AI Transformation with ModelOps

Risk Management: The Tip Of The AIceberg

Stu Bailey, Forbes – March 11, 2021

Many of the companies that I work with are large, diversified, non-digital-native enterprises. I call them “SuperPowers,” though I’d welcome a better name. They’re generally Fortune 1000 or even Fortune 100 organizations — a significant percentage of which are in regulated industries like financial services.

Most of these companies were directly impacted by the financial crisis of 2008 and made significant changes to their risk management functions in the wake of the crisis. In many cases, their enterprise compliance and risk management groups — which had always been important but were rarely the center of attention — moved to the core of corporate governance and became directly accountable to the board.

In support of this development, many SuperPowers — financial services companies in particular — centralized and fortified their governance functions and implemented systems and processes that gave them visibility and control for risk management on an enterprise-wide basis. Yet, even those companies that have mature governance practices in place are facing new challenges wrought by the rapid and broad adoption of AI. Further, many of the SuperPowers in less-regulated industries may be especially vulnerable to new risks that stem from wide use of AI technology.

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Five ways to mitigate the risk of AI models

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In recent years, the banking industry has been at the forefront of AI and ML adoption. A recent survey by Deloitte Insights shows 70% of all financial services firms use machine learning to manage cash flow, determine credit scores, and protect against cybercrime.