---
title: "nelgiriyewithana/credit-card-fraud-detection-dataset-2023"
description: "4 papers in the tracked journals link to the Kaggle dataset nelgiriyewithana/credit-card-fraud-detection-dataset-2023 in their Crossref records, the first in 2025; most of them (4) in 2025."
url: https://scholar4ai.com/code/kaggle-nelgiriyewithana-credit-card-fraud-detection-dataset-2023
markdown_url: https://scholar4ai.com/code/kaggle-nelgiriyewithana-credit-card-fraud-detection-dataset-2023.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "4 papers in the tracked journals link to the Kaggle dataset nelgiriyewithana/credit-card-fraud-detection-dataset-2023 in their Crossref records, the first in 2025; most of them (4) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# nelgiriyewithana/credit-card-fraud-detection-dataset-2023: papers linking to it

Research papers in the 480 tracked journals that link to nelgiriyewithana/credit-card-fraud-detection-dataset-2023 (https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Kaggle
- https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023

All repositories: https://scholar4ai.com/code.md

## Key findings

- 4 papers in the tracked journals link to the Kaggle dataset nelgiriyewithana/credit-card-fraud-detection-dataset-2023 in their Crossref records, the first in 2025; most of them (4) in 2025.
- By category: Economics (2) and AI (2); the journals with the most are Computational Economics (2), Expert Systems with Applications (1) and Journal Of Big Data (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links nelgiriyewithana/credit-card-fraud-detection-dataset-2023.

## By year

*Papers linking to nelgiriyewithana/credit-card-fraud-detection-dataset-2023 by publication year*

| Year | Papers |
|---|---:|
| 2025 | 4 |

## Journals

*Journals whose papers link to nelgiriyewithana/credit-card-fraud-detection-dataset-2023 most*

| Journal | Papers |
|---|---:|
| [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | 2 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [A systematic review of AI-enhanced techniques in credit card fraud detection](https://doi.org/10.1186/s40537-024-01048-8) (Journal Of Big Data, 2025)
  - [Benchmarking Credit Card Fraud Detection Models: A Comprehensive Evaluation Across Diverse Datasets](https://doi.org/10.1007/s10614-025-11234-2) (Computational Economics, 2025) — cites the paper above

## Papers

*Papers linking to nelgiriyewithana/credit-card-fraud-detection-dataset-2023, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2025-01-14 | [A systematic review of AI-enhanced techniques in credit card fraud detection](https://doi.org/10.1186/s40537-024-01048-8) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Ibrahim Y. Hafez, Ahmed Y. Hafez, Ahmed Saleh et al. | 111 | cites it |
| 2025-08-29 | [Enhancing Fraud Detection in Credit Card Transactions: A Comparative Study of Machine Learning Models](https://doi.org/10.1007/s10614-025-11071-3) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Masad A. Alrasheedi | 20 | cites it |
| 2025-12-29 | [Benchmarking Credit Card Fraud Detection Models: A Comprehensive Evaluation Across Diverse Datasets](https://doi.org/10.1007/s10614-025-11234-2) | [Computational Economics](https://scholar4ai.com/journals/computational-economics.md) | Deepankur Singh, Simranjit Singh, Neeraj Kumar | 3 | cites it |
| 2025-07-15 | [CFARMS: A clustered federated learning framework with recursive model selection](https://doi.org/10.1016/j.eswa.2025.128931) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Ebenezer Nanor, Bernard M․ Cobbinah, Qinli Yang et al. | 1 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). nelgiriyewithana/credit-card-fraud-detection-dataset-2023: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-nelgiriyewithana-credit-card-fraud-detection-dataset-2023

```bibtex
@misc{scholar4ai-code-kaggle-nelgiriyewithana-credit-card-fraud-detection-dataset-2023,
  author = {{Scholar4AI}},
  title = {nelgiriyewithana/credit-card-fraud-detection-dataset-2023: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-nelgiriyewithana-credit-card-fraud-detection-dataset-2023}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).