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Paper Citation Record · LEDGER

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions

As of 17 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2504.15491.

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

pith.paper-citation-record.v1
2504.15491 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:27:31.835651Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:57:11.276601Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T15:57:11.477919Z

Reference resolution

26 of 26 outbound references displayed

  • verified exact1
  • verified fuzzy16
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 56435228-3d6d-4e40-8e3d-8fb1af9ef363 · outbound

This paper cites Generative AI in Financial Fraud Detection.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Generative AI in Financial Fraud Detection

Reference 1

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raw_fallback, observed 2026-08-16T11:27:32.421252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 9aeea23b-a225-4c60-be6f-82c74dfae376 · outbound

This paper cites Financial Fraud Detection System Combining Generative Adversarial Networks and Deep Learning.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Financial Fraud Detection System Combining Generative Adversarial Networks and Deep Learning

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-16T11:27:32.406296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 8050a88d-5d75-4f98-803e-415947d2cae5 · outbound

This paper cites an unresolved cited work.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Unresolved cited work

Reference 3

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raw_fallback, observed 2026-08-16T11:27:32.387933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation 378feeaf-4d03-4a8e-ad32-3536ef24fabf · outbound

This paper cites Generative AI in battling Fraud.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Generative AI in battling Fraud

Reference 4

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raw_fallback, observed 2026-08-16T11:27:32.370949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.732796Z digest=sha256:26c4e2ba8fb6d085434f80ca8505df97722014eeb8975e9e229ec5d5e3b21759

Observation e359f67e-549b-4402-9ca7-6b6c39c9d364 · outbound

This paper cites Synthetic Data Generation for Fraud Detection Using Diffusion Models.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Synthetic Data Generation for Fraud Detection Using Diffusion Models

Reference 5

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raw_fallback, observed 2026-08-16T11:27:32.333770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.737703Z digest=sha256:b79de2e838be64ae6a0170aee98c87731c13735c362672a1cd65364b5b57895c

Observation 51bc43c5-1243-4e6f-b6dd-518dea83b87d · outbound

This paper cites Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks

Reference 6

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no resolver link, observed 2026-08-16T11:27:31.742399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.742399Z digest=sha256:bfc98f13605a9cf6176f09e431df83d79de5e768540941901a99933c66288557

Observation b2f2f3ea-d26d-42a5-a213-f5abd47dfde5 · outbound

This paper cites Credit Card Fraud Detection via Hierarchical Multi-Source Data Fusion and Dropout Regularization,.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Credit Card Fraud Detection via Hierarchical Multi-Source Data Fusion and Dropout Regularization,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-16T11:27:32.235955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.748829Z digest=sha256:dca7d9c5c88b7e5ee47fc17ec7e2028b8658af33393a1f0d1f513a451f446623

Observation d0ba35de-7218-421b-8d56-69734ddff948 · outbound

This paper cites Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference

Reference 8

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no resolver link, observed 2026-08-16T11:27:31.753442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.753442Z digest=sha256:e3da567f490d1c58c287d93dcc102c1a2d2551b742f9e525b782c86fdaa9d066

Observation 51b3d4e7-3aa1-410c-8065-f1d812a2cb03 · outbound

This paper cites Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining

Reference 9

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no resolver link, observed 2026-08-16T11:27:31.758257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.758257Z digest=sha256:48609e4f7614684778826b38341cdcb23b639605a47d6ffa2bc38006f3ca804f

Observation 0d4f49d4-1133-4533-853d-d7199a2afddb · outbound

This paper cites Revisiting LoRA: A Smarter Low-Rank Approach for Efficient Model Adaptation,.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Revisiting LoRA: A Smarter Low-Rank Approach for Efficient Model Adaptation,

Reference 10

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raw_fallback, observed 2026-08-16T11:27:32.150872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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Observation e8168894-bdea-4857-b0c8-f5057085ee81 · outbound

