Unlocking AI for financial risk managementView Agenda
Key reasons to attend
Delve into regulatory expectations related to artificial intelligence (AI) and machine learning
Examine a day in the life of an AI model validator
Identify opportunities and challenges of adopting AI
Does your team require a tailored learning solution on this or any other topic?
Working with the portfolio of expert tutors and Risk.net’s editorial team, we can develop and deliver a customised learning to make the most impact for your team, from initial assessment to final review.
About the course
This interactive learning experience explores key concepts related to the MRM of AI and machine learning models, while providing attendees with strategies for more efficient implementation.
Attendees will work to develop a stronger understanding of ever-evolving regulations related to model validation and AI risk management.
Analysis of challenges related to adoption of AI will guide attendees in understanding areas of strength and weakness for their companies and provide them with strategies for improvement. Through key sessions and speaker-led discussions, attendees will examine how AI is changing the face of traditional banking.
Assess the status of AI adoption at your organisation
Utilise strategies for efficient management of AI risk
Implement AI/machine learning model risk techniques
Manage the challenges associated with financial crime
Integrate first line of defence MRM into general operations
Examine the expectations of AI/machine learning MRM
Who should attend
Relevant departments may include but are not limited to:
Risk model validation
October 16–18, 2023
Time zones: Emea/Apac
Introduction to artificial intelligence (AI) model risk
Understanding the regulatory landscape
AI/machine learning model risk techniques
AI adoption and financial services risk management
Financial crime and AI/machine learning validation
AI/machine learning model risk: blockchain, quantum and looking ahead
Principal Data Scientist
Chris Preuss is a Senior Quantitative Analyst and Data Scientist specializing in finance and risk management. Currently, he serves as the Principal Data Scientist at Calvin Risk, where he employs his expertise in AI Model Risk Management. Chris holds a MSc in Finance from IE Business School.
Ms. Karen Wong is a Partner in the Consulting practice of PwC Hong Kong. She has over 15 years of experience in professional services, providing a full range of strategy and risk management consulting services for banks and financial institutions in Hong Kong, mainland China and Asia Pacific. Karen possesses extensive experience in strategic planning, risk & capital management, business operations, data analytics & AI modelling and finance transformation.
Prior to joining PwC, Karen worked on a range of business analytics and sales management projects focusing on predictive modelling, customer experience, marketing campaign management and liquidity control. Karen is often invited to deliver speeches on evolving risk & regulatory topics, and was the editorial panel for the regulatory publications in relation to Banking with AI.
Karen holds a Master degree from the Chinese University of Hong Kong in Risk Management Science and a Bachelor degree from the Hong Kong University of Science and Technology in Finance and Information Systems. She is a Chartered Financial Analyst (CFA) and Certified Financial Risk Manager (FRM).
Carl Chan is a director at Accuracy. He specialises in financial risk management (both quantitative modelling and regulatory compliance for credit risk, market risk, counterparty credit risk, liquidity risk, operational risk, and ESG climate risk), portfolio analysis (banking book and trading book), and adoption of system solutions for these areas. He has delivered over 100 engagements for banks, security firms, insurance companies, asset managers, pension funds and government bodies.
Carl has worked with professionals including product specialists, researchers, system developers and practice leaders to deliver projects.
Quantum Risk Advisory
Paul Chammas is a seasoned Technology Risk & Cybersecurity consultant, auditor, and trainer, with over 14 years of experience dedicated to financial services. In 2021, he co-founded Quantum Risk Advisory (QuRISK), a startup specialized in risk management for emerging technologies, particularly focusing on Artificial Intelligence and Quantum Technologies.
With an Engineering degree in Telecommunications from EFREI Paris, a Masters in Technology Management from HEC Paris, and a Masters in Quantum Technologies from UPM Spain, Paul seamlessly blends technical expertise with strategic insight. His unwavering commitment is focused on providing organizations with the appropriate methodological tools to safely adopt cutting-edge technologies.
The Risk.net resources below have been selected to enhance your learning experience:
Model risk management is evolving: regulation, volatility, machine learning and AI - Read article | Risk.net
BloombergGPT: Terminal giant enters the LLM race - Read article| Risk.net
OK regulator? How AI became respectable for AML controls - Read article | Risk.net
A Risk.net subscription will provide you access to these articles. Alternatively, register for free to read two news articles a month.