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Kasisto’s Zor Gorelov on the future of conversational banking

Artificial intelligence portends to change the way finance operates. From improving speeds and accuracy of customer service to automation to proactively offering clients the right product at the right time.

This week’s guest on the podcast is Zor Gorelov, the co-founder and CEO of Kasisto. Kasisto specializes in creating banking smart AI that can fundamentally transform the way banks and financial institutions connect with and serve with their customers.

I sat down with Gorelev to find out more about how Kasisto is helping its partners to achieve scale without opening another branch location.

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Below are highlights, edited for clarity, from the episode.
Conversational artificial intelligence
What conversational AI is the ability to maintain human-to-human-like conversations between humans and machines. To do that, we use artificial intelligence. It’s not just marketing, either. It spans the entire spectrum of customer interactions with the enterprise. Chatbots are one use of conversational AI systems. Chatbots have obviously become very popular recently with major announcements from Facebook, Microsoft, Amazon and others.

Chatbots as one example of conversational AI
Kasisto isn’t really a chatbot company — we do conversational AI. The difference is that we build virtual assistants that will create conversational experiences that span the spectrum of customer touch points within the enterprise — from Alexa to messaging platforms like Facebook Messenger and WeChat to  mobile applications, websites and call centers. Kasisto builds the true omnichannel experience.

Banks use our technology to interact with their customers across channels and use cases — from retail and business banking to wealth management and even interacting with employees. Think of KAI [Kasisto’s product] as a banking brain that knows how to interact with customers and employees using artificial technology.

DBS and Kasisto: A case study
Let’s take retail banking as an example. Singapore-based DBS is one of the largest banks in Southeast Asia. A few years ago the bank decided to expand its footprint in Asia and it chose a digital-only strategy. The launched a mobile-only bank, digibank.

There are two key characteristics that really set it apart: it enables 90-second paperless account opening and it uses virtual assistants built on KAI that manages all customer interactions.

If you’re a DBS customer in India, Indonesia or Singapore, you can use your mobile app to do thinks like open an additional savings account, understand your spending on restaurants on a recent trip, and determine whether you’ll need to pay ATM fees when you’re traveling overseas. And you do all of this simply by having a conversation with the bank as if you had a virtual banker or teller residing in the app.

In India, digibank acquired 1.5 million customers in a year and a half after launching. There are no branches and no call centers. KAI is there to help digibank acquire and support customers. Today, KAI handles 82 percent of all customer interactions with DBS customers without any human intervention. Put differently, only 18 percent of KAI sessions end up with live chat agents. That really changes the economics of digital banking. DBS is able to run its bank at one-fifth the cost of a traditional bank.

The future of AI and opening up new opportunities
Why use AI and not human agents? Well, you can train AI on an unprecedented number of use cases. You can teach KAI or other virtual assistant a sheer number of things that would be impossible for a human to keep in his or her head.

There’s another aspect of AI that’s really interesting: consumers interact differently with AI than they do with other humans and that creates new users experiences. For example, you can ask KAI how much you spend on Uber or on eating out. This isn’t something you’d consider calling your bank about. You probably wouldn’t call Bank of America to find out how much you spent on wine last month. These types of new experiences can generate customer surprise and delight with their bank, deepening customer relationships.

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Banks see artificial intelligence in their future, but are slow to invest in it

Most financial firms believe artificial intelligence will have a huge impact on their own business and the overall industry in the next few years. If that’s so, investment and integration need to begin fairly soon. And the reality is, most banks are far from doing so.

Below are five charts that show the current state of AI in U.S. financial institutions; what banks’ AI plans are; and why they’ve been slow to move on them, despite the hype.

No plans
In a Celent survey of banks, the only technologies being used are fraud analysis and risk detection and natural language processing, by 14 percent and 5 percent of respondents, respectively. For all the hype around chatbots, none of the banks have fully deployed one, although 9 percent are running pilots. Celent doesn’t identify which banks participated in the survey but it’s safe to assume those piloting chatbots are among the largest, like Bank of America’s erica as well as Capital One and Chase, who have also reported bot pilots.

Some banks are running pilots with RPA and Natural Language Generation, but most are still just considering different technologies or haven’t made any plans.

Few are investing in AI
Just 9 percent of banks, presumably the same ones that are running pilots and seeing positive results, plan to invest more than 50 percent in AI in the next year than they did in the last one. Another 23 percent of banks indicated they planned to invest more than 25 percent more. Thirty-two percent indicated their investment levels would remain the same, but another 32 percent indicated they’re not investing at all.

“A lot felt it was expensive, a lot of people have a general lack of understanding,” said Stephen Greer, an analyst at Celent and co-author of the AI report. “There’s such a perception and understanding that it will have an impact, but there’s a limited number of institutions doing a lot with it.”

Five percent of banks said they planned to invest less than 25 percent in AI in the coming year than they did in the last.

Expense and security
Fifty-eight percent of banks indicated that AI might be too expensive to implement and deploy. The same percentage listed security as a concern — although 13 percent indicated they weren’t concerned about security at all.

