---
title: "Home Credit: papers using this data, journals and trends"
description: "From Nov 2023 to Oct 2026, 13 archived papers mentioned Home Credit in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Apr 2024 (2)."
url: https://scholar4ai.com/datasets/home-credit
markdown_url: https://scholar4ai.com/datasets/home-credit.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "From Nov 2023 to Oct 2026, 13 archived papers mentioned Home Credit in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Apr 2024 (2). (as of 2026-11-05)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-05T06:02:37+09:00
window: 2026-10-07 to 2026-11-05
source: OpenAlex (CC0)
---

# Home Credit

[Browse dataset profiles](https://scholar4ai.com/datasets/directory/page/3.md)

Loan-level lending data and the public datasets used to train credit-scoring models.

- data category: Credit scoring and lending data
- 0 papers in the last 7 days
- Names searched for: Home Credit

Most-used data: https://scholar4ai.com/rankings/datasets.md · All Credit scoring and lending data datasets: https://scholar4ai.com/datasets/credit-scoring-and-lending-data.md



## Key findings

- From Nov 2023 to Oct 2026, 13 archived papers mentioned Home Credit in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Apr 2024 (2).
- No paper of the tracked journals mentioned Home Credit in the last 30 days (2026-10-07 to 2026-11-05).
- From 2017 to 2026 (so far), Home Credit was mentioned in 13 papers in the tracked journals, most of them in AI (6), operations research (3) and finance (1).
- 6 of them were published in 2025, against 0 in 2017.
- Since 2023, the institutions with the most of these papers were Universidade Estadual de Campinas (UNICAMP) (2), University of Southampton (2) and Central University of Finance and Economics (1).
- The journals publishing most of them were Annals of Operations Research (3), Neural Computing and Applications (2) and Technological Forecasting and Social Change (1).
- The most-cited of them is “Model-agnostic explainable artificial intelligence methods in finance: a systematic review, recent developments, limitations, challenges and future directions” (Artificial Intelligence Review, 2025), with 72 citations.
- Papers use Home Credit most often with Kaggle (8), German Credit (UCI Statlog) (4) and Taiwan credit card default (UCI) (4); it has 4 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

## Mentions over 36 months

*Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)*

| Trend | Nov 23–Oct 24 | Nov 24–Oct 25 | Nov 25–Oct 26 | 36 months |
|---|---:|---:|---:|---:|
|  | 4 | 7 | 2 | 13 |

## Papers per year

*Papers per publication year and category*

| Category | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 (so far) |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| [AI](https://scholar4ai.com/categories/ai.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 1 |
| [Operations Research](https://scholar4ai.com/categories/operations-research.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |
| [Finance](https://scholar4ai.com/categories/finance.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| [Economics](https://scholar4ai.com/categories/economics.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| [Marketing](https://scholar4ai.com/categories/marketing.md) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
| [Management](https://scholar4ai.com/categories/management.md) | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| All categories | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 4 | 6 | 2 |

## Where the papers come from

*Journals with the most papers, since 2023*

| # | Journal | Papers |
|---:|---|---:|
| 1 | [Annals of Operations Research](https://scholar4ai.com/journals/annals-of-operations-research.md) | 3 |
| 2 | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2 |
| 3 | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | 1 |
| 4 | [Journal of Consumer Policy](https://scholar4ai.com/journals/journal-of-consumer-policy.md) | 1 |
| 5 | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 1 |
| 6 | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 1 |
| 7 | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 1 |
| 8 | [Journal of Corporate Finance](https://scholar4ai.com/journals/journal-of-corporate-finance.md) | 1 |
| 9 | [Journal for Labour Market Research](https://scholar4ai.com/journals/journal-for-labour-market-research.md) | 1 |
| 10 | [IEEE Transactions on Neural Networks and Learning Systems](https://scholar4ai.com/journals/ieee-transactions-on-neural-networks-and-learning-systems.md) | 1 |

*Institutions with the most papers, since 2023*

| # | Institution | Papers |
|---:|---|---:|
| 1 | Universidade Estadual de Campinas (UNICAMP) | 2 |
| 2 | [University of Southampton](https://scholar4ai.com/institutions/university-of-southampton.md) | 2 |
| 3 | [Central University of Finance and Economics](https://scholar4ai.com/institutions/central-university-of-finance-and-economics.md) | 1 |
| 4 | Federal Institute of São Paulo | 1 |
| 5 | Friedrich-Alexander-Universität Erlangen-Nürnberg | 1 |
| 6 | Integral University | 1 |
| 7 | [Nvidia (United States)](https://scholar4ai.com/companies/nvidia-united-states.md) | 1 |
| 8 | [RMIT University](https://scholar4ai.com/institutions/rmit-university.md) | 1 |
| 9 | [Technische Universität Berlin](https://scholar4ai.com/institutions/technische-universitat-berlin.md) | 1 |
| 10 | [UNSW Sydney](https://scholar4ai.com/institutions/unsw-sydney.md) | 1 |

