For years, skip tracing has meant tedious manual work for asset-based lenders. Teams pull outdated addresses, stale phone numbers, and fragmented records, then burn hours chasing difficult to find borrowers. Portfolios age, costs climb, and resolution timelines stretch.
New research estimates that B2B contact data decays at 20–30% per year, meaning that within 12 months, as much as a third of the phone numbers and email addresses in a typical database are invalid.
Smart tracing changes the equation. By combining AI, predictive modeling, and real-time data, lenders and recovery partners can locate assets more quickly. This allows lenders to focus on high-value accounts and gives their team better information before they ever pick up the phone.
At ACS, we’ve seen how the right technology can make asset recovery both more efficient and more human. We have also observed that lenders have widely differing philosophies and policies guiding their acceptance and adoption of AI. Our approach honors client preferences with a combination of advanced and traditional tools.
For AI-ready clients, our experienced recovery professionals use data responsibly and communicate with borrowers respectfully and always in compliance with regulations and the highest ethical standards. For AI-averse clients, our skillful specialists continue to deliver industry leading outcomes using traditional, non-AI informed techniques which have been proven over our more than 25 years’ experience.
1. AI-Enhanced Skip Tracing: Seeing the Full Picture
Traditional skip tracing leans on static sources: credit headers, public records, change-of-address filings, and more. Every new source means another login and another manual search. Whether those dots ultimately connect depends on the individual collector.
Conversely, AI-enhanced skip tracing brings those data streams into a single environment and analyzes them at scale. Machine learning models weigh each source based on reliability and recency, then surface the most likely current address, phone number, or place of employment.
Case studies of early adopters of AI-powered skip tracing report up to 3× higher right-party contact rates, a 90% reduction in manual tracing effort, and 70% faster payment recovery than with traditional manual lookup workflows.
The results of AI-enhanced skip tracing are clear: fewer blind calls, less time wasted on bad information, and a stronger starting point for every outreach attempt.
2. Predictive Modeling Collections Process: Prioritizing the Right Accounts
Not every account demands the same level of effort. Some are unlikely to resolve regardless of outreach intensity, while others respond quickly when you contact them at the right time through their preferred communication channel.
Predictive models help find these key distinctions. By reviewing historical performance across thousands of accounts—balance size, account age, prior contact history, payment behavior, and more—these models assign each file a score tied to its likelihood of resolution and expected recovery value.
Collections teams that embed AI into operations cite a 40% reduction in operational expenses, around a 10% improvement in collections, and roughly a 30% boost in customer satisfaction on average.
For operations leaders, that means your top agents can spend more time on the accounts that matter most. High-potential files get earlier, more frequent attention, while lower-probability accounts follow a leaner, more automated path.
3. Real-Time Data Updates: Reducing Time-to-Recovery
Even the best model fails if the underlying data is stale. In a traditional environment, contact information might be months old by the time a collector sees it.
Smart tracing incorporates real-time or near-real-time data feeds into the asset-location process. As new information appears like address updates, employment changes, phone activity, it flows directly into the system that powers your models and prioritization hierarchy.
On the ground, the impact is direct: fewer dead ends, faster right-party contacts, and a shorter path from first outreach to resolution.
4. Compliance and Responsible AI
With any use of AI and expanded data sets, compliance can’t be an afterthought. Regulators, clients, and consumers expect clarity about which data are used, how decisions are made, and whether those decisions are fair and consistent.
Modern smart tracing platforms address this by building compliance into the design. Audit trails show what information was accessed and when. Engine guardrails ensure outreach strategies align with the FDCPA, TCPA, and other regulatory compliance laws, as well as personal data privacy and security standards. Models are continually monitored so they support—not undermine—ethical borrower interactions.
At ACS, we view responsible AI as part of risk management. The goal is not just better performance, but defensible processes you can explain and defend with confidence.
Building Your Smart Tracing Strategy
Moving from traditional skip tracing to smart tracing doesn’t require a complete technology overhaul overnight. Many organizations start by augmenting their existing workflows with better data enrichment, then layer in predictive models and real-time feeds as they see results.
What’s consistent is the outcome: higher right-party contact rates, shorter time-to-recovery, and more productive agents who walk into each conversation with better context. When technology takes on the heavy lift of finding and prioritizing accounts, your team can focus on what people do best: communicating clearly, solving problems, and preserving relationships wherever possible.
If you’re ready to move beyond manual skip tracing, ACS can help you map out a smart tracing strategy that fits your portfolio, risk profile, and operational goals.
Follow us on LinkedIn or reach for more industry insights on technology, training, and developments surrounding the asset-based lending landscape.