The Challenge
Before adopting Atlog, Uown faced a challenge common across collections teams: scaling outbound collections operations without continuously increasing headcount and operational costs.
Like many consumer finance companies, Uown had already transitioned from onshore to offshore call centers to reduce labor costs. But even offshore operations still relied heavily on human agents to power outbound dialing campaigns.
The AI changes that math because it can call as many people as you want it to call simultaneously.
Uown began exploring AI collections software to:
- Reduce collections costs
- Scale outbound collections calls
- Improve first payment default rates
- Increase operational efficiency
- Automate repetitive collections outreach
- Maintain compliance and customer experience
Why Established AI Vendors Fell Short
Before selecting Atlog, Uown evaluated several larger AI collections vendors.
Many platforms looked polished during demos, with advanced dashboards and strong ROI presentations. But most vendors struggled with one critical issue: they treated lease-to-own collections like traditional consumer finance.
For Uown, that created immediate concerns around compliance, customer communication, and operational fit.
Lease to own has some key differences.
Several vendors continued using incorrect lending terminology and generalized collections workflows that did not align with Uown's servicing model. That lack of flexibility became a major differentiator.
The Solution
Built specifically around Uown's business
What stood out immediately was Atlog's willingness to adapt.
Instead of forcing a generic AI collections workflow onto the business, Atlog customized the platform around Uown's operational needs, including:
- Lease-to-own compliant language
- Payment collection workflows
- Settlement campaigns
- Welcome calls
- First-payment delinquency outreach
You're dealing with someone who's decided to make the space that we operate in important to their business.
Faster deployment and iteration
Compared to enterprise vendors with long onboarding cycles, Atlog moved quickly.
Uown was able to launch pilots, test workflows, and iterate in real time instead of waiting months for implementation.
Speed.
Atlog's responsiveness became one of the company's biggest differentiators.
We would say, "Hey, it needs to do this," and whether it was two hours or forty-eight hours later, you guys got it to do at least the majority of that.
The Results
Lower collections costs
AI automation reduced the need to scale collections headcount linearly with account volume while dramatically increasing outbound calling capacity.
Improved first payment performance
After implementing AI-powered welcome calls and collections workflows, Uown saw measurable improvements in first payment default performance.
Our first payment default rate since February is the best it's ever been.
Better operational consistency
AI also improved consistency across servicing workflows. Unlike human agents, the AI followed collections rules and payment policies exactly as configured.
If you have good rules in your business, it understands them flawlessly.
What Comes Next
Uown views AI as a long-term operational advantage—not just for collections calls, but eventually for business intelligence, analytics, and operational monitoring.
The company continues expanding its use of AI across outbound collections, payment reminders, settlement handling, and operational analytics.
Uown did not choose Atlog because it was the biggest AI vendor. They chose Atlog because the platform moved faster, adapted quicker, and worked more closely with their team than larger competitors—for lease-to-own expertise, continuous iteration, and real-world collections performance.