CALM — credit scoring and risk assessment
Collection
Credit scoring, fraud detection, financial distress and claim analysis tasks from CALM (arXiv:2310.00566). • 10 items • Updated
Datasets:
Request access to CALM · ccFraud (fraud detection)
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CALM · ccFraud (fraud detection)
📄 Paper · 💻 Code · 🌐 The Fin AI
Part of CALM — Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models (arXiv:2310.00566).
| Task | fraud detection |
| Original dataset | ccFraud |
| Evaluation metric | Accuracy, MCC, F1, Miss |
| Source license | Public |
| Language | en |
Quick Start
from datasets import load_dataset
ds = load_dataset("TheFinAI/en-ccfraud", split="test")
print(ds[0])
Example prompt
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):
@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},
}
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