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DOI: 10.4230/OASIcs.SAIA.2024.2

Trustworthy Generative AI for Financial Services (Practitioner Track)

Authors: Marc-André Zöller, Anastasiia Iurshina, and Ines Röder

Published in: OASIcs, Volume 126, Symposium on Scaling AI Assessments (SAIA 2024)

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.

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Marc-André Zöller, Anastasiia Iurshina, and Ines Röder. Trustworthy Generative AI for Financial Services (Practitioner Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 2:1-2:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{zoller_et_al:OASIcs.SAIA.2024.2,
  author =	{Z\"{o}ller, Marc-Andr\'{e} and Iurshina, Anastasiia and R\"{o}der, Ines},
  title =	{{Trustworthy Generative AI for Financial Services}},
  booktitle =	{Symposium on Scaling AI Assessments (SAIA 2024)},
  pages =	{2:1--2:5},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-357-7},
  ISSN =	{2190-6807},
  year =	{2025},
  volume =	{126},
  editor =	{G\"{o}rge, Rebekka and Haedecke, Elena and Poretschkin, Maximilian and Schmitz, Anna},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SAIA.2024.2},
  URN =		{urn:nbn:de:0030-drops-227428},
  doi =		{10.4230/OASIcs.SAIA.2024.2},
  annote =	{Keywords: Generative AI, GenAI, Trustworthy AI, Finance, Guardrails, Grounding}
}

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Documents authored by Iurshina, Anastasiia

Trustworthy Generative AI for Financial Services (Practitioner Track)

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