research-article

Towards self-regulating AI: challenges and opportunities of AI model governance in financial services

Authors:

Jiahao ChenAuthors Info & Claims

ICAIF '20: Proceedings of the First ACM International Conference on AI in Finance

Article No.: 49, Pages 1 - 8

https://doi.org/10.1145/3383455.3422564

Published: 07 October 2021 Publication History

Abstract

AI systems have found a wide range of application areas in financial services. Their involvement in broader and increasingly critical decisions has escalated the need for compliance and effective model governance. Current governance practices have evolved from more traditional financial applications and modeling frameworks. They often struggle with the fundamental differences in AI characteristics such as uncertainty in the assumptions, and the lack of explicit programming. AI model governance frequently involves complex review flows and relies heavily on manual steps. As a result, it faces serious challenges in effectiveness, cost, complexity, and speed. Furthermore, the unprecedented rate of growth in the AI model complexity raises questions on the sustainability of the current practices. This paper focuses on the challenges of AI model governance in the financial services industry. As a part of the outlook, we present a system-level framework towards increased self-regulation for robustness and compliance. This approach aims to enable potential solution opportunities through increased automation and the integration of monitoring, management, and mitigation capabilities. The proposed framework also provides model governance and risk management improved capabilities to manage model risk during deployment.

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cover image ACM Conferences

ICAIF '20: Proceedings of the First ACM International Conference on AI in Finance

October 2020

422 pages

ISBN:9781450375849

DOI:10.1145/3383455

Conference Chair:
Tucker Balch
J.P. Morgan AI Research

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 October 2021

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ICAIF '20: ACM International Conference on AI in Finance

October 15 - 16, 2020

New York, New York

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https://doi.org/10.4018/979-8-3693-5593-0.ch017
Minz N(2024)Ethical Considerations in AI Applications in FinanceRisks and Challenges of AI-Driven Finance10.4018/979-8-3693-2185-0.ch012(277-290)Online publication date: 19-Apr-2024
https://doi.org/10.4018/979-8-3693-2185-0.ch012
Sun TSomalwar AChan H(2024)Multimodal Retrieval Augmented Generation Evaluation Benchmark2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)10.1109/VTC2024-Spring62846.2024.10683437(1-5)Online publication date: 24-Jun-2024
https://doi.org/10.1109/VTC2024-Spring62846.2024.10683437
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Marwala TMarwala T(2024)Policies and Standards Versus Laws and RegulationsThe Balancing Problem in the Governance of Artificial Intelligence10.1007/978-981-97-9251-1_12(189-206)Online publication date: 13-Nov-2024
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