The interview takes place at the Movemint Symposium, with John-Ashley Paul and A.K. Patel from Tyfone discussing the company’s lending strategy and partnership with Movemint. They say they are at the conference to explain the value Tyfone brings to Movemint’s client base and to learn from the market.

They explain that Tyfone has created a digital lending group focused on solving the problem of too many separate vendors across the lending lifecycle. Their goal is to offer a single platform that covers origination, servicing, payments, collections, re-origination, and new account opening, including checking and depository accounts.

The guests describe their product as an extended LOS, or more broadly a platform, rather than a single lending system. They say it can replace an average of four vendors and reduce what they call a “Frankenstack” of tools that have been layered together for account opening, lending, servicing, credit pulls, and other data sources.

They say the platform speeds up account opening by 90% to 95%. For mortgages, they cite closing times that are 60% to 70% faster, and for consumer loans they say funding can happen the same day or the next day.

The discussion turns to credit union competitiveness. They say credit unions can do more with less, improve profitability on products that may previously have lost money, and even generate revenue from servicing products. They also argue that credit unions and community banks are losing ground to fintechs because of slower processes and too many systems.

They say their platform reduces integration headaches, lowers vendor management and due diligence burdens, and cuts down on the number of contracts clients need. They also discuss the idea that digital banking is becoming more central to the system, with future interfaces becoming more conversational and easier to use.

Near the end, they describe Tyfone's work with Movemint as a natural integration, where Movemint drives members to apply and Tyfone's platform helps convert them quickly.

They conclude that credit unions need to combine their community relationships with modern technology and AI to stay relevant, compete with fintechs, and keep pace with changing member expectations.

Mitch Rutledge from Vertice AI discusses his visit to the Movemint Symposium, where he is participating alongside the Movemint team. He says the company partnered with Movemint to present a joint client case study and to explain how the two organizations work together. He identifies the shared client as GHS Credit Union in Binghamton, New York, and says the presentation will focus on using Vertice to identify indirect conversion opportunities.

He explains that the work involves identifying target audiences, then using Movemint to match those audiences with offers, while another partner, AviaryAI, handles execution through AI-driven outbound calling and texting.

Rutledge says Vertice and Movemint are not overlapping in function. He describes Vertice as focused on propensity scoring and identifying member needs and wants, while Movemint helps present offers and products based on that insight. He says the goal is to provide deeper behavioral understanding.

He also mentions an upcoming session where he will discuss the broader Vertice story and the partnership with Movemint. He says Vertice recently won a Best in Show award at Finovate for a new module called Vertice Optimize, which he describes as part of the company’s effort to help credit unions improve marketing efficiency and member engagement.

Looking ahead, he says Vertice plans to attend the Jack Henry event and the Filene Conference in December.

On a personal note, he says he is a college football fan, supports Georgia Tech, and lives in Athens, Georgia, where he also follows the University of Georgia when Georgia Tech is having a difficult season.

Asked about the future of technology, Rutledge says he is optimistic about AI. He compares AI to chemistry, saying it can be used for beneficial purposes but also carries risks. He argues that financial institutions are already focused on risk management and that AI can help them compete by using their existing data more effectively.

Aditya describes Precision CUSO as a CUSO focused on helping credit unions make “precise” decisions across the lending lifecycle. He says the goal is to support best-in-class decisioning so credit unions can compete with fintechs and banks. He explains that the platform sits beside the loan origination system, rather than replacing it.

Precision CUSO is presented as an analytical decisioning environment where credit unions test and refine decision logic using data from the LOS and core banking systems. He uses underwriting as an example, mentioning risk scores, LTV, FICO bands, DTI, policy cutoffs, and different credit policies for different products.

He says the platform is used both before and after loans are funded. Up front, it helps determine decision logic and credit strategy. After funding, it supports monitoring of portfolio performance, expected loss, and recalibration of decisions based on results.

On automation, Aditya says the company focuses on end-to-end decision automation, not just scoring. He notes that third-party scores are inputs, but that multiple steps still require decision logic and sometimes manual review.

Precision CUSO is described as the layer that helps automate the full decision process. He says a credit union CLO would see near real-time dashboards showing acquisition metrics, auto-approvals, auto-declines, roll rates, and early portfolio performance. He contrasts that with the manual process of gathering and analyzing data across teams.

Aditya also says the company fits with Movemint through prospecting and wallet-share growth, including AI-driven propensity models and messaging. He says the CUSO was formed by Teachers Federal Credit Union and Corridor Platforms, and that Teachers has used the system for more than two years.

He adds that credit unions should own and control their decisioning transparently, rather than relying on a black box. He says Precision CUSO connects mainly to data lakes, can use core banking data when needed, and delivers decision logic through API-enabled artifacts.