Emerging Frontiers: Exploring the Impact of Generative AI Platforms on University Quantitative Finance Examinations

TL;DR

This study assesses the performance of three AI language models on a university finance exam, revealing current limitations and potential future improvements for AI-assisted learning in quantitative finance education.

Contribution

It provides a comparative analysis of ChatGPT, BARD, and Bing AI in answering finance exam questions, highlighting their current capabilities and challenges.

Findings

ChatGPT scored 30%, outperforming Bing AI and Bard.

Models face challenges with calculations and formulas.

Future AI improvements could enable near-perfect exam performance.

Abstract

This study evaluated three Artificial Intelligence (AI) large language model (LLM) enabled platforms - ChatGPT, BARD, and Bing AI - to answer an undergraduate finance exam with 20 quantitative questions across various difficulty levels. ChatGPT scored 30 percent, outperforming Bing AI, which scored 20 percent, while Bard lagged behind with a score of 15 percent. These models faced common challenges, such as inaccurate computations and formula selection. While they are currently insufficient for helping students pass the finance exam, they serve as valuable tools for dedicated learners. Future advancements are expected to overcome these limitations, allowing for improved formula selection and accurate computations and potentially enabling students to score 90 percent or higher.