We’ve all heard it before: “Win or go home.” Whether in business or on the playing field, the pressure to win is intense.
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We’ve all heard it before: “Win or go home.” Whether in business or on the playing field, the pressure to win is intense. And in today’s financial services industry, the winner can literally take all. As banks struggle to adapt in the throes of digital disruption, executives find themselves squeezed to use () or (
Why the frantic push to deploy
The industry’s use of computational finance models to make decisions is nothing new. Models create a good competitive posture because they save time; help establish repeatable, reliable processes; and produce fast results based on more (equating to “better” ) data. But traditional statistical models are limited in the number of dimensions they can access.
Unlike traditional statistical models,
A recent survey by SAS and the Global Association of Risk Professionals (GARP) found that, over the next three to five years, businesses expect to significantly increase adoption of
But with all good things, there’s a catch.
Business leaders: Failing to prepare is preparing to fail
Deploying
New types of models raise the potential for operational risks due to unexpected impacts on an otherwise stable, well-managed business. When you add concerns around regulations, personal data privacy, “black box” transparency and explainable
Business leaders in firms adopting these new techniques must demand a clear understanding of models in development and deployment, at an enterprise level. Obtaining this comprehensive view calls for business leaders to seek out an explanation of the purpose and history of models in business terms – not technical modeling jargon.[…]
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