With Observe.AI, Stellantis Financial Services analyzes 100% of customer interactions, increases collection rates by 9.7 percentage points, and lifts promise-to-pay rates by over 5 points.
As the financing arm of the automaker behind iconic brands such as Chrysler, Jeep, and Dodge, Stellantis Financial Services (SFS) plays an important role in helping customers manage their car loans. It provides full-service auto financing and supports everything from customer care and payment collections to legal and recovery services.
The company has rapidly expanded in recent years. Formerly known as First Investors Financial Services, it was acquired by automaker Stellantis in 2021 and relaunched as a dedicated US financing arm.
That growth brought new demands to the company’s contact center operations: larger teams, more agents, and the challenge of ensuring every customer interaction adhered to internal policies and brand standards. It became clear that legacy QA processes weren’t enough to support this growth.
“Controls became extremely important as we transitioned from First Investors Financial Services and as we scaled, to make sure everyone was doing things the right way and following our policies and procedures,” says the Director of Collections, Lamar Nevels, who oversees all support functions in collections. “We began asking, ‘Are we operating as efficiently as we could? Are we staying current? Where can we improve?’”
Like other auto lenders, SFS also had to deal with growing volumes of calls, stricter compliance requirements, and sensitive customer conversations, especially around repossession and recovery of vehicles.
“We needed better visibility into how our agents were handling these calls and to be able to respond quickly to what they found,” adds Nevels.
With a time-consuming QA process that was unable to keep up with the volume and complexity of its operations, SFS implemented Post-interaction AI from Observe.AI to analyze customer interactions and understand what’s happening in these conversations. Previously, Nevels’ team could only review 10 to 20 calls per agent each month, leaving major gaps in visibility.
“You need a product like Post-interaction AI to handle QA at scale,” says Nevels. “As you grow, it’s hard to match the resources needed to evaluate all calls. The Auto QA tool allows us to process 100% of our customer interactions, so we can look for specific things. Are agents sticking to scripts? Are we having the right conversations with our customers based on the status of their account?”
This level of visibility has helped the team respond quickly to risks, ensure compliance, and prioritize critical conversations—such as those involving customers at risk of repossession. Following an analysis in July 2024, the team noticed a slight uptick in silence and average handle time (AHT) among agents.
However, with targeted coaching and the use of in-platform alerts to discourage excessive silence and minimize work avoidance, SFS significantly improved these metrics. Since October 2024, silence levels have steadily declined and have remained below 2% into 2025. Meanwhile, with key call objectives now automatically evaluated, QA teams have been able to reassign resources toward more strategic initiatives.
“Before, we spent a lot of time just trying to catch issues,” Nevels adds. “Now with Auto QA, we’re spending more time addressing them by coaching our agents, identifying patterns, and supporting long-term improvements.”
As SFS grew, it added new teams and collaborated with international partners, making it more challenging to maintain consistent customer experiences. The stakes were particularly high for payments collections, according to Nevels.
“You have to train more, monitor more, and really listen to what’s happening on the phones. That’s how you make sure everyone’s moving in the right direction.”
Before using Observe.AI, his team had limited visibility into agents’ performance and how they individually handled collections or compliance. Now, access to real-time and post-call data enables more tailored coaching and clearer performance tracking.
In a recent performance review, the Observe.AI team helped SFS identify top-performing collection agents and correlate specific behaviors to their success. Armed with these insights, SFS launched a coaching strategy modeled on those behaviors. It focuses on key metrics that would drive the greatest improvement among lower-performing agents. This targeted approach led to measurable gains in collection rates and strengthened overall agent performance across teams.
To better understand where to focus their coaching efforts, SFS partnered with Observe.AI to review collections calls over four months, grouping agents into performance quartiles based on how often they successfully collected payments.
A recent data review revealed that the overall collection rate had improved by 9.7% over the last eight months to reach 67.3%. This improvement was driven in part by targeted coaching efforts focused on the lowest-performing agents (quartile 4), who saw a 72% increase in collection rates following data-driven coaching initiatives. That improvement alone contributed to an estimated $8.7 million in recovered payments. Across all quartiles, total net recovered payments reached $6.2 million.
As Nevels explains, “There were certain key drivers that were the difference between those who were performing at a high level and those who weren’t. Some of that was taking ownership of the phone call, probing, and asking the right questions.”
As managers adopted the new coaching strategy, they saw immediate impact: the rate for securing customer promises to pay improved by more than 5 percentage points. Given the company handles thousands of calls, even a small percentage lift translates into significant financial returns.
Importantly, this initiative didn’t just establish a coaching playbook for collections—it laid the foundation for a broader, outcome-driven performance framework. SFS can now apply the same data-backed approach to any “north star” metric moving forward, empowering the organization to align agent performance with business outcomes, not just activity-based quotas. This development moves SFS closer to becoming a true profit center, where coaching and operations are tightly linked to measurable business impact.
“We’ve improved in a particular area that’s key for the business—our ability to secure promises after contacting the customer,” says Nevels. “And now, we have a system we can apply across other areas too.”
Likewise, managers can now see whether agents are negotiating effectively, offering the right solutions, and preserving relationships with customers. As Nevels explains, it often comes down to how the call starts, whether the agent asks for the total amount due, actively listens, and suggests helpful options.
Using transcripts and insights from Observe.AI, managers can coach on missed opportunities, such as when a customer qualified for a program but wasn’t informed.
“We can listen to those calls and coach the agent: ‘You did your best to collect money, but this customer was eligible for a program. Let’s talk through how you could have handled that differently,’ ” shares Nevels.
He adds that the goal is to help customers get back on track. “Just because someone had a tough time doesn’t mean they’re a bad person. It’s about helping them navigate their situation, and once they’ve satisfied their loan, you want them to come back and be a customer again.”
The visibility and insights provided by Observe.AI don’t just help managers. They also give agents tools to take ownership of their own performance.
For example, they can go in the system, listen to their own recordings, evaluate themselves, and think of ways to improve their skills.
This self-awareness has helped agents become more proactive in meeting their performance goals. Together with consistent coaching and support from managers, it has created a culture of accountability that helps strengthen the collections operations.
Though only recently implemented, Screen Recording is already making coaching more effective at SFS. It enables managers to verify what agents saw on their screens during a call and how they navigated different systems, so they can be coached on technical issues and customer-facing behaviors.
Nevels also sees strong potential in Real-time Agent Assist, which tracks whether agents are completing key actions during calls. It flags missed steps and provides instant feedback.
“Over time, this kind of reinforcement helps agents build better habits, especially during onboarding. The more they see something, the more they do it—it becomes a habit.”
SFS is also starting to implement Knowledge AI, which Nevels expects to help reduce the day-to-day strain on contact center managers.
“When agents, especially new ones, need quick answers, they call a manager,” he says. “And they usually want that information right away, while they’re on the call.”
With Knowledge AI, agents can get instant and accurate responses with just a few clicks, boosting their confidence in speaking with customers and helping them provide better customer experiences.
Looking ahead, Nevels sees even more potential in how AI can support real-time decisions, more accurate forecasting for staffing and performance, and more confident calls across SFS.
“We’re excited about what’s ahead and how else Observe.AI can support us,” he says. “The more we build into the platform, the more we get back—and the better decisions we’re able to make.”
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