Model Risk Australia

This two-day workshop has been designed to delve into best practice approaches to building a model risk framework. Attendees will be equipped with a thorough understanding of model risk now and into the future, including the impact of machine learning.


Model Risk Australia

13-14 August | Sydney


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This course has been designed to delve into the best practice approaches for building a model risk framework and the sessions will be led by expert speakers from a range of consultancies and financial institutions.

Steven Claxton

senior analytical consultant


Stephen Edney

head of market risk quantitative support

National Australia Bank

Stephen is the Head of the Markets Risk Quantitative Support team at NAB since 2015, looking after model validation of derivative models across traded and non-traded markets. Over the previous 15 years Stephen has held roles in NAB in London, CBA and St George Bank, across model validation and model development for traded and non-traded markets. Stephen has also worked with ISDA as part of regulatory engagement with the Basel Committee on the Fundamental Review of the Trading Book reforms.  He holds a PhD in Physics from the University of Sydney.

David Maher

associate director, market risk

National Australia Bank

David is currently the Market Risk Oversight lead for Fixed Income and Interest Rate Derivatives. Prior to this he worked for 10 years as a quantitative analyst in model validation and development. This also included 4 years on NAB’s trading floor in London.  More recently, he has been implementing Machine Learning in the market risk space, including  applications to FRTB.

David holds a PhD in Pure Mathematics from UNSW, and a BSc from Macquarie University, Sydney.

Song Ling Ooi

senior manager – pricing & modelling


Song leads the Pricing & Modelling team within the Chief Investment Officer function of the Australian Wealth Management business unit in AMP. She is the Senior Manager responsible for the development of the pricing methodology and pricing recommendations for product developments – in particular for the North Guarantee.

Song is also responsible for overseeing the development and assurance of complex models that support both hedging and pricing activities. Having prior data and modelling experience in business areas including Customer Analytics, Hedging Strategy and Operations,  Song understands that a strong risk culture and risk management framework is essential for teams to be able to safely operate in a fast-moving environment that allows for innovation at the same time.

Song is currently leading the education of the Model Risk Management concepts across the CIO function. Her goal is to encourage everyone to embrace the importance of working within the risk management framework and motivating everyone to engage, practice and get better at managing model risks.

Timo Reinemer



Timo is a Director in the Treasury and Capital Markets team at Deloitte Australia. He has 8 years’ experience in providing advisory and assurance services to financial institutions around the globe. During various projects along the three lines of defence he has gained extensive experience in a wide range of quantitative fields including modelling of market risk and counterparty credit risk. He also has developed expertise in qualitative areas such as risk management processes. He focusses on model implementation, development, validation and governance in the Markets business.

About the Course

Risk Training have designed this two day training course to delve into best practice approaches to building a model risk framework. The course will also equip attendees with a thorough understanding of model risk now and into the future, including the impact of machine learning.  

Day one will cover the history of model risk management and the regulatory landscape, followed by how to go about building a model risk management strategy and manage a models life cycle. The day continues with an overview of model validation and prudent valuation.

Day two begins with an overview of how to manage model risk for non-pricing models, before going on to a discussion on credit model validation and what new strategies need to be in place to be CECL compliant. The course finishes with discussion surrounding utilizing machine learning for model validation and a look at model risk into the future.

Group Discussion
What Will You Learn?
  • Best practice approaches to building a model risk management framework
  • Effective methods for model validation and related performance analysis reviews
  • Pricing models and prudent valuation and how to establish frameworks for both
  • CECL regulation and its impact on model risk management
  • Machine learning and AI and how to embrace it in the context of model validation
  • The history of model risk, capturing model interconnectivity and governance 
Who Should Attend?

Relevant departments may include but are not limited to: 

  • Risk Management
  • Quantitative Analysis
  • Model Risk and Validation
  • Compliance
  • Internal Audit
Course Highlights
  • Model Risk Management & Governance
  • Building a Model Risk Management Framework 
  • Model Validation & Performance Analysis 
  • Pricing Models & Prudent Validation 
  • Model Risk Management of Non-Pricing Models 
  • Model Validation for CECL 
  • Utilizing Machine Learning for Model Validation  
  • Model Risk into the Future