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
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CALM · German credit benchmark (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 | German (Hofmann, 1994) |
| Evaluation metric | Accuracy, F1, Miss |
| Source license | CC BY 4.0 |
| Language | en |
Quick Start
from datasets import load_dataset
ds = load_dataset("TheFinAI/en-german-credit-benchmark", split="test")
print(ds[0])
Example prompt
Predict whether the loan applicant is a credit risk using the features below. Directly respond with 'bad' if the applicant is a bad credit risk or 'good' if they are a good credit risk.
Text: ' ageInYears: 67.00, creditAmount: 1169.00, creditHistory: criticalAccountOrOtherCreditsExisting, durationInMonth: 6.00, foreignWorker: yes, housing: own, installmentRateInPercentageOfDisposableIncome: 4.00, …
Dataset Structure
| Split | Rows |
|---|---|
train |
700 |
valid |
100 |
test |
200 |
| 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 |
License
The original data is released under CC BY 4.0 (CALM paper, Table 1).
Citation
Please cite CALM and the original dataset (German (Hofmann, 1994)):
@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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