On-Demand Webinar

Model Governance Full Disclosure – Using Resilient AI/ML Systems with Confidence

Available Now | Cost: Complimentary

The value of sophisticated model risk management extends well beyond the satisfaction of regulatory regimes. But how can we ensure that their frameworks are capturing this value thoroughly? Join us to find out the answers! Our speakers deep dive into using resilient Artificial Intelligence (AI) and Machine Learning systems with confidence.

  • Governance for confidence: How to deal with Black Box Models

    How can you use AI/ML models without knowing what is inside them? Many model explainability methods make good guesses but may not meet the confidence levels of managers who are used to knowing exact variables and their contributions. This session explores how managers can design governance structures and processes to provide them with the confidence to use models with unknown variables.
  • Building resilient AI systems that you can trust

    In an age of automation and digitalization, the use of Artificial Intelligence and Machine Learning (AI and ML) is now mainstream in our society. It is also delivering tangible benefits in Risk Management: AI and ML are able to improve the accuracy of risk estimation models, automate repetitive processes, and accelerate risk-based decision making. However, ‘brittle’ AI and ML models are costly to develop and validate and do not transfer well to real-world applications. How can firms build more trust and confidence in AI and ML by improving model resiliency, explainability and putting in place the right controls? The session highlights practical cases where AI and ML deliver value to the Risk function and discusses global industry practices on how robustness assessments can be incorporated in continuous monitoring frameworks.

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About the Experts

Naeem Siddiqi

Naeem Siddiqi

Senior Advisor, Risk Management
Risk Research and Quantitative Solutions
SAS

Naeem Siddiqi is the author of Credit Risk Scorecards : Developing and Implementing Intelligent Credit Scoring, (Wiley and Sons, New York, 2005), Intelligent Credit Scoring : Building and Implementing Better Credit Risk Scorecards (Wiley and Sons, 2017), and various papers on credit risk topics.

Naeem meets with senior executives and decision makers worldwide, and provides strategic advice to them on areas such as the development and validation of credit scoring models, infrastructure planning for analytics, and retail credit risk strategy. He is also responsible for SAS climate risk solutions. He has trained hundreds of bankers in over 25 countries on the art and science of credit scorecard development, and helps credit risk analysts develop better scorecards.

Naeem has an Honours Bachelor of Engineering from Imperial College of Science, Technology and Medicine at the University of London, and an MBA from the Schulich School of Business at York University in Toronto.

Terisa Roberts

Terisa Roberts

Global Solution Lead – Risk Modeling and Decisioning
SAS

Dr Terisa Roberts is the global solution lead for Risk Modeling and Decisioning at SAS. She has nearly two decades of experience in quantitative risk management and advanced analytics.

Throughout her career, she has helped companies in financial services, telecommunications, government, energy and retail derive business value and make better decisions using risk analytics. She has domain experience in regulatory compliance, IFRS9, BCBS239, model risk management and enterprise stress testing.

She advises banks and regulators around the world on industry best practices in Artificial Intelligence, automation and digitalization related to risk modeling and decisioning and in the responsible use of AI and Machine Learning. She regularly speaks at international Risk conferences on innovation in Risk Management.  

She holds a Ph. D in Operations Research and Informatics and lives in Sydney, Australia with her family.

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