Apr 13 2022
Data Analytics

Artificial Intelligence for Banking: How It's Being Used for Risk Mitigation & Revenue Generation

As financial institutions explore technological means for managing data, they should consider automated tools that add protection while increasing value.

The rise of online banking, financial apps and mobile devices has resulted in a steady barrage of data for financial institutions. As many of them grapple with the overwhelming volume of incoming data, others are implementing innovative technology to realize business outcomes by turning that data into revenue.

According to a recent survey by Open Text, many financial services firms are currently reviewing how to apply artificial intelligence and advanced analytics to reshape their organizations — from internal operations and customer experience to treasury services and payments.

According to Open Text, “the world’s most successful Financial Services organizations are already considering AI and machine learning to help them address” such issues and functions as payment processing; personalization of customer experiences; and compliance, anti-money laundering and fraud detection efforts.

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Many Banks Are Already Using Some Common Applications of AI

As Business Insider reports, “most banks (80 percent) are highly aware of the potential benefits presented by AI and machine learning. In fact, many banks are planning to deploy solutions enabled by AI: 75 percent of respondents at banks with over $100 billion in assets say they're currently implementing AI strategies, compared with 46 percent at banks with less than $100 billion in assets,” according to a UBS Evidence Lab report.

Business Insider further notes that many banks are using AI in front-office applications “to smooth customer identification and authentication, mimic live employees through chatbots and voice assistants, deepen customer relationships, and provide personalized insights and recommendations.” 

In middle-office functions, the UBS report states, AI is being used to assess risk, detect and prevent fraud, improve processes to prevent money laundering, and perform know-your-customer regulatory checks. “The winning strategies employed by banks that are undergoing an AI-enabled transformation reveal how to best capture the opportunity. These strategies highlight the need for a holistic AI strategy that extends across banks' business lines, usable data, partnerships with external partners, and qualified employees.”

READ MORE: Learn how banks can decide which storage solution is right for their data.

Banks Must Make Some Key Changes to Enable Greater Use of AI

In a 2021 report, McKinsey notes that “for artificial intelligence to deliver value across the organization, banks need core technology that is scalable, resilient, and adaptable. Building that requires changes in six key areas.”

The report goes on to state that as AI technologies play an increasingly central role in creating value for banks and their customers, financial services organizations need to reinvent themselves as technology-forward institutions so they can deliver customized products and highly personalized services at scale in near real time.

Such a transformation would place crucial demands on core technology and data infrastructure, McKinsey writes. Key considerations should include: a robust strategy for building technology capabilities; superior omnichannel journeys and customer experiences; a modern, scalable platform for data and analytics; a scalable hybrid infrastructure strategy for the cloud; highly configurable and scalable core product processors; and a secure and robust perimeter for access. 

MORE FROM BIZTECH: Discover other emerging uses for AI in the financial sector.

A Well-Developed Strategy Is Key to Realizing AI’s Potential

For banks seeking to implement AI in their overall IT strategy, a recent Deloitte report lists six important steps to make it possible. The first is to develop an AI strategy. The report states, “To stay competitive in both the short and long term, banks must escalate AI as a foundational component instead of treating it as a stand-alone initiative.”

Next, a bank should define a use case–driven process. “Defining relevant use cases and prioritizing them into a road map can help banks stay focused during implementation and help achieve the goals defined during the strategy phase,” Deloitte writes.

Experimenting with prototypes and building with confidence are essential, as well. According to research in Deloitte’s State of AI in the Enterprise, “60 percent of financial services AI adopters believe that risks associated with using AI-based models are slowing their organizations’ adoption of AI technologies. Despite this awareness, only about 36 percent of adopters are actively addressing the risk — establishing guiding principles or a board and following leading practices.”

Finally, the report states that AI tools must be scaled for enterprise deployment and should drive sustainable outcomes. “The goals for AI after deployment should focus on continuously learning how models react to various inputs and identifying ways to improve results.”

Getty Images/ Galeanu Mihai

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