Would you trust a robot e-banker?

In retail banking, trust and a commitment to protecting consumers’ information, money and investments are of paramount importance when it comes to the overall customer experience (CX).

And while technologies like AI-powered robotic process automation (RPA), virtual assistants and chatbots can elevate CX by reducing human error and speeding up consumer-facing requests, trust issues surrounding AI and data privacy continue to be a hurdle for consumers, slowing the digitization of retail banking.

Companies that benefit from automation must address these trust issues, wrote Ajay Bhalla, Mastercard’s president of cyber and intelligence solutions, in a recent editorial for the World Economic Forum. “The thirst and drive to innovate with these new technologies at speed must be balanced with the need to carefully build consumer trust in those same innovations,” says Bhalla. This is becoming especially relevant now that wider adoption of digital transactions throughout COVID-19 has accelerated the volume and pace of data creation, boosting the role of AI in our lives.

The key, says Bhalla, lies in bringing consumers on that journey.

Bots in banking

The benefits of RPA, AI and chatbots are starting to materialize as the technology sees wider adoption. A recent study from Juniper Research found that the use of chatbots in banking will save $7.3 billion in operational costs by 2023, up from an estimated $209 million in 2019. According to the report, “this represents time saved for banks in 2023 of 862 million hours, equivalent to nearly half a million working years.”

Chatbots are to the front-end side of CX what RPA is to the back-end. Like a virtual worker, RPA can replicate human actions, streamlining processes. Customer onboarding, for instance, often requires tedious manual entry for setting up individual accounts. RPA can easily tackle this task, freeing up people to focus on other steps of the CX journey. RPA can also be used to sift through large volumes of data to identify fraud, to research clients more quickly for the Know-Your-Customer process, as well as to process cheque deposits and borrowing approvals.

At the recent Sibos banking and financial conference, Lisa Frazier, chief innovation officer at Wells Fargo said that AI and machine learning are “fundamental to the future of banking.” These technologies are already playing an important role in using data to adapt to customers’ evolving needs.

A multitude of specialized financial companies are already offering this type of personalized experience. Take robo-advisors like Wealthsimple, for example. These types of companies, which use algorithms to allocate investments on behalf of their clients’ individual risk profiles and investing preferences, have garnered significant interest over the past few years. Straddling the line between automation and algorithms, these robo-advisors leverage tech to keep fees low for consumers. Despite their great success, however, the trust survey released by the CFA Institute in May 2020 noted that three-in-four respondents say they still prefer to receive advice from a human. At the same time, 48% of retail investors say they would be willing “to pay more for personalized products and services.” This is a perfect example of how blending smart, personable customer service with the latest digital tools can meet the needs of almost all customers.

The hang-ups of digitization

While retail banking companies are eager to lean-in digitally, consumers’ digital anxieties are still slowing that trend from fully materializing.

A Longitude/Mastercard study from August 2020 found that less than a third of consumers (30%) feel comfortable interacting with AI, and more than half (53%) believe AI “will always make decisions based on the biases of the person who created its initial instructions.”

As Bhalla pointed out in his editorial, high-profile cases of AI misuse have had a tremendous impact on consumer trust. “The subsequent fallout has also raised greater global awareness of the broader issues around the use of data and our personal information,” he wrote.

Chatbot technology also seems to fall into the same bucket for consumers. Countless industry surveys have shown that the majority still prefer to wait in a phone queue for a human agent because they don’t think chatbots will understand them or know how to solve their issues.

But consumers actually use AI and chatbots more frequently than they realize. It’s AI that finds them the best route in Google Maps and cues up the next show in Netflix; it’s just that consumers are not consciously choosing AI in those circumstances, but rather passively letting AI help them. So we know consumers aren’t totally opposed to using AI if they can predict how it will serve them, and if that help is streamlined into their existing customer experience.

That’s good intel to have, since many major retailer banks and financial services companies are starting to heavily rely on consumer-facing chatbots and voicebots. As of the first quarter of 2020, Bank of America’s virtual assistant, robo-banker Erica, has helped more than 12.2 million users check their balance, monitor their credit scores and learn more about their budgeting and spending habits.

Moving towards trust

A lot of the disconnect between the benefits of AI, RPA and chatbots, and consumer interest in using these tools on a day-to-day banking scale, comes from the fact that they feel highly-technical and complicated. “The trust that is needed for it to be most effective will come when consumers see and feel its real-world benefits in action,” wrote Bhalla in his WEF editorial.

Retail banks can build consumer trust in bots by finding the right balance between high-tech and high-touch interactions, making transitions between the two smoother, and making sure the consumer understands when a hand-off is happening. Banks can further build trust by communicating how next-gen technologies can play a pivotal role in protecting their customers’ data from breaches and fraud, and how the company will act to quickly identify and handle threats. This open communication is essential to build consumer trust and further develop AI tools in such a highly sensitive sector, noted Bhalla. “For those that get it right, the possibilities are endless.”