Trustworthy Generative AI for Financial Services (Practitioner Track)

This work introduces GFT EnterpriseGPT, a regulatory-compliant, trustworthy generative AI (GenAI) platform tailored for the financial services sector. We discuss the unique challenges of applying GenAI in highly regulated environments. In the financial sector data privacy, ethical considerations, and regulatory compliance are paramount. Our solution addresses these challenges through multi-level safeguards, including robust guardrails, privacy-preserving techniques, and grounding mechanisms. Robust guardrails prevent unsafe inputs and outputs, and privacy-preserving techniques reduce the need for data transmission to third-party providers. In contrast, grounding mechanisms ensure the accuracy and reliability of artificial intelligence (AI) generated content. By incorporating these measures, we propose a path forward for safely harnessing the transformative potential of GenAI in finance, ensuring reliability, transparency, and adherence to ethical and regulatory standards. We demonstrate the practical application of GFT EnterpriseGPT within a large-scale financial institution, where it successfully improves operational efficiency and compliance.

Institutions

Publication Details

Journal
DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)
Published
2025-01-01
DOI
https://doi.org/10.4230/oasics.saia.2024.2
Primary Topic
FinTech, Crowdfunding, Digital Finance
Type
article
Field-Weighted Citation Impact
0.00
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article

Trustworthy Generative AI for Financial Services (Practitioner Track)

DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)
FinTech, Crowdfunding, Digital Finance
article

Trustworthy Generative AI for Financial Services (Practitioner Track)

article en

Abstract

This work introduces GFT EnterpriseGPT, a regulatory-compliant, trustworthy generative AI (GenAI) platform tailored for the financial services sector. We discuss the unique challenges of applying GenAI in highly regulated environments. In the financial sector data privacy, ethical considerations, and regulatory compliance are paramount. Our solution addresses these challenges through multi-level safeguards, including robust guardrails, privacy-preserving techniques, and grounding mechanisms. Robust guardrails prevent unsafe inputs and outputs, and privacy-preserving techniques reduce the need for data transmission to third-party providers. In contrast, grounding mechanisms ensure the accuracy and reliability of artificial intelligence (AI) generated content. By incorporating these measures, we propose a path forward for safely harnessing the transformative potential of GenAI in finance, ensuring reliability, transparency, and adherence to ethical and regulatory standards. We demonstrate the practical application of GFT EnterpriseGPT within a large-scale financial institution, where it successfully improves operational efficiency and compliance.

DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)
Stuttgart Technical University of Applied Sciences (DE), Deutsche Gesellschaft für Internationale Zusammenarbeit (DE)
Openalex Percentile: Top 36%
FinTech, Crowdfunding, Digital Finance
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Trustworthy Generative AI for Financial Services (Practitioner Track) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) (2025) | TGRS Research Map | TGRS