Intelligent Issue 05 | Page 58



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 .

“ 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 .” �
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 , coauthors 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 realworld 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 .
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