Artificial Intelligence & Machine Learning in Credit Risk Management
Recorded On: 09/20/2021
This on-demand webinar is available for 14 days after purchase.
In an age of automation and digitalization, the use of artificial intelligence (AI) and machine learning (ML) is now mainstream in our society. It delivers tangible benefits in risk management: AI and ML are increasingly able improve the accuracy of risk estimation models, automate repetitive processes and accelerate risk-based decision making.
However, great technologies are not without risks. It raises a fundamental question for high stakes decisions: If we don’t fully understand the workings of AI, how can we trust it? The trust issue is further highlighted by a range of challenges with AI in the news: from data privacy concerns to how do we explain black box algorithms and decisions to stakeholders.
How can firms build trust and confidence in AI and ML with the right models, controls and intrinsic explainability? The talk will highlight cases where AI and ML are delivering value to the risk function, and discuss global industry practices to address bias or fairness and how current model governance frameworks can be extended to safeguard the responsible use of AI and ML.
About the Speaker
Terisa Roberts, Director and Global Solution Lead, SAS
Terisa Roberts is a director and global solution lead for risk modeling and decisioning at SAS. Terisa has extensive experience in quantitative risk management, regulatory compliance and model governance and validation. She has worked in financial services, telecommunications, government, energy and retail sectors.
She advises banks and regulators around the world on best practices topics in risk modeling and decisioning and the responsible use of artificial intelligence and machine learning. She regularly speaks at international conferences on the application of innovative models in risk management and holds a Ph.D. in operations research and informatics. Terisa lives in Sydney, Australia with her family.