Writing Quality and Soft Information in the GenAI Age: Evidence from Online Credit Markets

We document systematically how various dimensions of writing quality matter for different borrower groups on a dominant online credit platform, and would be reshaped by the use of large language models (LLMs). Using human assessments, we find that LLMs (e.g., ChatGPT) significantly enhance the writing and perceived quality of loan applications. We further build proprietary BERT-based multimodal models of lenders’ decision-making that accommodate lenders’ constraints, and introduce “deep” Heckman correction for sample selection bias. We demonstrate that ChatGPT adoption decreases soft information conveyed and increases credit misallocation, likely due to convergence in writing; when lenders respond to borrowers’ LLM adoption, they rely more on hard information, mitigating misallocation. We leverage generative modeling to characterize the counterfactual equilibrium with endogenous borrowers’ LLM adoption and lenders’ responses. Our findings provide insights into the evolving role of soft information and potential impacts of GenAI on lending.