AI: No longer optional but mandatory for financial services

With everything they do, financial organizations are looking to make money or save money. And their customers—whether commercial or private individuals—are looking to achieve these same goals. This isn’t a new realization for financial organizations, but a lot of other things have changed in how their customers can meet their financial goals. Some of the most impactful changes are the amount of choice, availability of information, and evolution of technology. So, why is AI so important—or should I say mandatory—for financial services from now on?

For centuries, there has been competition within the banking world. Companies and individuals could choose between different organizations to meet their financial requirements. The amount of choice was never that large, and more often than not, customers would choose just one or two institutions for everything they needed—and that was fine.

However, things change. There are now more financial services organizations looking to help customers. It’s no longer just the brick-and-mortar bank against its neighbor. More companies are being created to look at niche parts of the financial services world, offering specialized services, and there are also larger nontraditional organizations (especially IT companies) entering the financial services market. With all these choices, it’s not always a financial decision that influences where customers take their business, but the experience customers have.

For the traditional banks to thrive against newer, more agile competition, AI is something that absolutely has to be embraced. The advantage that these traditional players have over their newer competition is financial data on customers, but the sheer amount of this data can be overwhelming. By using AI to analyze and process such vast quantities of data, spot patterns, and suggest courses of action with increased reliability, financial institutions can not only speed up decision making but handle all levels of competition.

As the world becomes more connected, the amount of information continues to grow at an exponential rate: The amount of data that we create is expected to hit 1 yottabyte by 2023. This data can give us greater visibility into events that influence decisions—but it can also introduce more irrelevant information. Trying to decipher what is credible, what is real, what is current, and what will make a difference is becoming harder and harder.

As you look at what will affect investments, decide on factors for loans and credit, or look for patterns to provide better services, using AI is going to become more critical—even mandatory! 

AI isn’t a new concept in financial services. However, technological advancements have increased the speed of evolution in this space. Previously, running high-powered CPUs in a PC under your desk was the norm for data analytics and early AI in financial services, and this attitude hasn’t changed much over the years. Sharing data was hard, time consuming, and expensive, often resulting in multiple copies of the same small dataset throughout the organization.

As technology continues to develop at a considerable pace, the shift is moving to more centralized, enterprise-level deployments for AI. Using high-powered GPUs to process AI models at a faster rate, organizations can increase training and improve resource sharing. Sharing data through enterprise technologies can help organizations comply with regulations, reduce costs, and improve productivity across teams.

AI will continue to expand, and it becomes mandatory if financial services organizations are going to thrive among ever-growing competition and increased customer demands. Underpinning AI is the heavy requirement on data.

Being able to collate large quantities of data and keep it available for data scientists and AI deployments—without sacrificing data security—are capabilities that NetApp has honed over the years. By providing industry-leading storage and data-management products, NetApp helps customers meet the heavy data demands of AI at enterprise scale.