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 · Credit Card Fraud (fraud detection)
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CALM · Credit Card Fraud (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 | Credit Card Fraud (anonymized, PCA features) |
| Evaluation metric | Accuracy, MCC, F1, Miss |
| Source license | (DbCL) v1.0 |
| Language | en |
Quick Start
from datasets import load_dataset
ds = load_dataset("TheFinAI/en-ccf", split="test")
print(ds[0])
Example prompt
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)):
@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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