Evidence map›Paper›PMID 40989315›Full record

ArticlePeerJ. Computer science2025

Gated attention based generative adversarial networks for imbalanced credit card fraud detection.

Jiangmeng Ge, Lanxiang Yin, Shiqing Zhang, Xiaoming Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jiangmeng GeInstitute of Intelligent Information Processing, Taizhou University, Taizhou, Zhejiang, China.
Lanxiang YinSchool of Business, Taizhou University, Taizhou, Zhejiang, China.
Shiqing ZhangInstitute of Intelligent Information Processing, Taizhou University, Taizhou, Zhejiang, China.
Xiaoming ZhaoInstitute of Intelligent Information Processing, Taizhou University, Taizhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Credit card fraud detection is highly important to maintain financial security. However, it is challenging to train suitable models due to the class imbalance in credit card transaction data. To address this issue, this work proposes a novel deep learning framework, gated attention-based generative adversarial networks (GA-GAN) for credit card fraud detection in class-imbalanced data. GA-GAN integrates GAN and the gated attention mechanism to generate high-quality synthetic data that realistically simulates fraudulent behaviors. Experimental results on two public credit card datasets demonstrate that GA-GAN outperforms state-of-the-art methods on credit card fraud detection tasks in class-imbalanced data, indicating the advantage of GA-GAN. The code is publicly available at https://github.com/Gejiangmeng/gagan/tree/main.

Indexed as

Attention mechanismClass imbalanceCredit card fraud detectionDeep learningGenerative adversarial networks

Identifiers

PMID40989315
PMCPMC12453723

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.