Opportunities and Challenges of Generative-AI in Finance

TL;DR

This paper provides a comprehensive overview of Generative-AI applications in finance, highlighting opportunities, challenges, methodologies, and future research directions to advance financial technology and integration.

Contribution

It offers the most extensive summary of Gen-AI techniques in finance, detailing methodologies, applications, and identifying key areas for future development and research.

Findings

Gen-AI enhances language understanding and data handling in finance.

Opportunities include improved decision-making and automation.

Challenges involve data privacy, model bias, and regulatory issues.

Abstract

Gen-AI techniques are able to improve understanding of context and nuances in language modeling, translation between languages, handle large volumes of data, provide fast, low-latency responses and can be fine-tuned for various tasks and domains. In this manuscript, we present a comprehensive overview of the applications of Gen-AI techniques in the finance domain. In particular, we present the opportunities and challenges associated with the usage of Gen-AI techniques. We also illustrate the various methodologies which can be used to train Gen-AI techniques and present the various application areas of Gen-AI technologies in the finance ecosystem. To the best of our knowledge, this work represents the most comprehensive summarization of Gen-AI techniques within the financial domain. The analysis is designed for a deep overview of areas marked for substantial advancement while simultaneously pin-point those warranting future prioritization. We also hope that this work would serve as a conduit between finance and other domains, thus fostering the cross-pollination of innovative concepts and practices.