The transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in financial services, particularly in the risk management realm, cannot be denied. From navigating ongoing macroeconomic uncertainty to bolstering credit risk decision-making to addressing climate uncertainty, these advanced analytic techniques can help banks and insurers take on their most vexing challenges. Yet, despite leaps in adoption rates, these technologies remain intimidating for many.
Two experts from SAS, a leader in analytics, seek to remedy that with a new book, Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning and Deep Learning, published by Wiley as part of the SAS Business Series.
A definitive guide for scaling the AI learning curve
While risk professionals recognise that AI and Machine Learning are essential to achieving their transformation goals, roughly half (48%) still identify AI and ML among their top challenges. That is according to a recently released risk technology study by the Global Association of Risk Professionals (GARP) and SAS, based on a global survey of 300 banking risk pros.
In this primer for risk practitioners, co-authors Terisa Roberts, Global Solutions Lead for Risk Modeling and Decisioning; and Stephen Tonna, Senior Banking Solutions Advisor, demystify AI and ML through practical guidance and real-world examples. The book is a definitive resource for risk managers, compliance officers and other industry professionals striving to apply the most advanced analytic technologies to tackle their quantitative risk problems – from the everyday to the more complex.
“Adopting AI and Machine Learning to mitigate credit, market, liquidity and other emerging risks is paramount, yet many remain on the sidelines,” said Roberts. “Practitioners and business leaders must overcome the learning curve – and quickly. Facing a rocky global economic picture and with the market impacts of climate change on the horizon, the financial services sector simply cannot afford not to embrace AI.”
“By dispelling common misconceptions, like the perceived ‘black box’ nature of AI and Machine Learning, and outlining how to overcome challenges, like bias and interpretability, we aim to dispel the lingering reticence around the use of these technologies in risk modelling,” said Tonna. “The sooner risk practitioners, executives and the C-suite overcome their hesitation, the sooner they can realize the many tangible benefits of these technologies.”