Opportunities and Challenges of Generative-AI in Finance

2024-10-21

BigData Congress [Services Society]

AI Summary

This study systematically investigates the opportunities, core challenges, and implementation pathways for generative AI (Gen-AI) in finance. Addressing critical use cases—including intelligent investment research, risk management, regulatory compliance, customer service, and automated reporting—it constructs, for the first time, a comprehensive Gen-AI application taxonomy spanning the full financial value chain, identifying six high-potential application directions and five categories of cross-cutting risks. Methodologically, the work innovatively integrates domain-adaptive fine-tuning, financial knowledge augmentation, multimodal understanding, trustworthy AI evaluation, and cross-domain coordination mechanisms to propose a finance-specific model optimization and assessment framework. By bridging the technology–business divide, the study clarifies key deployment barriers and prioritizes actionable research directions, delivering an implementable roadmap and methodological foundation for academia, industry, and regulators. (149 words)

Technology Category

• Philosophy and Ethics of AI: Applications
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Reasoning under Uncertainty: Applications

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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 domainsIn 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 ecosystemTo 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.

Problem

• Applications of Gen-AI in finance
• Opportunities and challenges of Gen-AI
• Training methodologies for Gen-AI

Innovation

• Enhances language modeling nuances
• Handles large financial data volumes
• Offers fast low-latency responses