This paper cites Efficient Compression of Large Language Models with Distillation and Fine-Tuning,.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Efficient Compression of Large Language Models with Distillation and Fine-Tuning,

Reference 11

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no resolver link, observed 2026-08-16T11:27:31.767577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.767577Z digest=sha256:1565fd81dcb7df4762fe4c8f08ec7a31d60b58a3f4657a17e15e2f64ef273eb5

Observation b0eae562-498c-4747-a616-1ea6b4d4500c · outbound

This paper cites Deep Learning for Cross-Domain Recommendation with Spatial-Channel Attention,.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Deep Learning for Cross-Domain Recommendation with Spatial-Channel Attention,

Reference 12

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no resolver link, observed 2026-08-16T11:27:31.772251Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.772251Z digest=sha256:77058d26de7169566d15476d5098fbb01d1c23df35f4b9122da16e6f58a4b907

Observation e2b00793-cbec-4316-af0f-cc91e53585c7 · outbound

This paper cites Multimodal Data-Driven Factor Models for Stock Market Forecasting,.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Multimodal Data-Driven Factor Models for Stock Market Forecasting,

Reference 13

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raw_fallback, observed 2026-08-16T11:27:32.116667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.776717Z digest=sha256:8314aac9c62c3c3b6042f66bbce0d196d0ca2d04e9bb43ee21e2fe47cd2589fd

Observation 676ecd57-62fe-492d-89be-917d4a58a2db · outbound

This paper cites Time-Series Premium Risk Prediction via Bidirectional Transformer,.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Time-Series Premium Risk Prediction via Bidirectional Transformer,

Reference 14

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raw_fallback, observed 2026-08-16T11:27:32.101078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.781109Z digest=sha256:dad8f253db178dc72f2dac599a9ab7c1f84e78b3158509b9abc339b8df94519f

Observation 5700cbb6-deb4-48f6-b6d1-f5020d4a3332 · outbound

This paper cites A Reinforcement Learning Approach to Traffic Scheduling in Complex Data Center Topologies,.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions A Reinforcement Learning Approach to Traffic Scheduling in Complex Data Center Topologies,

Reference 15

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no resolver link, observed 2026-08-16T11:27:31.785562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.785562Z digest=sha256:793571451e851ead94c79c68f42fac883e4a16fde9b152189ece7add1debcdc3

Observation 1fdfa2d8-6e1c-4bb3-aba2-41235d63750c · outbound

This paper cites Investigating Hierarchical Term Relationships in Large Language Models,.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Investigating Hierarchical Term Relationships in Large Language Models,

Reference 16

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unresolved
no resolver link, observed 2026-08-16T11:27:31.789842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.789842Z digest=sha256:9e049fb74e68b5f687068c9b6db031e6ffcc0a46286ed26a4072cc0b7addfa59

Observation 1a68d182-1f6e-45f4-b14c-53371a3bbdc8 · outbound

This paper cites A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions A Data Balancing and Ensemble Learning Approach for Credit Card Fraud Detection

Reference 17

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verified exact
local_arxiv, observed 2026-08-16T11:27:31.893881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.794111Z digest=sha256:e816a7b9e1d48a25384a50629f6823ffedc311198cba91804a9eab66dbc5bf89

Observation 45a528b0-af23-438e-b998-61c7d4643860 · outbound

This paper cites Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Unsupervised Detection of Fraudulent Transactions in E-commerce Using Contrastive Learning

Reference 18

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no resolver link, observed 2026-08-16T11:27:31.798911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:27:31.798911Z digest=sha256:ce1cdaaf31e49b2520aaf7ecebe06749af338bd9faba4fdd936411b78d80002f

Observation 401ce41c-edef-4e62-8487-4a78e5b59142 · outbound

This paper cites Audit Fraud Detection via EfficiencyNet with Separable Convolution and Self-Attention.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Audit Fraud Detection via EfficiencyNet with Separable Convolution and Self-Attention

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-16T11:27:32.065755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.803563Z digest=sha256:30841d48434503f3ea22553e7c93e2f1500d4fbb257732fb858a697df011703f

