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 · Lending Club (credit scoring)
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CALM · Lending Club (credit scoring)
📄 Paper · 💻 Code · 🌐 The Fin AI
Part of CALM — Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models (arXiv:2310.00566).
| Task | credit scoring |
| Original dataset | Lending Club |
| Evaluation metric | Accuracy, F1, Miss |
| Source license | CC0 1.0 |
| Language | en |
Quick Start
from datasets import load_dataset
ds = load_dataset("TheFinAI/en-lendingclub", split="test")
print(ds[0])
Example prompt
Assess the client's loan status based on the following loan records from Lending Club. Respond with only 'good' or 'bad', and do not provide any additional information. For instance, 'The client has a stable income, no previous debts, and owns a property.' should be classified as 'good'.
Text: 'The client has attributes as follows: The state of Installment is 267.07. The state of Loan Purpose is …
Dataset Structure
| Split | Rows |
|---|---|
train |
9,417 |
validation |
1,345 |
test |
2,691 |
| 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 CC0 1.0 (CALM paper, Table 1).
Citation
Please cite CALM and the original dataset (Lending Club):
@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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