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FinGuard: A Multimodal AIGC Guardrail in Financial Scenarios

Authors:

Xu Ding,

Jin LiuAuthors Info & Claims

MMAsia '23: Proceedings of the 5th ACM International Conference on Multimedia in Asia

Article No.: 104, Pages 1 - 3

https://doi.org/10.1145/3595916.3626351

Published: 01 January 2024 Publication History

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Abstract

Recently, the development of foundation models has led to significant advances in the ability of artificial intelligence (AI) to generate multimodal content such as text and images. However, specialized industrial scenarios such as finance, which require high levels of security and compliance, pose challenges for the application of generative AI due to its uncontrollability. To address this issue, we propose FinGuard, a multimodal AI-generated content (AIGC) guardrail specifically designed for financial scenarios. We provide detailed definitions of the general quality, financial compliance, and security dimensions of AIGC, and implement the evaluation and inspection of multimodal AIGC including text and images. Our proposed FinGuard has been applied to a financial marketing application serving hundreds of millions of users.

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Cited By

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Li QDu WLiu J(2024)Detoxifying Large Language Models via Kahneman-Tversky OptimizationNatural Language Processing and Chinese Computing10.1007/978-981-97-9443-0_36(409-417)Online publication date: 1-Nov-2024
https://doi.org/10.1007/978-981-97-9443-0_36
Yang YLi YXiong H(2024)Human- and AI-Generated Marketing Content Comparison Corpus, Evaluation, and DetectionPRICAI 2024: Trends in Artificial Intelligence10.1007/978-981-96-0119-6_18(177-183)Online publication date: 12-Nov-2024
https://doi.org/10.1007/978-981-96-0119-6_18

Index Terms

FinGuard: A Multimodal AIGC Guardrail in Financial Scenarios
1. Computing methodologies
  1. Artificial intelligence
2. Security and privacy

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MMAsia '23: Proceedings of the 5th ACM International Conference on Multimedia in Asia

December 2023

745 pages

ISBN:9798400702051

DOI:10.1145/3595916

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 01 January 2024

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MMAsia '23: ACM Multimedia Asia

December 6 - 8, 2023

Tainan, Taiwan

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Li QDu WLiu J(2024)Detoxifying Large Language Models via Kahneman-Tversky OptimizationNatural Language Processing and Chinese Computing10.1007/978-981-97-9443-0_36(409-417)Online publication date: 1-Nov-2024
https://doi.org/10.1007/978-981-97-9443-0_36
Yang YLi YXiong H(2024)Human- and AI-Generated Marketing Content Comparison Corpus, Evaluation, and DetectionPRICAI 2024: Trends in Artificial Intelligence10.1007/978-981-96-0119-6_18(177-183)Online publication date: 12-Nov-2024
https://doi.org/10.1007/978-981-96-0119-6_18

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Recommendations

Multimodal Multi-Task Financial Risk Forecasting

How Do Financial Firms Manage Risk? Unraveling the Interaction of Financial and Operational Hedging

On the Integration of Production and Financial Hedging Decisions in Global Markets

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