Observation ff5a1237-1ef6-4405-922f-ae27a636520a · outbound

This paper cites A hybrid deep learning approach with generative adversarial network for credit card fraud detection.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions A hybrid deep learning approach with generative adversarial network for credit card fraud detection

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-16T11:27:32.051108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.807982Z digest=sha256:642dc267b5d930f3ad5bdf10606f6e1adc5d460019137e03254ad02deed71ae8

Observation 7c32e648-05c4-4d7c-a38a-d0a26e234847 · outbound

This paper cites Fraud Data Generator: Modelling Sequence Data with Privacy in the Financial Fraud Domain.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Fraud Data Generator: Modelling Sequence Data with Privacy in the Financial Fraud Domain

Reference 21

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verified fuzzy
raw_fallback, observed 2026-08-16T11:27:32.034836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.812428Z digest=sha256:1134e37ad5fd65ab878888b4a76b06432b76d840bc9d64f31d5ce21b2d56a991

Observation 811a830d-3ca5-482f-b0d9-9e310d4d6e80 · outbound

This paper cites Advantages of the PaySim simulator for improving financial fraud controls.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Advantages of the PaySim simulator for improving financial fraud controls

Reference 22

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raw_fallback, observed 2026-08-16T11:27:32.017122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.817792Z digest=sha256:ca9beb6e99b448ba8a8e3f894947dd25477313483fa50cfa1f4d6bae009eb021

Observation c533edc1-72ed-4843-8fba-c7bd989e8c74 · outbound

This paper cites GCT-VAE- GAN: An image enhancement network for low-light cattle farm scenes by integrating fusion gate transformation mechanism and variational autoencoder GAN.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions GCT-VAE- GAN: An image enhancement network for low-light cattle farm scenes by integrating fusion gate transformation mechanism and variational autoencoder GAN

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-16T11:27:32.000911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.822221Z digest=sha256:7a6de6b99119ab510921bf3e8d19102246eda3f65ca719f99b0b717c7b360d92

Observation 7ac66bc9-e638-44d2-90c5-639ebca90d94 · outbound

This paper cites Hemisphere-separated cross-connectome aggregating learning via VAE-GAN for brain structural connectivity synthesis.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Hemisphere-separated cross-connectome aggregating learning via VAE-GAN for brain structural connectivity synthesis

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-16T11:27:31.985533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.826628Z digest=sha256:c4dbba2cd6eaa35dfe57d95567eae91e9696282fe3f267787069a2f16f89bf4c

Observation 35997703-9024-40d2-899d-2f6d628f3db3 · outbound

This paper cites Fraud Detection in Accounting and Finance Enhanced by Knowledge- Driven GAT Networks.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions Fraud Detection in Accounting and Finance Enhanced by Knowledge- Driven GAT Networks

Reference 25

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raw_fallback, observed 2026-08-16T11:27:31.970430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.831159Z digest=sha256:b309a66eab3c593e57480969c3bbc007aacb6a74c613e94d0ba63396583d85f9

Observation 6b1b8d42-ac0b-4353-888d-cbc3429e5d71 · outbound

This paper cites The role of finance in environmental innovation diffusion: An evolutionary modeling approach.

Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions The role of finance in environmental innovation diffusion: An evolutionary modeling approach

Reference 26

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raw_fallback, observed 2026-08-16T11:27:31.955260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:27:31.835651Z digest=sha256:2ca543b5316ca2eec5d2ee568feaf190e0802afb205b965d5e9797b035f5eafc

Pith citing papers

Observation d66cc7e4-2008-4343-91d1-ccedd10f0798 · inbound

Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling cites this paper.

Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions

Reference 30

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verified exact
local_arxiv, observed 2026-08-06T15:57:11.499980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-06T15:57:11.276601Z digest=sha256:7deb3ffb606fb64a1dff9e183539ce7ee2bfe6a314613745166ffb5082749bab