*Countries with the most papers, since 2023*

| # | Country | Papers |
|---:|---|---:|
| 1 | United Kingdom | 4 |
| 2 | Australia | 2 |
| 3 | Brazil | 2 |
| 4 | Netherlands | 2 |
| 5 | United States | 2 |
| 6 | China | 1 |
| 7 | Germany | 1 |
| 8 | India | 1 |

*Keywords with the most papers, since 2023*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | credit scoring | 4 |
| 2 | algorithmic fairness | 2 |
| 3 | [class imbalance](https://scholar4ai.com/keywords/class-imbalance.md) | 2 |
| 4 | credit risk | 2 |
| 5 | [explainable AI](https://scholar4ai.com/keywords/explainable-ai.md) | 2 |
| 6 | [transparency](https://scholar4ai.com/keywords/transparency.md) | 2 |
| 7 | EU consumer law | 1 |
| 8 | German Socio-Economic Panel | 1 |
| 9 | German credit dataset | 1 |
| 10 | Kaggle | 1 |

## Most-cited papers

*Most-cited papers since 2017*

| # | Citations | Per month | Title | Journal | Published | Authors |
|---:|---:|---:|---|---|---|---|
| 1 | 72 | 4.0 | [Model-agnostic explainable artificial intelligence methods in finance: a systematic review, recent developments, limitations, challenges and future directions](https://doi.org/10.1007/s10462-025-11215-9) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | 2025-05-02 | Farhina Sardar Khan, Syed Shahid Mazhar, Kashif Mazhar et al. |
| 2 | 16 | 0.4 | [The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach](https://doi.org/10.1016/j.techfore.2023.122936) | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | 2023-11-16 | Libo Li, Huan Yu, Martin Horacio Kunc |
| 3 | 16 | 0.7 | [How can consumers without credit history benefit from the use of information processing and machine learning tools by financial institutions?](https://doi.org/10.1016/j.ipm.2024.103972) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 2024-11-22 | Bjorn van Braak, Joerg R. Osterrieder, Marcos Machado |
| 4 | 14 | 0.5 | [On the Bumpy Road to Responsible Lending in the Digital Marketplace: The New EU Consumer Credit Directive](https://doi.org/10.1007/s10603-024-09564-z) | [Journal of Consumer Policy](https://scholar4ai.com/journals/journal-of-consumer-policy.md) | 2024-04-17 | Olha O. Cherednychenko |
| 5 | 13 | 0.9 | [Best practices for responsible machine learning in credit scoring](https://doi.org/10.1007/s00521-025-11520-y) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 2025-08-05 | Giovani Valdrighi, Athyrson M. Ribeiro, Jansen S. B. Pereira et al. |

## Used together with

Papers use Home Credit most often with Kaggle (8), German Credit (UCI Statlog) (4) and Taiwan credit card default (UCI) (4); it has 4 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

*What papers use Home Credit together with (at least 3 papers; lift 1 = chance)*

| Used with | Type | Papers using both | Lift |
|---|---|---:|---:|
| [Kaggle](https://scholar4ai.com/datasets/kaggle.md) | Dataset | 8 | 51.1 |
| [German Credit (UCI Statlog)](https://scholar4ai.com/datasets/german-credit-uci-statlog.md) | Dataset | 4 | 129.1 |
| [Taiwan credit card default (UCI)](https://scholar4ai.com/datasets/taiwan-credit-card-default-uci.md) | Dataset | 4 | 490.7 |
| [UCI Machine Learning Repository](https://scholar4ai.com/datasets/uci-machine-learning-repository.md) | Dataset | 4 | 23.4 |

The whole network: https://scholar4ai.com/code/network.md

## Cite this page

Scholar4AI. (2026). Home Credit. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/home-credit

```bibtex
@misc{scholar4ai-datasets-home-credit,
  author = {{Scholar4AI}},
  title = {Home Credit},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/home-credit}},
  urldate = {2026-11-05},
  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/).