Opportunities and Challenges of Generative-AI in Finance

TL;DR

The paper surveys opportunities and challenges of Generative AI in finance, arguing that large language models can surpass traditional ML in language-centric finance tasks while presenting unique data, cost, and regulatory hurdles. It classifies opportunities into interactive, assistive, educative, and advisory categories and details training and deployment methodologies, including out-of-the-box use, instruction/ task-specific fine-tuning, PEFT, agentic systems, and quantization. Concrete applications in numerical reasoning, trading, summarization, and risk monitoring illustrate Gen-AI's potential to improve accuracy, efficiency, and decision support, while data privacy, bias, and computation costs remain critical constraints. The work aims to provide a comprehensive roadmap for researchers and practitioners, bridging finance with other domains and guiding future prioritization and cross-domain knowledge transfer.

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.