Paper Citation Record · LEDGER

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network

As of 8 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2506.19871.

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

pith.paper-citation-record.v1
2506.19871 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:27:52.156765Z

measured 27 of 27 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

27 of 27 outbound references displayed

verified exact0
verified fuzzy18
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 139735a5-822d-43e5-bd24-936194d1e252 · outbound

This paper cites An intelligent machine learning approach for fraud detection in medical claim insurance: A comprehensive study.Scholars Journal of Engineering and Technology, 11(9):191–200, 2023.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network An intelligent machine learning approach for fraud detection in medical claim insurance: A comprehensive study.Scholars Journal of Engineering and Technology, 11(9):191–200, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.601424Z

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 b53018a1-ff93-4aa6-b4e7-9c3b41a184ad · outbound

This paper cites Implementation of a faith community nursing transition of care program in the usa: A propensity score matching analysis.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Implementation of a faith community nursing transition of care program in the usa: A propensity score matching analysis

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.544740Z

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 dafdf78d-0ed8-4b66-b7ff-1120b27c8d2a · outbound

This paper cites an unresolved cited work.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:27:52.471602Z

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 d5d69ec6-db73-4f99-a7f0-757640509304 · outbound

This paper cites an unresolved cited work.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:27:52.456743Z

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 3434bf8f-78c4-48f2-a2a0-2a299c8e433c · outbound

This paper cites The mediating role of medical Title Suppressed Due to Excessive Length 13 service geographical availability between the healthcare service quality and the medical insurance.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network The mediating role of medical Title Suppressed Due to Excessive Length 13 service geographical availability between the healthcare service quality and the medical insurance

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.410983Z

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 0fd96b98-23e9-4951-ad50-b68498d930ba · outbound

This paper cites Medicare fraud detection using machine learning methods.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Medicare fraud detection using machine learning methods

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.391158Z

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 32df3df9-ffab-4d58-9b61-05b751f5820c · outbound

This paper cites Adversarial attack vulnerability of medical image analysis systems: Unexplored factors.Medical Image Analysis, 73:102141, 2021.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Adversarial attack vulnerability of medical image analysis systems: Unexplored factors.Medical Image Analysis, 73:102141, 2021

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.381462Z

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 973db17c-ac93-4dec-ab5a-4884e51986c0 · outbound

This paper cites A survey on adversarial attacks and defences.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network A survey on adversarial attacks and defences

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.371162Z

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 0539410e-623f-4f68-8cd0-f6ac8d757c80 · outbound

This paper cites Advancing fraud detection through deep learning: A comprehensive review.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Advancing fraud detection through deep learning: A comprehensive review

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.358254Z

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 2bc770d4-8d7b-466a-ae06-98fd592a228f · outbound

This paper cites Redefining insurance through technology: Achievements and perspectives in insurtech.Research in International Business and Finance, page 102301, 2024.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Redefining insurance through technology: Achievements and perspectives in insurtech.Research in International Business and Finance, page 102301, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.348708Z

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 fb38c9a7-0a22-4a33-ada7-e18e0cd1bcf9 · outbound

This paper cites Adversarial attacks on medical machine learning.Science, 363(6433):1287–1289, 2019.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Adversarial attacks on medical machine learning.Science, 363(6433):1287–1289, 2019

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.338470Z

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 11d64da2-ccff-42c4-bf37-cb999d174614 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Explaining and Harnessing Adversarial Examples

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:52.109859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67ab8bc2-12e0-456c-94e9-c28e3d31b778 · outbound

This paper cites Big data fraud detection using multiple medicare data sources.Journal of Big Data, 5(1):1–21, 2018.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Big data fraud detection using multiple medicare data sources.Journal of Big Data, 5(1):1–21, 2018

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.329504Z

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 70347eb9-4753-47e1-bfde-1988efe5d8cf · outbound

This paper cites Comparingmedicareplanselectionamongbenefi- ciaries with and without a history of cancer.Health Affairs Scholar, 2(2):qxae014, 2024.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Comparingmedicareplanselectionamongbenefi- ciaries with and without a history of cancer.Health Affairs Scholar, 2(2):qxae014, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.318870Z

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 369c9c53-e293-486a-9dad-b37eb6dea052 · outbound

This paper cites Medicare fraud detection using neural networks.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Medicare fraud detection using neural networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.308609Z

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 1ddeff96-3a5f-44d4-a6cc-2f7434d4fc71 · outbound

This paper cites an unresolved cited work.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:27:52.298632Z

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 3f592bd5-2541-49b6-bfc4-a9fd6d531486 · outbound

This paper cites an unresolved cited work.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:27:52.288623Z

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 89514c0c-a8ef-40a8-99ad-2a955e91f15c · outbound

This paper cites Adversarial machine learning-industry perspectives.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Adversarial machine learning-industry perspectives

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.278433Z

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 3e32ae10-9807-4e70-a6ae-bbb8d95c2d04 · outbound

This paper cites Adversarial Machine Learning at Scale.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Adversarial Machine Learning at Scale

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:52.132389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c6ace91-8693-429e-8bd4-b383306ea0a3 · outbound

This paper cites Future of generative adversarial networks (gan) for anomaly detection in network security: A review.Computers & Security, 139:103733, 2024.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Future of generative adversarial networks (gan) for anomaly detection in network security: A review.Computers & Security, 139:103733, 2024

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.267961Z

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 e5a15782-85eb-4c81-9781-cb6ccfb59365 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:52.138923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 404855ee-5ebc-445b-8c2b-ca9cd27dec19 · outbound

This paper cites Adversarial Robustness Toolbox v1.0.0.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Adversarial Robustness Toolbox v1.0.0

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:52.142469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2119853c-bb6a-4161-9c04-a2b138220856 · outbound

This paper cites Residual attention unet gan model for enhancing the intelligent agents in retinal image analysis.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Residual attention unet gan model for enhancing the intelligent agents in retinal image analysis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.258393Z

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 911332e4-f45c-4604-bec9-425b9acd3b0a · outbound

This paper cites Syn-gan: A robust intrusion detection system using gan-based synthetic data for iot security.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Syn-gan: A robust intrusion detection system using gan-based synthetic data for iot security

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.248378Z

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 273615b6-2238-493a-ab16-7513985feeef · outbound

This paper cites Metaheuristic-based hyperparameter optimization for multi-disease detection and diagnosis in machine learning.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Metaheuristic-based hyperparameter optimization for multi-disease detection and diagnosis in machine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.238079Z

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 36dde821-5447-4851-9d72-63fb22767623 · outbound

This paper cites Medicare fraud detection using graph analysis: A comparative study of machine learning and graph neural networks.IEEE Access, 11:88278–88294, 2023.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Medicare fraud detection using graph analysis: A comparative study of machine learning and graph neural networks.IEEE Access, 11:88278–88294, 2023

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:27:52.228390Z

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 f3b6c996-1c82-4a01-98db-1f2ac7ca7800 · outbound

This paper cites Efficient adversarial training with transferable adversarial examples.

An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network Efficient adversarial training with transferable adversarial examples

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:52.156765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.