
transactions.

## Licences and source

Derived from
[IBM AMLworld](https://www.kaggle.com/datasets/ealtman2019/ibm-transactions-for-anti-money-laundering-aml).
The original transaction CSVs are not included.

- Data and features (`data/`, `extras/`): [CDLA-Sharing-1.0](./LICENSE-CDLA-SHARING-1.0.txt).
- Supervised model parameters and outputs (`ml_baselines/`): MIT.

See [NOTICE.md](./NOTICE.md) for attribution and licence scope.

## Contact

Open a [GitHub issue](https://github.com/nat-nischw/AMLworld-Compact/issues) for
questions about the dataset or the code.

## Acknowledgments

This work was initiated at SCB 10X in late 2025, following the release of our
earlier work, [FinCoT](https://arxiv.org/abs/2506.16123). We are grateful to
[Oravee Smithiphol](https://th.linkedin.com/in/ornsmith) for introducing our
team to Phume Ngampornsukswadi, whose interest in FinCoT led to this
collaboration. We also thank the members of the
[Typhoon team](https://opentyphoon.ai/) for their support, and Duncan Halverson
for contributing to the early stages of this work.

## Citation

<details>
<summary>BibTeX</summary>

```bibtex
@misc{nitarach2026amlworldcompactimportanceweighteddownsamplingcosteffective,
      title={AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis}, 
      author={Natapong Nitarach and Phume Ngampornsukswadi and Pittawat Taveekitworachai and Surapon Nonesung and Warit Sirichotedumrong and Kunat Pipatanakul},
      year={2026},
      eprint={2610.13627},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2610.13627}, 
}
```

Please also cite AMLworld. Its citation is included in the companion
[README citation section](https://github.com/nat-nischw/AMLworld-Compact#citation).

</details>