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Paper Citation Record · LEDGER

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

As of 7 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.19266.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.19266 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T12:58:49.057313Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved27
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 638fbad9-2234-4343-b716-ea32e4698168 · outbound

This paper cites Xgboost: A scal- able tree boosting system.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Xgboost: A scal- able tree boosting system

Reference 1

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Observation fbdcc08a-7230-47a5-9eea-bb19d24bf4b8 · outbound

This paper cites SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation SAGE: An LLM-driven Self Reflective Agentic Framework for Fraud Detection

Reference 2

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Observation a50554d4-2764-4784-82ea-f465869ffeeb · outbound

This paper cites Graph neural networks for financial fraud detec- tion: A review.Frontiers of Computer Science, 2025.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Graph neural networks for financial fraud detec- tion: A review.Frontiers of Computer Science, 2025

Reference 3

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Observation 5244ddcd-b6f3-4373-9232-3f3c6b1d6dbb · outbound

This paper cites How paypal’s ai blocks$500 million in fraud per quarter, 2026.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation How paypal’s ai blocks$500 million in fraud per quarter, 2026

Reference 4

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source=pdf_text observed=2026-08-01T12:58:46.204750Z digest=sha256:d351d363864f1df59e17ddb5cb8ca140440ad4907d7f6a119b2af40db2f377f6

Observation 4dd0879a-7b87-4b82-8bf3-a28aeff71ce8 · outbound

This paper cites Ai fraud detection in banking 2026 guide,.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Ai fraud detection in banking 2026 guide,

Reference 5

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Observation 201fb4b9-db3c-422b-ab98-6f04385c3401 · outbound

This paper cites Experian’s new fraud forecast warns agentic ai, deepfake job candidates and cyber break-ins are top threats for 2026, 2026.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Experian’s new fraud forecast warns agentic ai, deepfake job candidates and cyber break-ins are top threats for 2026, 2026

Reference 6

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Observation fc0a0225-750a-4d2e-b59e-5f4245e2a9d9 · outbound

This paper cites Friedman.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Friedman

Reference 7

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source=pdf_text observed=2026-08-01T12:58:46.594760Z digest=sha256:53fcd84bfa217075657a9b1379a8575d0fc817c095c42d9e4d8fe429e35a5b3d

Observation 8f0650a9-48fa-4dd4-a1c6-b959bf938ec1 · outbound

This paper cites node2vec: Scal- able feature learning for networks.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation node2vec: Scal- able feature learning for networks

Reference 8

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Observation fc7208f0-0ddd-4695-8204-fd6f8691d71a · outbound

This paper cites Can llms find fraudsters? multi- level llm enhanced graph fraud detection.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Can llms find fraudsters? multi- level llm enhanced graph fraud detection

Reference 9

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Observation e3e65cff-1556-4f86-ae3b-9a2b47668b46 · outbound

This paper cites Kipf and Max Welling.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Kipf and Max Welling

Reference 10

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Observation 5e003f43-d194-49f2-9e01-d7f92b7ff776 · outbound

This paper cites Se- fraud: Graph-based self-explainable fraud detection via interpretative mask learning.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Se- fraud: Graph-based self-explainable fraud detection via interpretative mask learning

Reference 11

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source=pdf_text observed=2026-08-01T12:58:46.867507Z digest=sha256:37d533b9cb2af94ba75b2d3e30d4e7a966220ef65f8d092dc89c8f23af99f451

Observation 889a7cda-67de-4c33-8ddf-dfaceee356a8 · outbound

This paper cites Autonomous chain-of-thought distillation for graph-based fraud detection, 2026.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Autonomous chain-of-thought distillation for graph-based fraud detection, 2026

Reference 12

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source=pdf_text observed=2026-08-01T12:58:46.959743Z digest=sha256:ef1c5ba1d2fbac12c4fa9f929c375cb1d2cf88a5e9cd16d85431885c24204c2f

Observation d8113428-f5e6-4150-a8bf-216ad5faf267 · outbound

This paper cites Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects

Reference 13

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Observation b2b28912-91e4-43ed-ada5-23ce54c7ee5f · outbound

This paper cites PaySim: A financial mobile money simulator for fraud detection.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation PaySim: A financial mobile money simulator for fraud detection

