By W.B. King
Roughly 150 credit union leaders and fintech innovators virtually met at CU 2.0’s Brainstorm Event to discuss topics shaping the current and future state of the industry.
“This is a two-way event – not a bunch of talking heads,” said CU 2.0 Founder Kirk Drake. “This is designed to be an opportunity for connection and collaboration with key credit union and fintech executives.”
The event is an offshoot of CU 2.0 Fintech Mastermind, a group of nearly 100 credit union and fintech leaders who meet on a monthly basis “to tackle the financial industry’s biggest challenges,” noted Drake.
Among topics discussed during the July 14, 2021 half-day event were: Analytics; AI in Finance; Blockchain; The Rise of CUSOs; Marketing to Millennials and Gen Z; and Digital CEO: The Tools Tomorrow’s CEO Needs Today.
The Ongoing Blockchain Discussion
While the concept of blockchain isn’t new and has been proven useful in certain scenarios, the platform is only slowly being embraced by the credit union industry. COVID-19, however, may prove to be the game changer for wider adoption, said speaker John Best, CEO of Best Innovation Group (BIG).
“The number one use case is identity. That is still growing quickly and making a lot of progress. The pandemic has pushed that in a lot of countries because they are watching who has been vaccinated and who hasn’t,” said Best. “Logistically the burden of proof is there. It’s a life or death sort of thing so you need something that is rock-solid that can’t be tampered with.”
The medical record use-case, Best said, is an inverse movement toward decentralized data. Among questions he fielded by session attendees was how credit unions can get involved in the blockchain movement.
“In blockchain in general, the best opportunity I have seen so far is CULedger,” said Best.
In 2019, BIG, Connect Financial Software Solutions and CULedge, which was rebranded in 2021 as Bonifii, announced a partnership to bring distributed ledger technology to BIG’s FIVE Voice Banking Platform.
“CULedger, Connect FSS and Best Innovation Group have partnered to provide a revolutionary new security solution to the voice banking channel,” noted CULedger’s president and CEO, John Ainsworth in an announcement. “Through our partnership, FIVE’s users are among the first to leverage CULedger’s member-owned, self-sovereign identity capabilities.”
For credit union executives looking for blockchain success stories, Best said 4Front Credit Union is a worthwhile use-case example to study. The Michigan -based credit union engaged CULedger for “creative ways to leverage blockchain designs to better serve members and MemberPass’ Symitar integration and omnichannel approach,” Zach Eychaner, senior vice president of Strategic Innovation for 4Front Credit Union, said in an announcement late last year.
“MemberPass has already tremendously improved our digital capabilities by offering a seamless identity verification without causing our members any friction,” added Eychaner. “CULedger is truly invested in the success of this application and we look forward to a prosperous partnership.”
Since embracing the solution, Best said 4Front Credit Union is attracting “more and more people” because members are realizing how “secure and simple” it is to use. And he added that the platform also addresses back office issues, such as streamlining call center operations.
“I’m like everyone else. I like information and data. And I have already proven a lot to myself with the data already collected from the 15 credit unions we work with,” said Best. “They are all seeing the same thing — the trend is out there. You can’t hide from it – it’s happening.”
The AI-Credit Union Proposition
As the day progressed, attendees were funneled into breakout rooms between sessions to talk shop as well as answer group discussion topics, such as favorite adult beverages (answers ranged from sour beer, to Resistance Rosé wine to margaritas) and what attendees looked forward to celebrating this time next year.
While some said they want to celebrate a return to "normal" by traveling and attending in-person conferences, Nusenda Credit Union’s Senior Vice President of Business Services Chris Clepper said he hopes his team’s recent customer relationship management (CRM) platform rollout will be viewed as a success.
Those attending the "AI in Finance" session were next immersed in a fluid discussion with a number of AI fintech executives, including FlexPays’ CEO Darryl Hicks who likened AI adoption to the rise of the smartphone.
“Apple and Samsung would not be the companies there are today if they weren’t about smartphones and the same thing is happening with AI,” said Hicks. The Montreal -based company uses advanced machine learning models to intelligently determine the best way to recover declined transactions.
“Even if AI is not core to your application or business, if you’re not using it to solve the right problems, you will be out-competed and risk becoming irrelevant,” said Hicks. “What we are seeing in the financial services industry is that fraud losses are increasing. AI can find data from disparate sources. Merchants will always know more about a transaction than an issuer will. And an issuer will always know more about the customer that the merchant will. We need to bring that data together in real-time.”
Finn AI’s Co-Founder and CEO Jake Tyler said credit unions have to decipher between the “myth, magic and reality” of AI. The Vancouver, British Columbia –based company builds conversational AI solutions to improve financial institution’s digital customer experience on mobile, online and call center channels.
“At the end of the day you don’t want to buy AI, you want to buy something that solves your problems. The way AI works is volume of data and quality of data,” said Tyler adding that most AI developers spend about 80% of their time with data preparation and analysis that result in tailored algorithms.
For credit unions looking for an AI vendor, Tyler said executives should be seeking a pre-packaged solution that includes training. He added that AI solutions are a result of “technical, laborious” work that should be backed by data, a proof of concept and a security paradigm directly related to the client.
“If you have to bring the data and train yourself, you’re doing pretty much all the work,” said Tyler. “Is it magic and alchemy you’re buying or a [AI] product that actually works?”
Building on the notion of vetting AI vendors, DocFox’s CEO Ryan Canin said credit unions, in part, should drive the conversation on what AI tools the vendor is developing and how these solutions will continue to benefit membership. The Miami, Fla. –based SaaS business offers a digital tool that automates BSA for business account opening and ongoing due diligence for high risk and complex accounts.
“You have to negotiate with the vendor and turn the tables,” said Canin. Credit unions, he added, should query vendors as to what "success means to them" and how the vendor can "better serve"the credit union’s membership.
“You should ask them what they will pursue over the next 12 to 18 months,” he said, noting that with communication and idea sharing a successful partnership between a credit union and vendor can be achieved. “A lot of this is quite new and it’s very important to align expectations.”
Scienaptic AI's Executive Vice President of Client Impact Eric Steinhoff said that one key to success in AI is the ability to pool data, especially for smaller organizations. The New York –based company offers an AI-powered credit underwriting decisioning solution.
“With the smaller credit unions we are trying to pool data so we are talking, for example, to regional credit union groups where we can build a customized solution for those credit unions dealing with the same geography and similar member profiles,” said Steinhoff.
This approach can work, Steinhoff noted, because regional credit unions with lower respective memberships can share data points that will result in an AI solution that ultimately benefits all participating credit unions.
FlexPay’s Hicks agreed and explained that his team will build an AI model across the general population that, on average, will correlate to 60% of a client's needs. Then the team will develop a cohort of “look-alike” solutions that mirrors that client’s peer group, which might increase the beneficial impact to roughly 80%.
“The longer you are working with a client, the more data you get and then you can start to solve problems and build custom models based on their data and then layer in the general population model in one case, the peer model in another case and its own model. When combined altogether you end up with best-in-breed practices,” said Hicks.
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