Overcoming AI and ML roadblocks in banking and insurance

MORE THAN A THIRD OF TECHNOLOGY LEADERS at banks and insurance firms (36%) say they’re not free to select the tools they need to build and deploy artificial intelligence (AI) and machine learning (ML) models. And nearly all (96%) report that organizational security and compliance policies hinder their ability to obtain those tools.

These findings, from a new study by IDG and Red Hat, highlight some of the struggles in the banking and insurance industry around AI and ML deployment. They come at a time when 86% of banking firms rate deploying AI as important to their success in the next two years, according to Deloitte.

But the IDG/Red Hat study also offers hope for overcoming obstacles to putting AI and ML models into production.

The Value of AI and ML in Banks and Insurance Firms

Artificial intelligence and machine learning have become critically important to banks and insurance companies. That’s because banks, insurance firms, and other institutions increasingly rely on AI to help with customer service, fraud detection, and more. Those that don’t risk getting left out of a potential $1 trillion-a-year increase in value, according to an estimate by McKinsey & Company.

Even so, obstacles remain for deploying AI and ML models, putting companies at risk of falling behind their competitors. To get a read on some of those challenges, IDG and Red Hat surveyed IT decision-makers (ITDMs) at 100 banks and insurance firms in the US with 5,000 or more employees. The survey also uncovered solutions for meeting those challenges, most notably turning to cloud resources and common platforms for deploying AI and ML models.

Check out this IDC Whitepaper to learn more.


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