How AI may help solve banks' customer relationship issues

Learn how artificial intelligence and analytics can be used to improve customer service in banking.

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Image: iStockphoto/seb_ra

When I was a CIO for a financial institution, I worked with executives on the operations side to see how we could improve relationships with customers at our branches.

Our front-line tellers at these branches were more like order takers--they did what customers asked, but no more. These employees were in low-wage positions, and they often had limited skills. One of the skills we wanted was interpersonal engagement with customers that you would typically find in a salesperson.

We decided to hire people with retail and/or people-facing experience, figuring that we could train them to be tellers. We implemented systems that would prompt a teller to ask a customer about new products the customer might be interested in, and we offered financial incentives for enrolling customers in new products. 

The experiment yielded mixed results and likely would have gone better if we'd had some of the analytics and artificial intelligence (AI) automation tools that are available today. 

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"Most customers tend to keep their accounts with the bank even if they are not too happy," said Eran Livneh, VP of Marketing at Personetics, which provides AI solutions for the financial services industry. "However, there are now many banking and non-banking alternatives available that were not available in the past that customers can also consider. Millennials also expect more personalization and technology-driven interactions with their financial institutions."

Livneh believes that over time, bank customers will take an increasing portion of their financial relationships and activities to alternative investment platforms; this will erode banks' ability to maintain these relationships and grow new ones. "We are already seeing flat or decreasing deposits for many of the US regional banks as a result," Livneh said.

This is where AI comes in and where banks have an advantage because of all of the data they already have on their customers.

"AI can help identify customer personal behavior patterns in their financial transactions and highlight the products, services, and areas of interest that are most important and useful to each customer at a given moment," said Livneh. "It can also be used to turn the experience from looking in the rearview mirror into forward-looking insights and advice, empowering customers to make better financial decisions."

The AI works by aggregating and categorizing customer account activity to provide an integrated view of a customer's financial history. Analytics are then added to highlight exceptions and important events in the customer's history. 

The AI engine works on this data to develop prescriptive product and service recommendations for bank frontline personnel to present to customers. This AI can also be integrated into online applications to enable self-service recommendations, which are favored by millennials.

By using AI, banks have improved performance against KPIs by increasing customer engagement, improving customer satisfaction, reducing customer attrition, and increasing deposits and product adoption.

"Royal Bank of Canada recently launched an AI-powered budgeting tool that offers personalized advice to its mobile app customers," said Livneh. "Within the first month of launching, 230,000 budgets have been set up, and customers have been able to save over $83 million total."

This is just one example of how advanced analytics capabilities like AI are making a difference in the quality of customer relationships that are impactive and long-lasting.

To move forward with AI, banks can start by collecting and analyzing both transactional and non-transactional big data on their customers, training their frontline personnel to use these AI engines (which can be integrated into teller applications), and creating easy-to-use online apps for customers who prefer to bank electronically.

"AI and personalized customer analytics are not an all-or-nothing proposition but rather a multi-level framework with increasing degrees of autonomous capabilities," said Livneh. "Each level provides a foundation for more advanced capabilities that can be added to the bank's offering while delivering immediate value to the customer and to the bank. The earlier you start, the more data you have to make it better."

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