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Unleashing the potential of AI in the financial sector ?(future prospects, AI and data Act impacts, data standards…)?

Day 1 Afternoon

Wednesday 13 September

Room :

ROOM 1

Speakers

Chair
Joachim Wuermeling
Member of the Executive Board - Deutsche Bundesbank
Public Authorities
Olivier Fliche
Director of FinTech Innovation Hub - Autorité de Contrôle Prudentiel et de Résolution (ACPR)
Petra Hielkema
Chairperson - European Insurance and Occupational Pensions Authority (EIOPA)
Industry Representatives
Manel Carpio
Partner, Risk Advisory - Deloitte
Magnus Haglind
Senior Vice President, Head of Products, Marketplace Technology - Nasdaq Stockholm AB
Sophia Wikander
General Manager, Financial Services Industry, Western Europe - Microsoft

Objectives

This session will first discuss how the uptake of AI is progressing in the financial sector, how use cases are expected to evolve in the medium term with the sophistication of AI technologies and increasing analytical capacity and also the related benefits and challenges for customers and financial institutions of a wider use of AI.

The panel will also assess whether the policy implications of a wider adoption of AI in finance are appropriately taken into account in the EU AI and data frameworks that are in the process of being implemented and whether any further measures are needed to support an appropriate development of AI in the financial sector.

Points of discussion

  1. How is the use of AI progressing in the financial sector and what are the main drivers? What are the main current use cases and how are these expected to evolve in the coming years? Will future generations of AI such as Generative AI trigger further adoption of AI in finance? What are the main opportunities and challenges associated with AI use in the financial sector?
  2. Do the AI Act and the European strategy for data provide an appropriate framework for supporting the uptake of AI in finance? Are they adapted to the latest developments in AI and to expected future evolutions? Are further efforts needed in terms of data quality and standardization in particular to facilitate AI uptake?