| --- |
| pretty_name: CALM · Credit Card Fraud (fraud detection) |
| license: other |
| license_name: dbcl-1.0 |
| 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 · Credit Card Fraud (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 ((DbCL) v1.0) 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 · Credit Card Fraud (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 | Credit Card Fraud (anonymized, PCA features) | |
| | Evaluation metric | Accuracy, MCC, F1, Miss | |
| | Source license | (DbCL) v1.0 | |
| | Language | en | |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("TheFinAI/en-ccf", split="test") |
| print(ds[0]) |
| ``` |
|
|
| ## Example prompt |
|
|
| ```text |
| Detect the credit card fraud using the following financial table attributes. Respond with only 'yes' or 'no', and do not provide any additional information. Therein, the data contains 28 numerical input variables V1, V2, ..., and V28 which are the result of a PCA transformation and 1 input variable Amount which has not been transformed with PCA. The feature 'Amount' is the transaction Amount, this… |
| ``` |
|
|
| ## Dataset Structure |
|
|
| | Split | Rows | |
| |---|---| |
| | `train` | 7,974 | |
| | `validation` | 1,139 | |
| | `test` | 2,279 | |
|
|
| | 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 original data is released under **(DbCL) v1.0** (CALM paper, Table 1). |
|
|
| ## Citation |
|
|
| Please cite CALM and the original dataset (Credit Card Fraud (anonymized, PCA features)): |
|
|
| ```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}, |
| } |
| ``` |
|
|