Most striking for Greer was the high percentage (31 percent) of respondents who said ethical considerations weren’t at all a concern. Eliminating bias, abusing AI and displacing people’s jobs all fall under the ethics category — and they’re some of the largest sources of uncertainty for most consumers who don’t work with the technology hands on.

Where does it go?
According to PwC, most firms in banking, capital markets and wealth management say they’ll rely more on human judgment than machine algorithms to inform their biggest decisions.

Further, according to Celent, banks aren’t very optimistic about AI’s applications for their customers. They seem most excited about using it for fraud detection — particularly as fraudulent activity gets more sophisticated.

“I don’t get the sense most of the industry was acutely aware of how [AI] will happen or why it will evolve,” Greer said.

For some AI technologies, it takes time to digest the customer data they need and to have humans teach them how to react appropriately. Over time, the hope is that AI can reduce banks’ cost of serving customers and improve customer interactions. It’s hard to see that in these early stages, when AI is still in the learning stage. However, banks showed they don’t expect AI to handle more than 30 percent of their front or back end operations.

 

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How Kasisto avoids the financial chatbot fatigue

There’s an entire financial chatbot ecosystem emerging in the artificial intelligence space.

Chatbots, designed to simulate conversations with human users, have existed for a long time. Now, with so many millennial consumers who prefer digital interactions for accessing and managing their financial services, chatbot popularity has erupted.

Kasisto, which calls its MyKAI chatbot a “Siri for financial services,” is one of the most well-funded, having just closed a $9.2 million Series A round in January. It gives consumers a conversational platform over which they can ask about their bank account activity and allows them to link their Venmo or Facebook Messenger accounts to make payments initiated through KAI. It also works with banks to allow them to create their own conversational experiences with customers. The company was part of the inaugural class of Wells Fargo’s accelerator program and was originally a spin-off venture of SRI International. Incidentally, Siri was too before it was acquired by Apple in 2010.

Digiday spoke with Dror Oren, cofounder and VP of product at Kasisto, about how it separates itself from chatbot hype, how it’s adapted to technology changes in banking, the company’s long and short term goals.

How do you avoid bot fatigue?
The way we view ourselves isn’t as a bot company. We’re a conversational AI platform. We enable conversations on different channels, which can be chatbots on messaging but can also be conversations on mobile applications, on the web, through Alexa – we’re across channels.

The second difference is that we’re a platform. Yes, we have our own MyKAI bot but we really enable banks to build their own bots.

The third thing is, not all bots are created equal. There are dumb bots and smart bots. It’s easy to build a bot that demos well, it’s hard to build a bot that answers the questions you want and can deploy in an enterprise environment where you can train, retrain, scale, add more capabilities and have it run in a banking environment.

How has Kasisto’s vision of itself changed since 2013 given how much fintech and banking have changed in that time?
The value proposition around conversation for finance and redefining the way people interact with banks has been a consistent focus from the very beginning. When we were raising money, no one believed conversation would be the modality people interact with. Now we don’t need to convince people about conversation, but the conversation is a little different. Voice had higher appeal back in the day; we see it less today. Now the focus seems to be more on written conversation — messaging and chat interactions.

We’re also seeing an extension of use cases. It used to be about proving the ROI — how you justify the deployment of these systems. Now it’s about customer support messaging and banks are looking at more opportunities to extend their reach.

Won’t voice make a comeback?
We don’t see much demand coming from the market. One exception would be Alexa and the success of Echo. We’re piloting an integration with them so we know there are interesting use cases there. I don’t think standalone speech solutions add much value to your bank applications but adding a conversational element to devices that already do speech is probably something we’ll see develop.

It seems like every fintech company is touting an AI component now.
Yes, AI has become a buzzword. It’s everywhere but not clear where and in what contexts. We’re using AI in training our models, training a system that doesn’t already know the banking system. We’re also using it in run time; you ask a question to the bot and the system decides what the right answer is, what the “intent” is, what it is you’re really trying to do when you ask “how much have I spent on Uber between March and August?”

What is the future of MyKAI?
In the next couple years, we will see live deployments with banks solving simple but real problems around customer support, helping you understand your transactions, personal finance management; but also actual actions, like making payments, asking about reducing a fee, buying overdraft protection. Every bank and company will find their own valuable use cases and will double down on those. We’re lucky to be live early in the process because we already have a lot of data to help us look at what people are asking for and doubling down on those things.

How much does customer trust or distrust play into the evolution of the chatbot ecosystem?
There’s no real reason when you apply for loan or mortgage you’d wouldn’t want to with a bot. It’s only a matter of filling out a bunch of forms but right now people don’t do it. As time goes by we’ll see trust move to virtual conversations and away from live conversations.

And channel maturation?
We think a conversation can be cross-channel and blur the lines of how you think of the channel. If a user is going to a bank’s website on their mobile is that the mobile channel or the web? At the end of the day the consumers don’t care [about the channel]. They want a consistent experience across all the touchpoints.