AI-driven banks must start with fundamentals

Artificial intelligence has been among the hottest topics in banking in 2017.

Nearly all of the biggest financial institutions have made a major announcement around new AI applications this year. This is not all hype—few industries are as vulnerable to disruption by AI as banking.

That’s because banking is a process– and data-driven industry. AI tools are ideal for leveraging data to optimize processes. That, in turn, can enable a myriad of benefits for banks, including personalizing the customer experience, automating operations, and improving risk management.

However, overall adoption of AI is still relatively low throughout the industry, with few mature case studies to help guide banking executives in deciding how to leverage AI’s disruptive potential.

Less than a third of traditional financial institutions have initiated an AI project, according to a survey of bank executives released earlier this year by Narrative Science and the National Business Research Institute.

Additionally, the difficulty in acquiring scarce and incredibly expensive AI talent means that many banks lack familiarity with AI. A report by Paysareleased in November indicates that financial services companies allocated over $82 million for AI development and research this year, but almost half of that was invested by three very large banks—JPMorgan Chase, Capital One, and Wells Fargo.

To get started on the road to leveraging AI, banks need a strategy that addresses the broad variety of AI use cases in the industry, the many challenges to implementing AI, and each bank’s own digital strategy.

For most banks, leveraging AI will start with back-end applications, as the technologies behind those applications are more mature.

Many in banking—and other industries—are fascinated by customer-facing AI applications based on computer vision and natural language processing, like chatbots.

However, it’s the rapid advancements in different types of machine learning technologies—which are well suited to gleaning insights from the mountains of data that banks compile—that are driving innovations in those customer-facing applications.

Additionally, back-end AI applications tend to involve less risk than front-end ones that directly affect the customer experience.

Still, banks have an incredible plethora of use cases to consider in regards to machine learning tools. Prioritizing those use cases will come down to two important factors: the banks’ business priorities, and the data that’s available to feed algorithms.

Where does it hurt?

Every bank has different pain points and business goals that AI can help resolve.