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 · Taiwan Economic Journal (financial distress)
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CALM · Taiwan Economic Journal (financial distress)
📄 Paper · 💻 Code · 🌐 The Fin AI
Part of CALM — Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models (arXiv:2310.00566).
| Task | financial distress identification |
| Original dataset | Taiwan Economic Journal |
| Evaluation metric | Accuracy, MCC, F1, Miss |
| Source license | CC BY 4.0 |
| Language | en |
Quick Start
from datasets import load_dataset
ds = load_dataset("TheFinAI/en-taiwan", split="test")
print(ds[0])
Example prompt
Predict whether the company will face bankruptcy based on the financial profile attributes provided in the following text. Respond with only 'no' or 'yes', and do not provide any additional information.
For instance, 'The client has attributes: ROA(C) before interest and depreciation before interest: 0.499, ..., Net Income Flag: 1.000, Equity to Liability: 0.044.' should be classified as 'no'. …
Dataset Structure
| Split | Rows |
|---|---|
train |
4,773 |
validation |
681 |
test |
1,365 |
| 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 CC BY 4.0 (CALM paper, Table 1).
Citation
Please cite CALM and the original dataset (Taiwan Economic Journal):
@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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