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:
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CALM · PortoSeguro (claim analysis)
📄 Paper · 💻 Code · 🌐 The Fin AI
Part of CALM — Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models (arXiv:2310.00566).
| Task | insurance claim analysis |
| Original dataset | PortoSeguro (anonymized) |
| Evaluation metric | Accuracy, MCC, F1, Miss |
| Source license | Public |
| Language | en |
Quick Start
from datasets import load_dataset
ds = load_dataset("TheFinAI/en-portoseguro", split="test")
print(ds[0])
Example prompt
Identify whether or not to files a claim for the auto insurance policy holder using the following table attributes about individual financial profile. Respond with only 'yes' or 'no', and do not provide any additional information. And the table attributes that belong to similar groupings are tagged as such in the feature names (e.g., ind, reg, car, calc). In addition, feature names include the pos…
Dataset Structure
| Split | Rows |
|---|---|
train |
8,332 |
validation |
1,190 |
test |
2,382 |
| 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 (PortoSeguro (anonymized)):
@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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