The financial services industry is fueled by data. Lots of data. This information deluge includes countless processes on the backend, as well as a myriad of user-facing transactions on the front. Complex business systems such as these are designed to process large amounts of data and therefore are particularly ripe for digital transformation through Artificial Intelligence (AI).

Banks and other financial institutions have traditionally been leaders in the development of AI solutions to handle all manner of tasks. As early as 2008, stock trades by algorithms constituted half of all market volume (a number which now accounts for 90% according to some observers). In more recent years, the industry has started tapping AI-powered solutions such as Amelia to automate, optimize and scale their user engagements on the front end. Since so many pioneering financial companies are already years into their user-facing AI journeys, they offer some lessons for companies just beginning theirs.

Be Mindful of the User Experience

When enterprises tap AI to automate internal digital services, they open the door to new operational efficiencies that benefit both company and workers. Of course, in order for an implementation to be successful, the automation backend must be partnered with an intuitive, human-like User Experience (UX). Otherwise, adoption will linger and frustrations will grow — which defeats the purpose of using automation and AI in the first place.

Case in point: In early 2017, a major international bank hired Amelia to automate IT support for thousands of users across dozens of countries. Not only does Amelia provide access to personalized information, but her backend integrations empower employees to independently resolve many IT issues with little-to-no human intermediation. But all that functionality would have been for naught if no employees actually used it.

When developing the UX for AI, the bank’s implementation team made sure to get the product in front of users early for a variety of feedback outside of the team developing and deploying the solution. Furthermore, the company organized a well-rounded Cognitive Center of Excellence (CCoE), which included not only engineers but linguists and neurologists. The company credits this IT-plus model, as well as early employee feedback, with accelerating adoption of Amelia.

Keep Secondary Benefits in Mind

Banking customers directly benefit from user-facing automations through always-available access to information and resolutions, without the need to wait in a queue. However, automation often has secondary effects that benefit both customers and employees.

Customer-facing automation has the potential to free experienced support staff from high-volume tasks, so they can spend more time addressing complex or unique customer needs that the AI system cannot handle. Furthermore, AI can automate many associated service tasks, such as information gathering, which is another way to redirect employees toward provide more personalized service.

A major Spanish bank with millions of customers hired Amelia to automate high-volume customer engagements at its call center. For tasks that Amelia is not able to independently resolve, she acts as a “customer service filter” who accelerates resolution times by directing customers to the relevant bank department and pre-collecting customer info before handing off a consumer interaction to a human agent. Furthermore, Amelia cut call volume by encouraging callers to seek out answers to their general questions on the bank’s website, resulting in a 69% abandon rate. That’s considered a positive metric for this customer service segment, as it means customers were pointed in the right direction without the need for a human to step in. Amelia now handles 32% of the bank’s total calls related to credit and debit card fraud claims, with plans to scale Amelia’s abilities into other areas.

Many companies are only now taking their first steps into reinventing their businesses with AI. The good news for these newcomers is that AI technologies have only become more powerful and useful in recent years. But perhaps even more importantly, they will have the advantage of taking lessons and inspiration from the true AI pioneers who have come before them.

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