Model risk management and quantification

  • 1 day
  • Quant and model risk
View Agenda

Key reasons to attend  

  • Access impactful ways to maximise data and analytical abilities
  • Implement strategies to optimise and transform models 
  • Evaluate the key elements and design principles of model validation

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Customised Solutions

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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. 

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About the course

This in-person event offers the opportunity for participants to enhance their understanding of MRM by exploring the key characteristics and emerging technologies in the industry.  

Participants will learn how to develop a healthy MRM framework by studying model risk appetite and optimal organisational structures. Sessions will explore key requirements for effective MRM, such as the governance, policies and controls required.

Through active learning and Q&A sessions alongside an expert tutor, participants will acquire the necessary tools to improve the robustness of models in order to withstand the current volatile markets. 

Learning objectives

  • Identify relevant model validation techniques and approaches
  • Implement artificial intelligence (AI) applications to model risk management (MRM) frameworks
  • Address the impact of environmental, social and governance (ESG) factors in financial risk modelling
  • Align current trends and regulatory requirements between AI, machine learning and MRM
  • Apply appropriate policies and procedures into MRM frameworks
  • Evaluate the results of model quantification

Who should attend

Relevant departments may include but are not limited to:  

  • MRM
  • Model risk
  • Pricing models
  • Credit models
  • Risk management
  • AI
  • Data science
  • Technology
  • Regulation
  • Front office

Agenda

May 20, 2024

In-person. Location: Unit 1801-05, Shui On Centre 6-8 Harbour Road Wanchai, Hong Kong

Sessions:

  • The importance of managing model risk
  • Model risk regulatory landscape
  • MRM frameworks
  • Case study one: end-to-end processes for MRM
  • Ideal infrastructure for managing model risk
  • Case study two: examples of model risk quantification
  • Model risk in artificial intelligence and machine learning models
  • Case study three: presentation and use of model risk metrics

VIEW DETAILED AGENDA

Tutor: 

  • Yan Li, model risk management specialist

Guest speaker:

  • Hua Xiang, Head of model validation, Bank of China

Tutors

Yan Li

Model risk management specialist

View bio

Yan Li is a specialist of model risk management. He led risk consulting service on model risk management for KPMG Hong Kong and had consulting experiences for both the Hong Kong Monetary Authority and the Securities and Futures Commission in terms of internal model approach and model risk management for traded risk and stress-testing.  Yan has in-house model risk management experience in HSBC in the independent model review team.  Yan had also worked for BNPP in risk global markets and for HSBC in the traded risk team. Yan holds a PhD degree from the Economics Department of UCL. He was previously a lecturer in Aston Business School of Aston University, and a lecturer in Remin University of China.

Hua Xiang

Head of model validation

Bank of China (Hong Kong)

View bio

Hua is currently the head of model validation at BOCHK where she manages both model risk management and model validation functions. Hua has worked in modelling, analytics, risk management areas for about two decades and built her expertise on model risk management in her role at HSBC Asia Pacific covering all models underpinning the bank’s operation and decision making for 10 years.

Hua also has decent experiences in both risk management and asset management. Previously, as head of development and commercialization at the Lab for AI-powered FinTech, the only FinTech lab sponsored by the Hong Kong government, she has built a team and successfully delivered securities analytics tools covering valuation and risk measures, as well as algorithmic trading strategies by combining traditional valuation approaches for financial products with AI techniques to utilise big data. The analytical solutions carry her passion to put return, risk, liquidity, and compliance perspectives in one analytical tool package for asset management.

Hua holds a Ph.D. in Operations Research from The Chinese University of Hong Kong with specialisation in optimisation techniques for profit making, with the constraints of operation and cost efficiency. She has established rapport and credibility with diverse stakeholders to drive business growth, enhance risk capabilities, and promote strategy efficiency through her career in the financial industry.

Accreditation

This course is CPD (Continued Professional Development) accredited. One credit is awarded for every hour of learning at the event.

Pre-reading materials

The Risk.net resources below have been selected to enhance your learning experience:

  • A new automated model validation tool for financial institutionsRead article
  • Model risk management is evolving: regulation, volatility, machine learning and AIRead article
  • Unlocking the power of model ops for risk management gainsRead article

To access some of the above articles you need to have a current subscription to Risk.net. If you don’t have one now, please subscribe to a free trial

Registration

May 20, 2024

In-person, Hong Kong

Price

$1,499

Early-bird Price

$1,299
Ends April 19
Book now

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  • Agenda and registration process
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  • Customisation of this programme
  • Season tickets options

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