Mercator Blog

AI's Impact on Banking Will Be Broader and Faster Than the Impact of the Internet.
Date: August 21, 2017
Tim Sloane
Vice President, Payments Innovation

Consumers increasingly expect their smartphone will answer their questions, give them directions, and warn them when accidents will slow them down. Over time, machine learning will become as prevalent within banks as software systems are today. Eventually every software application will be reconstructed to accommodate machine learning — it’s simply a matter of time.

Mercator Advisory Group recommends immediate investment in machine learning for five reasons.

First, it is creating new payment markets and new channels, such as conversational commerce and chatbots.

Second, it is altering the way payment networks operate and reducing operational costs by improving the detection of fraud, the authentication of users, and automating operations.

Third, its ability to determine context for each consumer is changing the way consumers interact with their mobile phones, houses, cars, and all of the products and services they consume.

Fourth, machine learning can significantly reduce operational costs while establishing new revenue opportunities across all lines of business, not just payments.

Last, a first-mover advantage is often associated with the deployment of machine learning solutions. This advantage is derived from interactions with end users, data on whose actions is used to further improve the effectiveness of the model in a feedback loop known as reinforcement learning. As the solution gathers more users, the model becomes more effective, establishing a positive feedback loop that in some situations delivers an insurmountable market advantage.

The research report, Bringing AI into the Enterprise: A Machine Learning Primer, is a primer on machine learning that explains its inner workings and identifies general problems it can address (prediction, categorization, etc.). The report also describes specific solutions implemented with machine learning and suggests how the technology should be brought into an enterprise or payments organization.