Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T12:58:49.057313Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T12:58:49.057313Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
32 of 32 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 638fbad9-2234-4343-b716-ea32e4698168 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Xgboost: A scal- able tree boosting system
Reference 1
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fbdcc08a-7230-47a5-9eea-bb19d24bf4b8 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a50554d4-2764-4784-82ea-f465869ffeeb · outbound
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
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5244ddcd-b6f3-4373-9232-3f3c6b1d6dbb · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4dd0879a-7b87-4b82-8bf3-a28aeff71ce8 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Ai fraud detection in banking 2026 guide,
Reference 5
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 201fb4b9-db3c-422b-ab98-6f04385c3401 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc0a0225-750a-4d2e-b59e-5f4245e2a9d9 · outbound
Reference 7
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8f0650a9-48fa-4dd4-a1c6-b959bf938ec1 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation node2vec: Scal- able feature learning for networks
Reference 8
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc7208f0-0ddd-4695-8204-fd6f8691d71a · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e3e65cff-1556-4f86-ae3b-9a2b47668b46 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Kipf and Max Welling
Reference 10
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5e003f43-d194-49f2-9e01-d7f92b7ff776 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 889a7cda-67de-4c33-8ddf-dfaceee356a8 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d8113428-f5e6-4150-a8bf-216ad5faf267 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2b28912-91e4-43ed-ada5-23ce54c7ee5f · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation PaySim: A financial mobile money simulator for fraud detection
Reference 14
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3c677561-7c20-4ecc-a69a-9969bf20b0aa · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Lundberg and Su-In Lee
Reference 15
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 324993a9-dbc6-41ca-a18f-8c94ade5c895 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M
Reference 16
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c56ccc04-b120-41f9-b5af-ab7d9d2ee147 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work
Reference 17
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 4a97dd3e-aaa4-4c17-9456-52f7f26c698f · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c3faf681-cadb-4ae3-8fe6-bedbae1e6af1 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b4e877d9-2107-4865-ae48-1363ca0496a1 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d7eab0d5-36da-4f91-adb1-f7bd5f79a68e · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac1ba197-400d-4fe7-b52a-0a76fdbc8e5b · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4b8d65b2-ef9a-4150-bbe9-3d1d98c219f5 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Kam, and Yee Ling Boo
Reference 23
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 03278012-5744-4631-878d-92fafb93b9e0 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work
Reference 24
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation d9d44ea5-00bb-448b-958e-9560d2d499b3 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Stripe radar: fraud detection architecture and model choices
Reference 25
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fe1ecee2-98c5-4244-9c6d-17b6a1e511a6 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 540e4d91-24d1-4d77-9a08-e3d7792a59cb · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e74629e3-9718-49db-9d68-b962b0574cf2 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8f1df6cd-f6fe-4baf-86d0-94168741c309 · outbound
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
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f82b92a2-daef-4f4b-ba51-832dc9afbdf1 · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work
Reference 2016
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6e9ef00c-dd64-40f4-8fda-4ef3e14686ff · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work
Reference 2020
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Unavailable: canonical work link unavailable.
Observation f6c72af0-e4a3-4b0e-9403-518cfc68a68c · outbound
Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation Unresolved cited work
Reference 2026
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
No inbound Pith citation observations are available.