AI adoption, risk & resilience: How are risk managers approaching the opportunities and challenges?
The adoption of artificial intelligence in banking is exciting. The opportunities are endless and the rewards are promising, but the reality of it can often be underwhelming. We spoke to leading risk leaders, including CROs around the world, to uncover the risks and challenges they face in adopting AI, and to share which areas of risk management are getting the AI treatment.
Volatility in risk regulation with CRO Bo Boisen
Bo Boisen, Chief Risk Officer, Nordea Denmark, outlines the impact of recent elections, technological innovations like AI, and the dual regulatory approach proposed by the Bank of England. The conversation covers essential skills for risk professionals amid evolving regulations and highlights the crucial role of AI and machine learning in future risk management and compliance.
The impact of trustworthy AI on business growth
In this insightful discussion, Angela Luget, Partner at McKinsey & Company, elucidates the critical importance of AI trust and how organisations can achieve it. She explains the framework for developing trusted AI systems encompassing governance, ethics, data quality, model accuracy, and user transparency. Angela highlights the commercial and societal benefits of responsible AI adoption, citing real-world examples of AI leaders. She also provides practical guidance on fostering a culture of ethical AI use within organisations, emphasising the role of senior leadership and robust risk management practices.
The future of AI models in risk with Ratul Ahmed, Commerzbank
Ratul Ahmed, Group Head of Model Validation, Commerzbank, discusses the biggest risks of adopting AI technology, and its impact on model risk, data quality, fairness, and regulatory compliance. Ratul shares methodologies for assessing AI models, emphasizes the importance of AI literacy, and explores how technology has transformed risk management practices over the years.
The impact of neural networks on model risk management
Cécile Auger, Head of Model Risk Management at Murex, explains the challenges of managing complex portfolios of derivatives, and how neural networks can accelerate calculations, reducing the dependency on costly infrastructure. Learn about the solutions developed, the precision achieved, and unexpected advantages like noise reduction in Monte Carlo models.
Will Artificial General Intelligence cause human extinction?
Professor Jon Hill, the first Professor of Model Risk Management in the US, shares his insights on the potentially fatal risks of Artificial General Intelligence (AGI). Hill also discusses the critical importance of model risk management, using past model risk failures like the tragic Boeing 737 crashes and the 2008 financial meltdown as case studies. He stresses the need for industries beyond finance to adopt robust model risk management practices to prevent future disasters.
