Peer-to-peer (P2P) lending has emerged as a distinctive financing mechanism, linking borrowers with lenders through online platforms. However, P2P lending faces the challenge of information asymmetry, as lenders often lack sufficient data to assess the creditworthiness of borrowers. This paper proposes a novel approach to address this issue by leveraging the textual descriptions provided by borrowers during the loan application process. Our methodology involves processing these textual descriptions using a Large Language Model (LLM), a powerful tool capable of discerning patterns and semantics within the text. Transfer learning is applied to adapt the LLM to the specific task at hand. Our results derived from the analysis of the Lending Club dataset show that the risk score generated by BERT, a widely used LLM, significantly improves the performance of credit risk classifiers. However, the inherent opacity of LLM-based systems, coupled with uncertainties about potential biases, underscores critical considerations for regulatory frameworks and engenders trust-related concerns among end-users, opening new avenues for future research in the dynamic landscape of P2P lending and artificial intelligence.

翻译：点对点（P2P）借贷作为一种独特的融资机制，通过在线平台连接借款人与贷款人。然而，P2P借贷面临着信息不对称的挑战，因为贷款人通常缺乏足够数据来评估借款人的信用状况。本文提出一种新颖方法，通过利用借款人在贷款申请过程中提供的文本描述来解决这一问题。我们的方法涉及使用大型语言模型（LLM）处理这些文本描述，LLM是一种能够识别文本中模式与语义的强大工具。我们应用迁移学习使LLM适应于当前特定任务。基于Lending Club数据集的分析结果表明，由广泛使用的LLM——BERT生成的风险评分，显著提升了信用风险分类器的性能。然而，基于LLM的系统固有的不透明性，加之对其潜在偏倚的不确定性，凸显了监管框架需考量的关键问题，并引发了终端用户对信任的担忧，这为P2P借贷与人工智能这一动态领域中的未来研究开辟了新的途径。