AI-powered personalization at scale: The key to increasing fintech customer engagement and revenue

Presented by Envestnet

Personalization at scale is a key strategy for fintechs to deliver hyper-relevant products and services. Learn how top fintechs are delighting customers and building strong relationships with AI-enabled platforms and data sources in this VB Spotlight.

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“There is a direct correlation between customers loving your products and revenue,” said Bala Chandrasekharan, VP of product management at Chime. “Customers are much more engaged and likely to recommend your product to others. Referrals are an incredibly powerful viral marketing channel compared to paid marketing.”

And to do that requires true personalization. Chandrasekharan, David Goodgame, COO of Tricolor Auto Group and Eric Jamison, Head of Product – Banking and Technology at Envestnet talked about how personalization at scale is giving fintechs a greater competitive edge than ever, and how AI and analytics are changing the game, during a recent VB Spotlight Event.

The case for personalization

For Tricolor Auto Group, a used car dealer and direct lender, personalization means digging into the deep desire behind a customer’s request.

“All of our efforts here, when it comes to marketing and even the inventory we choose to put on our sales lots, are focused on what we call jobs to be done,” Goodgame said. “We looked at our entire business and said, ‘When a customer comes to us, what are they looking for us to offer? How can we, in our marketing efforts, in our dealings with them, in our customer service centers, make sure that we take grab it?'”

A “job to be done” could be a customer unsure of their credit, hoping for an American Dream lifestyle or tackling big projects – and personalized ads sell this value proposition in the form of a car or loan.

Achieving this requires blending the consumer relationship with the use case, which is where good data is critical, Jamison said. As a B2B2C service provider, Envestnet comes in when a lender may need to fully understand a loan applicant outside of their credit report – or if they don’t have one. This data may include cash flow information, such as income and expenses from a bank or other supplier.

“It really helps to personalize that application for that consumer, to help that provider make a more informed decision and to help connect the dots that might not appear in a traditional way for that consumer,” he explained. “It’s bringing together our ability to do something about consumer needs and making sure those things are aligned. That will give the best result.”

How AI and machine learning are changing the game

“Our AI risk model is the secret sauce behind our company,” Goodman said. “What we believe is that if our customers go somewhere else in America, they’re all thrown into a bucket. The one bucket is a very predatory set of terms for that customer. It will be the government’s maximum interest rate. It’s going to be an inferior product. Affordability for that customer is never going to be part of the conversation.”

According to Goodman, approximately 90% of applications the company does not receive information from any of the credit bureaus. But the vast amount of data they collect, from a wide range of venues, can identify what he called a more reliable scoring system than a FICO score, so they’re able to offer low rates to someone with no credit data.

“Our risk model – it enables us to sell cars that have very low losses,” he explained. “We are able to lower our rates, which attracts more borrowers. We’re doing more of this, and the flywheel effect is starting to happen, because as we’re able to get more data and get more applicants, our model gets smarter. It gets tighter. We can reduce the terms even more. We take more and more risk out of the equation, so we can offer better terms. As we offer better terms, we get more customers. That flywheel effect becomes real.”

And in doing so, they help lift up an often-overlooked demographic so they can begin to establish a financial history and build credit.

Taking all the application data also helps them move the risk model higher up the funnel – and the higher up the funnel they can do that, the more personalized marketing can be. If a customer comes through a certain channel, their interests, needs and background can be identified to ensure that the content they receive is relevant to them, thereby increasing the conversion rate because they feel that their needs – for a particular financing style, price range, etc. – is seen and met.

This also applies to Chime, which aims to provide accessible financial services to Americans who may have been denied traditional banking services.

“In that world, when you don’t have a lot of explicit public information available, AI and ML play a big role,” Chandrasekharan said.

For example, it is important to distinguish the negative marks on a customer’s records between irresponsible behavior and someone who has encountered unfortunate circumstances. The question becomes how to read a customer’s behavior pattern – how they have used the platform and products in the past, what a negative event looks like, and what value the customer can add.

“That’s where AI and ML play a big role in trying to understand how we can separate the good from the bad,” he said. “What actually makes it possible is the flywheel effect discussed earlier. You can provide an excellent, wonderful member experience in that case when you know they’re a good customer. That can make a big difference. These are the moments that matter to a customer . When you’re able to use AI and ML to get it right, it ends up turning into a delightful experience, which means they’re likely to be loyal customers for a long time. They’re likely to refer your products to others.”

The power of data comes from identifying patterns, Jamison said, which requires as large a pool of data as possible. Envestnet works from a set of about 40 million consumers, and their regular transactional activity allows the company’s data scientists to identify key behavioral similarities, he said.

It could be identifying ways to trade your own financial portfolio to save money or helping a financial advisor scale by bringing wealth management advice to the masses. It helps eliminate the danger of using a one-size-fits-all approach, which means missing out on the majority of your customers.

“We’re all individuals and we’re all unique, but our patterns usually match someone else’s,” Jamison said. “We can start to adjust those intersections to help identify next-best actions. That can help the consumer achieve a better financial outcome. Our platform and the AI ​​and machine learning they use help consumers throughout the lifecycle. We can take apply the right solution at the right time so the customer can help their customers. That’s really the power of data, helping to understand consumers across these broad segments in a very targeted and specific way.”

To learn more about driving nuanced hyper-personalization at scale, overcoming data and privacy challenges and more, don’t miss this VB Spotlight.

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Agenda

• How FinTechs are using personalization at scale to gain a competitive edge
• Various AI-enabled technologies to securely collect, enrich and analyze financial data
• How advanced analytics and transactional data can deliver valuable customer insights
• Ways to identify opportunities for customer acquisition, cross-selling and up-selling
• How to create personal experiences that are relevant and emotionally “sticky”

Lecturers

• David Goodgame, COO, Tricolor
• Bala Chandrasekharan, VP of Product Management, Chime
• Eric Jamison, Head of D&A Product — Tech & Bank Product & Design, Envestnet
• Mark Kolakowski, freelance writer and editor; Lecturer; Former Financial Services Professional (moderator)