We’ve all heard it before: “Win or go home.” Whether in business or on the playing field, the pressure to win is intense.
copyright by www.bankingexchange.com
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 artificial intelligence (AI) or machine learning (ML) models to power their digital transformation initiatives forward.
Why the frantic push to deploy AI and ML models now?
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, machine learning models can consume vast amounts of unstructured data, spot patterns and translate them into usable information. These models improve automatically through experience – by “learning” – which results in greater accuracy and predictability over time. Not only are such capabilities enticing, they’re becoming imperative in an industry driven by continual change, digital interactions and a “need it now” consumer mindset.
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 AI and ML models to support key risk business cases.1 Banks are also using machine learning models for marketing, fraud detection and anti-money laundering.
But with all good things, there’s a catch. AI and ML models need more governance than other data models. Winning in the digital space also requires a well-drilled team that communicates clearly across all players – data scientists, modelers, validators, auditors and managers alike. That’s much easier said than done.
Business leaders: Failing to prepare is preparing to fail
Deploying AI models in haste can have serious consequences. For example, losses occur as a result of poorly functioning models – including loan origination, debt management and pricing. Some firms have even gone out of business by deploying models that weren’t properly managed and tested. Consider well-documented model-risk-based failures, such as the Flash Crash of 2010, the London Whale or Knight Trading’s losses.
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 AI, sobering questions arise.
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.[…]
read more – copyright by www.bankingexchange.com
We’ve all heard it before: “Win or go home.” Whether in business or on the playing field, the pressure to win is intense.
copyright by www.bankingexchange.com
Why the frantic push to deploy AI and ML models now?
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, machine learning models can consume vast amounts of unstructured data, spot patterns and translate them into usable information. These models improve automatically through experience – by “learning” – which results in greater accuracy and predictability over time. Not only are such capabilities enticing, they’re becoming imperative in an industry driven by continual change, digital interactions and a “need it now” consumer mindset.
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 AI and ML models to support key risk business cases.1 Banks are also using machine learning models for marketing, fraud detection and anti-money laundering.
But with all good things, there’s a catch. AI and ML models need more governance than other data models. Winning in the digital space also requires a well-drilled team that communicates clearly across all players – data scientists, modelers, validators, auditors and managers alike. That’s much easier said than done.
Business leaders: Failing to prepare is preparing to fail
Deploying AI models in haste can have serious consequences. For example, losses occur as a result of poorly functioning models – including loan origination, debt management and pricing. Some firms have even gone out of business by deploying models that weren’t properly managed and tested. Consider well-documented model-risk-based failures, such as the Flash Crash of 2010, the London Whale or Knight Trading’s losses.
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 AI, sobering questions arise.
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.[…]
read more – copyright by www.bankingexchange.com
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