Jochen Papenbrock

Bio

Financial Technology Customer & Partner Relationship Manager @EMEA

Dr. Jochen Papenbrock’s main focus is accelerated, explainable, trustworthy AI in Financial Services, both as researcher and manager. He is a financial data scientist and received his degree and PhD in Financial Business Engineering from the Karlsruhe Institute of Technology (KIT). He supports his thought leadership role in the industry through an international academic network and publishing activities, such as a recent series on the next generation of AI in asset management, which received worldwide attention. In this work he combines innovative approaches such as explainable/responsible AI, graph theory, synthetic data generation, evolutionary optimization and data visualization.

Jochen has a 15 year consulting and entrepreneurial background in the financial services industry, where he works at the intersection of AI, quantitative modeling, business, IT, risk management and regulation. He shapes the ecosystem around 'AI in Financial Services' including public lectures as well as panel/moderator/keynote activities. He is also a board member of the EU Horizon 2020 project 'FIN-TECH', specialty chief editor (Co) at Frontiers 'AI in Finance', member of the advisory board of Springer's 'Digital Finance' and vice president of the association 'AI in Financial Services'. He recently got involved in AI projects on TrustTech and Algo Auditing at GAIA-X, the European Cloud Project.

Talks

• Computing Platforms for Trustworthy, Responsible AI in Financial Services

  Keynote
  10-09-2021 - 08:30-09:30
  Abstract
  

  Trust has always been a key element in financial services. This is not changing in the age of AI. So how can we implement trustworthy, responsible AI in financial institutions?
  Computing platforms, intelligent SDKs and open source suites for accelerated Data Science play an important role. They support enterprise-scale implementation of trustworthy AI and highly automated AI model assessments.
  A key function can be attributed to explainable AI and we demonstrate this in a use case in credit risk management implemented in the GAIA-X project FAIC, based on the legacy of the EU Horizon2020 project FIN-TECH.
  Also, the impact of “AI/ML” in capital markets is growing. However, pure outperformance requirements have expanded to include more robust, explainable, trustworthy, sustainable, efficient, transparent, and simply larger AI/ML models that open a new era of model building.
  We will give an overview and present some use cases, one of which is based on a project with Munich Re Markets on robust accelerated portfolio construction and market data generators, recently published in the Journal of Financial Data Science.