Horizontal AI’s inability to build trust with customers in sensitive situations is one of its most significant limitations in debt recovery. While it shines at detecting patterns, automating processes, and efficiently creating messages, generic AI falls short in understanding the nuances of human behavior that drive repayment decisions. Whether it’s in segmentation or messaging, AI often misses the mark, leading to flawed strategies that alienate customers rather than engaging them.  

Horizontal AI’s blind spot: Building human connections in collections

Segmentation models powered by horizontal AI may group customers based on payment history or financial stress indicators—but fail to account for the emotional and psychological factors that influence decision-making. This lack of nuance often leads to generic messages like this one, sent to a past-due customer during the holidays:  

“Your account is overdue. Please make a payment immediately to avoid penalties.”

This message might technically convey urgency, but it completely ignores the emotional and financial challenges the customer might be facing. Instead of motivating repayment, it risks making the customer feel overwhelmed and unsupported—pushing them further into avoidance.  

What happens when debt recovery strategies focus on people, not just data

While generic AI collections strategies often fall flat or are even counterproductive, personalized communication can dramatically change collections outcomes.

For example, a specialty lender servicing sub-prime auto loans saw a response rate of over 60% to resolve debt when they fine-tuned their segmentation and switched to tailored messages. More than 26% of customers self-cured through email links, while the rest reached out directly by phone or email. This result demonstrates the clear power of personalized, human-informed communication.  

What horizontal AI can’t solve: The psychology of repayment behavior

Generic AI also fails to account for the mental shortcuts and coping strategies customers under financial stress use, like delaying payment or avoiding contact. Without the ability to comprehend these psychological factors, AI-driven strategies risk failing to deliver sustainable results in delinquency management.

A past-due customer juggling multiple bills might prioritize smaller debts they feel they can “check off” quickly, ignoring larger balances entirely. Generic AI strategies often fail to account for this cognitive bias, missing an opportunity to guide the customer toward meaningful repayment.  

This is why combining AI with behavioral science is essential—because it bridges the gap between data and human connection to deliver better outcomes in debt recovery.  

The trust crisis: Customers are skeptical of AI

The limitations of generic AI extend beyond its inability to connect emotionally—customers are also becoming increasingly mistrustful of AI itself. In the next blog, we’ll explore what this means for debt recovery and how it impacts your collections strategy.

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What does AI’s trust gap mean for your collections outcomes—and how can you adapt?

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