The Adoption and Efficacy of Large Language Models: Evidence From Consumer Complaints in the Financial Industry

Large Language Models (LLMs) are reshaping consumer decision-making, particularly in communication with firms, yet our understanding of their impact remains limited. This research explores the effect of LLMs on consumer complaints submitted to the Consumer Financial Protection Bureau from 2015 to 2024, documenting the adoption of LLMs for drafting complaints and evaluating the likelihood of obtaining relief from financial firms. Utilizing a leading AI detection tool, we analyzed over 1 million complaints and identified a significant increase in LLM usage following the release of ChatGPT. We establish a causal relationship between LLM usage and an increased likelihood of obtaining relief by employing instrumental variables to address endogeneity in LLM adoption. Experimental data further support this link, demonstrating that LLMs enhance the clarity and persuasiveness of consumer narratives. Our findings suggest that facilitating access to LLMs can help firms better understand consumer concerns and level the playing field among consumers. This underscores the importance of policies promoting technological accessibility, enabling all consumers to effectively voice their concerns.