Six AI and Big Data Trends in Banking for 2022 – The Financial Brand

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By Lisa Joyce
There’s a reason it’s called “big data.” The growth in the volume of structured and unstructured information is exploding, literally exponentially, just as Moore’s Law predicted. In fact, by 2025 there will be more than 180 zettabytes of data created and consumed worldwide, per Statista, helping to catapult the global data market to $103 billion by 2027. (One zettabyte is equal to a trillion gigabytes, fyi.)
Since most banking products and services have become commodities, financial services executives are anxious to analyze even a tiny sliver of those zettabytes in order to differentiate themselves from competitors. As Capgemini puts it, banks and credit unions must evolve from capturing and managing data to using data to deliver hyper-relevant content, products and customized pricing based on customer and member behaviors, lifestyle, personality and preferences.
Artificial intelligence (AI) applied to big data provides this range of insights.
81% of bank and credit union executives believe that unlocking value from AI will be the key differentiator between winning and losing institutions, research by The Economist Intelligence Unit and Temenos found. Whereas AI has been used primarily for fraud analysis and threat reduction in banking, that is changing. Overall, a third of financial institutions are investing in AI to support better customer experience and product personalization, the research notes.
With these imperatives in mind, here are six big data and AI trends bankers should expect to unfold in 2022 and beyond.
For vendors in the data business, the AI spending forecast is rosy indeed. IDC predicts that spending on AI will increase from $85.3 billion in 2021 to $204 billion by 2025, a compound annual growth rate of 24.5%.
Instead of putting a damper on spending, Covid-19 has made businesses even more optimistic about investing in AI. Appen, a data collector, found that 71% of technologists say that Covid either somewhat or significantly accelerated their AI strategy.
( Read More: The State of Customer Data Management in Banking )
And one of the biggest spenders will be banks and credit unions, only slightly outpaced by retail. Banking will account for 13.7% of AI spend versus 13.8% for retail.
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AI comes in two flavors: narrow and general. Narrow is the AI we are all familiar with, from weather apps to digital assistants to chatbots. While narrow AI is powerful (Accenture notes that its clients achieve time savings of 70% with AI), it tends to focus on driving efficiencies for a task or set of related tasks such as powering digital advisors and voice-assisted engagement channels.
Today few banks or credit unions have used narrow AI to deliver personalized or proactive products or services (notable exceptions are Bank of America’s Erica and RBC’s NOMI digital assistants), expect these AI-powered tools to proliferate.
That doesn’t mean that narrow AI can’t become more efficient over time. According to Accenture, combining AI with machine learning and deep learning allows AI applications to analyze data and learn and adapt with consistency that is unmatched by humans, such as providing product recommendations based on customer behavior and preferences.
General AI — at least for now — is still more sci-fi than reality. In general AI, sentient machines can think as humans can: abstractly and creatively. Banks and credit unions are not there yet, so today AI focuses on transforming business processes and improving customer experience as an extension of, not a replacement for, human intelligence.
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What good is all this data if you can’t use it? AI can make sense of data — but banks and credit unions still need employees with the talents and skills to use technologies such as natural language generation, robotic process automation (RPA) and machine learning.
According to PublicisSapient, almost one-third (29%) of banks say that lack of skills have been a barrier to transformation. To fill their talent pipeline, 37% of banks and credit unions are beginning to invest more heavily in their people and develop their existing talent.
Research by the Digital Banking Report found that they are also looking outside their walls for talent by contracting with both existing (50%) and new (43%) providers to get the AI skills they need.
Big data and AI requires intense computing horsepower, so banks and credit unions are increasingly turning to the cloud to host data and applications. Not only is the cloud able to scale to handle high computing demands, but does it cost effectively.
IDC states that global spending on cloud services — including hardware and software — will surpass $1.3 trillion by 2025, growing at a CAGR of 16.9%. Both shared (pubic) cloud and dedicated (private) cloud are slated to grow, says IDC, with private cloud growing at a faster rate.
Since bank legacy systems weren’t designed for distributed computing environments, moving them to the cloud is challenging. However, banks and credit unions are softening up to the idea of moving legacy systems not just to the cloud but transforming them to cloud-native platforms, although few have made the leap to a fully cloud-based environment.
JPMorgan Chase and Arvest Bank, have both announced that they will switch portions of their core systems to a cloud-native platform. It’s a trend that’s expected to pick up steam: Gartner predicts that cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives — up from less than 40% in 2021.
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Banks and credit unions have gone to great pains to integrate their existing data silos and have celebrated some successes, but they stumble when it comes to external data that may be locked behind firewalls or located piecemeal in a range of locations. They can’t access the data fast enough and efficiently enough to support real-time customer self-service and analytics.
Data fabric solves this issue, giving banks and credit unions a holistic view of internal and external data and a unified user interface. Enterprise data company Tibco defines data fabric as an end-to-end data integration and management solution comprised of architecture, data management and integration software, and data shared between business groups.
Think of data fabric as a sort of layer between data and applications, relieving banks and credit unions from the messy work of integrating individual data streams with individual applications.
Gartner predicts data fabric can reduce data management costs by up to 70%.
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Computing power and massive datasets make AI a high priority for banks and credit unions. But there is a growing need to look beyond individual use cases and evolve and operationalize AI development and deployment, incorporating strong AI governance with automatic updates.
AI engineers are part data scientist and part software developers that take a holistic approach to execute a bank or credit union’s AI strategy. This is critical not only for achieving the kind of hyper-relevant offers and content consumers expect but for staying ahead of regulatory oversight of AI use in business. Rohit Chopra, new Director of The Consumer Financial Protection Bureau has stated that use of AI raises questions about the transparency of decision-making and the accuracy of algorithms.
Gartner predicts that by 2025, the 10% of enterprises that establish AI engineering best practices will generate at least three times more value from their AI efforts than the 90% of enterprises that do not.
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