Pith Number

pith:UVEZQUFJ

pith:2026:UVEZQUFJAT5TC73UWFPOAEGV3G

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Graph-Based Financial Fraud Detection with Calibrated Risk Scoring and Structural Regularization

Jiawei Wang, Ruobing Yan, Yilun Wu, Yuhan Wang, Yunfei Nie, Zouxiaowei Ma

Graph neural networks that model transaction relationships improve fraud risk ranking and probability calibration.

arxiv:2605.12782 v1 · 2026-05-12 · cs.LG

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\pithnumber{UVEZQUFJAT5TC73UWFPOAEGV3G}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The proposed method outperforms other methods in risk ranking and probability calibration quality, validating the effectiveness of graph structure modeling and representation learning collaboration in financial transaction fraud prevention.

C2weakest assumption

That the transaction graph constructed from shared attributes and interaction consistency accurately captures real inter-transaction relationships without introducing substantial noise or selection bias that affects fraud patterns.

C3one line summary

A graph neural network model for financial fraud detection that incorporates transaction graphs, message passing, weighted supervision, and structural regularization outperforms baselines in risk ranking and probability calibration on a public dataset.

References

2 extracted · 2 resolved · 0 Pith anchors

[1] bib2"><number>[2]</number>Tian Y, Liu G. Transaction fraud detection via spatial-temporal-aware graph transformer[J]. arXiv preprint arXiv:2307.05121, 2023.</bib> <bib id= 2022

[2] ASA-GNN: Adaptive sampling and aggregation-based graph neural network for transaction fraud detection[J] 2021

Receipt and verification

First computed	2026-05-18T03:09:13.126929Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a5499850a904fb317f74b15ee010d5d9bc65b4332180b87de5be37da05c01237

Aliases

arxiv: 2605.12782 · arxiv_version: 2605.12782v1 · doi: 10.48550/arxiv.2605.12782 · pith_short_12: UVEZQUFJAT5T · pith_short_16: UVEZQUFJAT5TC73U · pith_short_8: UVEZQUFJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/UVEZQUFJAT5TC73UWFPOAEGV3G \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: a5499850a904fb317f74b15ee010d5d9bc65b4332180b87de5be37da05c01237

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4aed37072389e5aa6d4416af3113d46209b89f2529568b41dc2eee7c807be123",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-12T21:52:24Z",
    "title_canon_sha256": "b9ecab29fa58d4af76737e05eeb0a04e8196f20494e9ca4ac9dc4dd96ba1f886"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12782",
    "kind": "arxiv",
    "version": 1
  }
}