Can LLMs be Good Financial Advisors?: An Initial Study in Personal
  Decision Making for Optimized Outcomes
Kausik Lakkaraju Sai Krishna Revanth Vuruma Vishal Pallagani Bharath Muppasani Biplav Srivastava
Increasingly powerful Large Language Model (LLM) based chatbots, like ChatGPT
and Bard, are becoming available to users that have the potential to
revolutionize the quality of decision-making achieved by the public. In this
context, we set out to investigate how such systems perform in the personal
finance domain, where financial inclusion has been an overarching stated aim of
banks for decades. We asked 13 questions representing banking products in
personal finance: bank account, credit card, and certificate of deposits and
their inter-product interactions, and decisions related to high-value
purchases, payment of bank dues, and investment advice, and in different
dialects and languages (English, African American Vernacular English, and
Telugu). We find that although the outputs of the chatbots are fluent and
plausible, there are still critical gaps in providing accurate and reliable
financial information using LLM-based chatbots.