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Edge CV Technology Primer

Christophe Quéva

It’s an opportunity to learn more about what is exciting in the field of cell therapies and CAR-T Cells and really to meet some of the early companies that would be interested in collaborating with us.

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Bryan Kobel

It’s really fascinating what’s happening in cell therapy, so having a concentrated amount of people here talking about NK Cells, the manufacturing, everything from the soup to the nuts of actual cell therapy has been incredible.

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Glenn Tillotson

Consultant

Ferring Pharmaceuticals

Companion Animal Showcase

Production Animal Showcase

Fairness, Explainability and Bias in Machine Learning Applications

Exploring Machine Learning Applications in Credit Risk
Understanding ‘The Fairness Issue’
Explaining ‘The Explainability Issue’
Investigating Machine Learning and Model Risk Frameworks

AI/ ML

Speaker

Author:

Peter Quell

Head of the Portfolio Analytics Team for Market and Credit Risk

DZ Bank

Dr. Peter Quell is Head of the Portfolio Analytics Team for Market and Credit Risk in the Risk Controlling Unit of DZ BANK AG in Frankfurt. He is responsible for methodological aspects of Internal Risk Models and Economic Capital. Prior to joining DZ BANK AG Peter was Manager at d-fine GmbH where he dealt with various aspects of Risk Management Systems in the Banking Industry. He holds a MSc. in Mathematical Finance from Oxford University and a PhD in Mathematics. Peter is member of the editorial board of the Journal of Risk Model Validation and a founding board member of the Model Risk Management International Association (mrmia.org).

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Effectively handling big data in new models

Overcoming the increase usage of AI and big data
Implementing sufficient frameworks and governance to protect the use of big data
The risks associated with big data in AI versus traditional, less data heavy models

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Preparing MRM for AI

Managing the disruptive nature of AI
Preparing for the scale increase from traditional to AI models
Overcoming challenges in interconnected models
Reducing bias and ensuring ethics in AI modelling

AI/ ML

Moderator

Author:

Julian Philips

Global Head of Model Risk Audit

HSBC

Speakers

Author:

Ushnish Banerjee

EMEA QAG Vice President

Morgan Stanley

Ushnish is an experienced model risk practitioner with more than 10 years of experience across Banks (Morgan Stanley and HSBC) as well as consulting firms (Ernst and Young and KPMG). Ushnish has accrued skills and experience across credit risk (IRB/IFRS9/CECL), traded credit risk (IMM/CVA/IRC) and stress testing models across all three lines of defence. Ushnish has prior experience in conducting learning courses for risk.net.

Author:

Rita Gnutti

Executive Director, Internal Validation and Controls, Group CRO Area

Intesa Sanpaolo

Author:

Sanja Hukovic

Head of Model Risk Management

London Stock Exchange Group

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AI/ML and the current regulatory framework: BoE DP5/22 and beyond

How do key existing sectoral legal requirements and guidance in UK financial services apply to AI?
Which ones are most relevant? Are they sufficient? What are the gaps?
What are the likely challenges in operationalising them, at scale?"

AI/ ML

Moderator

Author:

Shameek Kundu

Head Of Financial Services and Chief Strategy Officer

Truera

Shameek Kundu is Chief Strategy Officer at TruEra. and one of the representatives from Singapore at the Global Partnership on AI, where he is co leading a project to demonstrate the practical use of Privacy Enhancing and adjacent technologies for well-governed data access for "AI for good" projects.

Shameek has spent most of his career in driving responsible adoption of data analytics/ AI in the financial services industry. He is a member of the Singapore Government's Advisory Council on AI and Data, the Bank of England’s AI Public-Private Forum and the Monetary Authority of Singapore’s Steering Committee on Fairness, Ethics, Accountability and Transparency in AI..

Until 2020, Shameek was Group Chief Data Officer at Standard Chartered Bank, where he helped the bank explore and adopt AI in multiple areas, shaped the bank’s internal approach to responsible AI, and had direct experience of working on data privacy, data sovereignty and data sharing issues in a commercial context