Presented by DefinedCrowd
In fintech, customer experience is key to customer retention. Access this free VB Live event to learn how companies like MasterCard and Capital One implement AI strategies that transform how customer experience is done.
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“The democratization of AI technology means that it’s no longer confined to the university lab or the big corporate R&D lab, but available to individual developers and startups that can get going on this journey very easily and quickly,” says Dr. Steve Flinter, VP of artificial intelligence and machine learning, Mastercard Lab.
This is particularly relevant for the fintech world, where AI is offering increasingly sophisticated solutions in four specific areas: security, customer experience, new interfaces, and general internal process improvement. All these issues tie back to customer service.
“AI is available 24/7, it keeps learning, and it spots opportunities for efficiency — often before you even know you have a problem,” says Dr. Daniela Braga, founder and CEO at DefinedCrowd. “Banking is all about knowing how the future is going to play out. The data for these kinds of predictions is a playground for AI.”
In the financial world, AI is already solving problems, she says, from partial replacement of call center agents, to automation of back office tasks, to behavioral analytics, understanding customer sentiment, acting as financial advisors, doing query routing, fraud detection, ID verification, and even in the transcription of calls.
For instance, because your customer’s safety is paramount, AI can be used to deliver a better customer experience. AI and machine learning can detect who you are based on your behavior. When you identify yourself on a device or log in to a merchant’s website, even if your password is compromised, your identity can be verified. On the other hand, an intruder can be identified because they use their device differently.
Conversational AI is also revolutionizing how consumers interact with banks, merchants, retailers, and more on a day-to-day basis. It’s been a trend over the past few years, but the global pandemic has accelerated the development and adoption of conversational AI products. Self-service banking is the new normal, and every bank has to be able to deliver that as an experience to their consumers. As people have moved online and to mobile, they expect to be able to bank 24/7.
“When you’re automating call centers, inserting a digital assistant into the first line of customer care means you can answer 100% of calls versus missing 20 to 50%, which is the current state of care in a lot of services — and deliver much better customer experience,” Braga says. “You answer 80% of the most frequently asked questions without having a person have to search for content. Even better, you’re able to save 30% of costs and recover your investment in three years.”
“AI has the potential to achieve true personalization, something that we’ve been talking about for years,” says Carla Saavedra Kochalski, director of conversational AI and messaging products at Capital One. “Not only personalization based on the outcomes that we, as a business, would want you to do, but also serving our customers.”
Inclusivity and building an AI that serves everyone is of utmost importance, Saavedra Kochalski says. Capital One’s focus is on personalization to the point where they can understand who the customer is and what their needs are, anticipating and predicting what that customer might need, so that they have better outcomes in the future.
They use the many ways customers speak and express themselves to improve how their digital assistant, Eno, answers a question, but also to improve their other digital experiences as well.
“One of the benefits of a conversational AI is the pure data collection, and how expressive customers are with their need at that moment,” she adds. “We finally have a mechanism to understand what is in the customer’s head.”
About 80% of the time, customers are asking for more informational, frequently asked questions, rather than complex, multi-turn questions. Answering those more complex questions requires context and additional machine learning components beyond a basic FAQ NLP. To develop Eno, Capital One looked at the top things that customers call into an agent for, from making a payment to confirming whether a transaction might have been fraudulent.
As companies consider implementing AI, there are four key areas to focus on, says Flinter. The first is developing internal skills. This includes both hiring people with the right skill sets and experience, but also upskilling the teams already in place to get them ready for the broad and wide-scale adoption of AI technologies.
“We’re moving toward a place where every individual software development team may well need to have their own skills,” Flinter says. “It’s not going to be centered on centers of excellence or specialty pools within an organization.”
Organizations will also have to consider whether to choose cloud or on-premise deployment. For organizations in the banking, finance, and payments world, this consideration can pose real challenges around issues of privacy and data governance and so on.
Closely related to that consideration, companies also need to think about how to make their data available to machine learning and AI models. In large organizations particularly, data was designed years ago for broad scale data processing, but not necessarily for AI and machine learning, which means rethinking data access.
And then finally, companies need to consider model governance — managing AI models once they go into production. How do you ensure that they don’t have any unwanted biases? How do you keep them trained, refreshed, and so on? As companies move into productionizing AI, bringing it out of the labs and into the commercial world, it’s increasingly clear that this new skill is something many organizations need to turn their focus toward.
For a deep dive into where conversational AI is now — and where it’s going, and the nitty gritty on leveraging AI and voice technologies to dramatically improve customer service, boost efficiency, and improve your bottom line, access this event now.
Takeaways:
- Understand the different types of AI initiatives a company can launch to improve CX based on NLP and voice technologies
- Know how to develop those AI initiatives and the role of data on training AI/ML models
- Get to know a case study from fintech companies Mastercard and Capital One
Speakers:
- Dr. Steve Flinter, VP of Artificial Intelligence & Machine Learning, Mastercard Lab
- Carla Saavedra Kochalski, Director of Conversational AI & Messaging Products, Capital One
- Dr. Daniela Braga, Founder & CEO, DefinedCrowd
- Hari Sivaraman, Head of AI Content Strategy, VentureBeat (moderator)
Author: VB Staff
Source: Venturebeat
