  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.
