| --- |
| pretty_name: CALM · ccFraud (fraud detection) |
| license: other |
| license_name: public-source |
| language: |
| - en |
| task_categories: |
| - text-classification |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - extended |
| tags: |
| - finance |
| - benchmark |
| - thefinai |
| - calm |
| - flare |
| extra_gated_heading: Request access to CALM · ccFraud (fraud detection) |
| extra_gated_description: This CALM task is released by The Fin AI for research. Access is granted automatically after you complete this short form. |
| extra_gated_button_content: Agree and access |
| extra_gated_prompt: By accessing this dataset you agree to the license of the original source (Public) and to cite the CALM paper and the original dataset in any resulting publication. |
| extra_gated_fields: |
| Full name: text |
| Affiliation: text |
| Country: country |
| Intended use: |
| type: select |
| options: |
| - Research |
| - Education |
| - Commercial evaluation |
| - Other |
| I agree to the terms above and will cite the papers: checkbox |
| --- |
| |
| # CALM · ccFraud (fraud detection) |
|
|
| 📄 [Paper](https://arxiv.org/abs/2310.00566) · 💻 [Code](https://github.com/colfeng/CALM) · 🌐 [The Fin AI](https://thefin.ai) |
|
|
| > Part of **CALM** — *Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models* ([arXiv:2310.00566](https://arxiv.org/abs/2310.00566)). |
|
|
| | | | |
| |---|---| |
| | Task | fraud detection | |
| | Original dataset | ccFraud | |
| | Evaluation metric | Accuracy, MCC, F1, Miss | |
| | Source license | Public | |
| | Language | en | |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TheFinAI/en-ccfraud", split="test") |
| print(ds[0]) |
| ``` |
|
|
| ## Example prompt |
|
|
| ```text |
| Detect the credit card fraud with the following financial profile. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client is a female, the state number is 25, the number of cards is 1, the credit balance is 7000, the number of transactions is 16, the number of international transactions is 0, the credit limit is 6.' should be classified as 'good… |
| ``` |
|
|
| ## Dataset Structure |
|
|
| | Split | Rows | |
| |---|---| |
| | `train` | 7,339 | |
| | `validation` | 1,048 | |
| | `test` | 2,098 | |
|
|
| | Field | Description | |
| |---|---| |
| | `id` | Example id | |
| | `query` | Full instruction prompt given to the model | |
| | `answer` | Gold answer / label text | |
| | `choices` | Label space | |
| | `gold` | Index of the gold label in `choices` | |
| | `text` | Raw input text (without instruction) | |
|
|
| ## License |
|
|
| The paper lists the original data as **publicly available** without a specific license (CALM paper, Table 1); refer to the original source for its terms. |
|
|
| ## Citation |
|
|
| Please cite CALM and the original dataset (ccFraud): |
|
|
| ```bibtex |
| @misc{feng2024empoweringmanybiasingfew, |
| title={Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models}, |
| author={Duanyu Feng and Yongfu Dai and Jimin Huang and Yifang Zhang and Qianqian Xie and Weiguang Han and Zhengyu Chen and Alejandro Lopez-Lira and Hao Wang}, |
| year={2024}, |
| eprint={2310.00566}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/2310.00566}, |
| } |
| ``` |
|
|
