How Banks Can Lead the Shift to AI-First
Artificial intelligence can be transformative for banks — if they get the strategy right.
Banking provides a fertile ground for artificial intelligence. After all, AI lives on data, and banks are information businesses with terabytes of data. One breakthrough in AI is supervised learning, which enables a machine to mimic a human’s decision-making process based on what may be millions of examples. This advance has made switching to an AI-first business model a natural progression for banks, which are leading the charge in digital and mobile-first strategies.
If properly deployed in the next five to seven years, we estimate that AI could increase banks’ revenues by as much as 30% and potentially reduce their costs by 25% or more. To date, however, most banks have been scattershot in their approach to deploying AI, with models ranging from customer-support chatbots to price-elasticity analytics. But rolling out one model at a time, à la carte, without an overriding strategy, is certainly a recipe for failure given the hundreds of possible use cases AI now provides. (See “AI-First Bank Use Cases.”)
Banks need a clear AI strategy to get this transformation right. If they don’t have one, other banks that master AI will offer their customers more tailored services with lower fees. The template we’ve found working with global financial services companies to develop AI-first business models — and avoid being trapped in a revolving door of AI initiatives that are ultimately ineffective — can work for organizations in other industries as well. The most successful AI strategies are driven by four pillars: improving data assets, scaling infrastructure to allow widespread experimentation, enlisting employees so that they scout for new AI use cases, and looking for ways AI can solve customers’ problems beyond providing banking services.
Below are the four key components of a successful AI strategy, with examples of how leading companies have shifted to an AI-first paradigm:
1. Improve Data Assets
Most AI models for banking are relatively easy to build, copy, or buy. What makes them valuable is data when it is easy to access, load, and prepare for AI algorithms. Right now, banks have serious problems organizing their data.