AI Strategy in Banking: Choose Your Spots

When it comes to artificial intelligence (AI), most banks have focused first on productivity gains, such as automating repetitive tasks, and on reducing fraud or regulatory risks with improved anomaly detection and monitoring methods. Some banks have started to use AI in capital market operations.

While not every bank needs to be a frontrunner (one of five archetypes shown), frontrunners do share some instructive patterns. They’ve built out AI maturity and adoption throughout the organization. They focus on generating value for customers by, for instance, anticipating personalized offerings.

The position a bank aspires to will depend on three criteria:

• the competitive environment in the bank’s market;
• the nature of the customer base and the specific services and features that high-value customers expect; and
• the bank’s ambition to become a technology-driven company.

Banks that want to improve their AI capabilities should take a multiyear perspective, focusing on excelling at a few use cases, spreading those lessons throughout the organization, and then accelerating to take on new ones.