Our research investigates the potential of Large-scale Language Models
(LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary
classification task. Our findings suggest that LLMs, when directed by
judiciously designed prompts and supplemented with domain-specific knowledge,
can parallel the performance of traditional Machine Learning (ML) models.
Intriguingly, they achieve this with significantly less data-40 times less,
utilizing merely 20 data points compared to the ML's 800. LLMs particularly
excel in minimizing false positives and enhancing fairness, both being vital
aspects of risk analysis. While our results did not surpass those of classical
ML models, they underscore the potential of LLMs in analogous tasks, laying a
groundwork for future explorations into harnessing the capabilities of LLMs in
diverse ML tasks.

大规模语言模型在信用风险评估中的潜力研究表明，当遵循精心设计的提示并辅以领域特定知识时，这些语言模型可以与传统机器学习模型的性能相媲美。尤其值得注意的是，它们只使用了极少的数据 - 仅 20 个数据点，相比于机器学习模型的 800 个数据点少了 40 倍。大规模语言模型在降低误报率和增强公平性方面表现出色，这两个方面对于风险分析至关重要。虽然我们的结果没有超过传统机器学习模型，但它们突显了大规模语言模型在类似任务中的潜力，并为未来在多样化的机器学习任务中利用大规模语言模型的能力奠定了基础。