The banking and financial services industry is ripe for technological disruption. Banks are naturally experimenting with AI (artificial intelligence) to automate traditional day-to-day transactions; while insurance companies and financial advisors are chasing
alpha by layering AI on top of data to ascertain risk. These institutions in general are
expected to save more than $1 trillion by 2030 by using AI.
Enter the digital customer service representative, virtual agent, or advisor. Whatever you choose to call it, they all (at least the good ones) use AI to automate the handling of thousands of basic questions and endless tasks, that companies face every single
day. Everyone has to adapt. It simply is no longer financially or competitively feasible to rely solely on human agents.
But is the decision to implement conversational AI all about ROI? Yes, and it should be. But many firms are leveraging this automation to not only reduce associated costs, but also to uncover new revenue streams, and induce a higher level of customer loyalty.
In fact, conversational AI platforms are so sophisticated that Juniper Research
forecasts the technology has the potential to slash business expenses by as much as $8 billion by 2022.
European companies are moving even more swiftly than Americans in applying artificial intelligence across banking and financial services. This is due, in part, to a more centralized regulatory environment. Consider that DNB, which is one of the largest banks
in Europe, recently reported a rate of return of more than 14 times their investment in virtual assistants for their first-contact resolution pathways. This top-performing European financial institution was also able to reduce customer chat support by 49 percent,
while managing more than 10,000 automated conversations every day.
Know where and how to start
To really capture the ROI-boosting value of conversational AI, industry players don’t need to know how to design, develop, deploy, and train AI technologies. Best of breed digital technology providers already fill the gap between engineers, data scientists,
and business consultants, so these organizations can focus on what they’re good at, instead of becoming conversational AI experts. But businesses do need to know how to prioritize AI in their customer interactions to ensure alignment with their specific needs.
The advanced virtual agents employed by DNB use natural language processing (NLP) algorithms and new forms of semantic analysis to achieve complex, purpose-driven conversations. They also tap into a multi-level hierarchy that can process thousands of customer
interactions simultaneously, while also hooking into existing CRM and ERP systems to create more efficiency.
1. Constant availability
Today, many companies are expanding call center hours to keep up with customers. These customers are getting more demanding and want help exactly when they need it, not when the company decides it’s available.
Those that have successfully implemented conversational AI, like DNB, are already starting to decrease the availability of their human operators as one of many steps towards digital transformation.
In addition to the cost savings associated with reducing the hours human operators are available, organizations are increasing customer satisfaction by supplying an always-available company representative.
2. Instant responses
No one likes to be put on hold, and a long wait time is unlikely to encourage customers to continue a conversation with their bank or broker. If delays happen frequently, it goes without saying that it will lead to a loss of business, from both existing and
potential customers. The customer of the future is likely to be even more impatient.
Ironically, despite this ongoing push for quick service, feedback from large firms in the financial services and insurance industries has been remarkable, in that conversational AI might even respond too quickly for some customers. Because an instant response
is the default for virtual agents, Santander, for example, have reported that their customers were thrown off by a fast rate of response.
The solution has been to make the virtual assistant appear more “human” by adding a delay of 1-2 seconds to the response time. The change made it seem like the conversational AI was “typing,” and Santander never received another complaint.
3. Proactive abilities
Many people think that introducing a virtual agent has the potential to lower the quality of the service, but that doesn’t consider the clear and growing trend towards self-service. In general, this is a good thing, because the cost of providing service goes
down and customers can easily access information. But some might argue that this automated service reduces brand interaction.
Conversational AI can actually provide the best of both worlds. Taking customers back to “the good old days” when they could talk to “their guy” at the bank or credit union. What if you could talk to your customers like that again, one-on-one? What would
you say? How much more revenue would that bring?
Unfortunately, these kinds of conversations would cost a fortune today for large enterprises. But AI-powered virtual agents do still allow for personalization of service.
Storebrand, a provider of insurance and pension products, uses conversational AI technology to automatically reach out to a customer that recently purchased life insurance. They can then ask if the customer is happy with their policy, or if they can help
them with their pension savings. This has proven to delight customers and gain more business.
4. Capacity and scalability
Staffing a call center to tackle peak periods is a difficult task which often leads to frustration on both sides. Most budget chatbot technology can handle small peaks, but it takes true, innate scalability to tackle a growing number of inquiries over time.
This is also true for sudden, unexpected events--something banks, insurance companies, and financial institutions understand all too well. With proper conversational AI, organizations can handle an almost unlimited number of conversations simultaneously.
Preparing for such events will satisfy those that depend on customer service on a rainy day, and also have a positive effect on reputation, whether it’s in a crisis situation, or more generally.
One of humanity’s greatest assets is our creativity and variety. But when it comes to service and providing consistent information, even the most rigorously trained employees are known to deliver different answers, often based on their experience, mood or other
With conversational AI, that inconsistency is completely removed. It is a machine, after all, and will provide reliable, predictable responses every single time. Bank of America has developed its own virtual agent, for example, using AI to offer a standard
of consistent guidance to customers, so the firm doesn’t have to hire and train more customer service personnel.
Searching through websites for information can be a frustrating and time-consuming task. Even when a customer knows exactly what they’re looking for, using a site’s keyword-based search engine is seldom helpful. Eventually, resourceful people will find what
they need, but it will also leave a lasting, negative impression of a brand. But what about those who just can’t find what they’re looking for?
Conversational AI is now being customized or “trained” to intelligently predict, and then offer up, what customers should be looking at next through hierarchical conversation flows that cover all outcomes. This predictive element of the technology is something
that was impossible even a few short years ago.
An important role for conversational AI
It’s hard to say what the financial services and banking industry will look like a decade from now. What is indisputable, however, is that automation through AI-driven customer interactions, which is already affecting front-line access to services and transactions,
and other customer service functions, will play a significant role.
Conversational AI is now at a place where it can achieve the right balance between speed and accuracy, based on relevant information, conversation history, and predictive analytics. Firms are using it to provide the most accurate answers to first-response
questions, even as policies, people, and products shift.
Virtual agents also tap data-intelligence to route inquiries to an appropriate human agent when needed, and make second-line recommendations about how to handle issues or identify problems when the hand-off occurs.
While many executives and institutional leaders have yet to be convinced of the benefits of AI for handling customers, leaders like DNB and Santander are charging ahead and showing the way. They are driven by not only a desire to increase revenue and reduce
costs, but to accelerate the customer experience in the very first instance. And they have proven that significant gains are there for the taking.