Reference 14

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Observation 3c677561-7c20-4ecc-a69a-9969bf20b0aa · outbound

This paper cites Lundberg and Su-In Lee.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Lundberg and Su-In Lee

Reference 15

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Observation 324993a9-dbc6-41ca-a18f-8c94ade5c895 · outbound

This paper cites Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M

Reference 16

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source=pdf_text observed=2026-08-01T12:58:47.364733Z digest=sha256:ed092e403cbd2fe52511bd823dc46a37f89ae8147dd638bced6088c9406807df

Observation c56ccc04-b120-41f9-b5af-ab7d9d2ee147 · outbound

This paper cites an unresolved cited work.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work

Reference 17

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 4a97dd3e-aaa4-4c17-9456-52f7f26c698f · outbound

This paper cites Agentic ai: Streamlining the future of ach fraud detection, 2026.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Agentic ai: Streamlining the future of ach fraud detection, 2026

Reference 18

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Observation c3faf681-cadb-4ae3-8fe6-bedbae1e6af1 · outbound

This paper cites A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation A Comparison Study of Credit Card Fraud Detection: Supervised versus Unsupervised

Reference 19

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Observation b4e877d9-2107-4865-ae48-1363ca0496a1 · outbound

This paper cites A label-free heterophily-guided approach for unsupervised graph fraud detection.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation A label-free heterophily-guided approach for unsupervised graph fraud detection

Reference 20

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Observation d7eab0d5-36da-4f91-adb1-f7bd5f79a68e · outbound

This paper cites Correcting false alarms from unseen: Adapting graph anomaly detectors at test time.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Correcting false alarms from unseen: Adapting graph anomaly detectors at test time

Reference 21

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Observation ac1ba197-400d-4fe7-b52a-0a76fdbc8e5b · outbound

This paper cites How paypal uses real-time graph database and graph analysis to fight fraud, 2021.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation How paypal uses real-time graph database and graph analysis to fight fraud, 2021

Reference 22

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Observation 4b8d65b2-ef9a-4150-bbe9-3d1d98c219f5 · outbound

This paper cites Kam, and Yee Ling Boo.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Kam, and Yee Ling Boo

Reference 23

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Observation 03278012-5744-4631-878d-92fafb93b9e0 · outbound

This paper cites an unresolved cited work.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work

Reference 24

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d9d44ea5-00bb-448b-958e-9560d2d499b3 · outbound

This paper cites Stripe radar: fraud detection architecture and model choices.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Stripe radar: fraud detection architecture and model choices

Reference 25

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Observation fe1ecee2-98c5-4244-9c6d-17b6a1e511a6 · outbound

This paper cites Explainability in graph neural networks: A taxonomic survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5782–5799, 2023.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Explainability in graph neural networks: A taxonomic survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):5782–5799, 2023

Reference 26

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Observation 540e4d91-24d1-4d77-9a08-e3d7792a59cb · outbound

This paper cites Agentic ai in payments in 2026: What’s real, what’s pilot and what’s still hype.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Agentic ai in payments in 2026: What’s real, what’s pilot and what’s still hype

Reference 27

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Observation e74629e3-9718-49db-9d68-b962b0574cf2 · outbound

This paper cites The precision– recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation The precision– recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

Reference 28

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Observation 8f1df6cd-f6fe-4baf-86d0-94168741c309 · outbound

This paper cites Let Relations Speak: An End-to-End LLM-GNN Soft Prompt Framework for Fraud Detection.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Let Relations Speak: An End-to-End LLM-GNN Soft Prompt Framework for Fraud Detection

Reference 32

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Observation f82b92a2-daef-4f4b-ba51-832dc9afbdf1 · outbound

This paper cites an unresolved cited work.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work

Reference 2016

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Observation 6e9ef00c-dd64-40f4-8fda-4ef3e14686ff · outbound

This paper cites an unresolved cited work.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work

Reference 2020

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Observation f6c72af0-e4a3-4b0e-9403-518cfc68a68c · outbound

This paper cites an unresolved cited work.

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work

Reference 2026

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Pith citing papers

No inbound Pith citation observations are available.