DIY segmentation might seem like the fastest way to streamline debt recovery, but it’s a solution that often creates more problems than it solves. Let's examine why horizontal AI falls short—and what that means for your collections strategy.

70% of collections teams are turning to AI—but is it doing more harm than good?

As delinquency management teams look for scalable ways to improve recovery and cure rates, the adoption of AI for customer segmentation continues to grow. In fact, 70% of collections companies are exploring or actively adopting AI. The question is: Is AI delivering the results they need?

The blind spots in horizontal AI segmentation

Delinquency management teams increasingly use horizontal AI platforms like Google AI Platform, AWS SageMaker, and Azure ML for segmentation due to their ability to process vast amounts of data at lightning speed. These tools make it easier to group customers by attributes like payment history, engagement patterns, or financial stress indicators—helping teams tailor their strategies to individual customer needs.

However, segmentation with horizontal AI isn’t without its challenges. These platforms are designed to be general-purpose. That means they lack built-in understanding of delinquency-specific behaviors and the mental shortcuts customers rely on when under financial stress. As a result, debt recovery teams have to train models from scratch, relying on their own data and assumptions. This introduces risks, including:

  • Noncompliance with regulations: Data points like age, race, and zip code cannot be used to train AI, as that could lead to illegal discriminatory practices. With generic AI, the burden falls on collections teams to ensure compliance and avoid unintentionally breaking the law.
  • Amplified biases: If training data is incomplete or skewed, models can unintentionally reinforce biases, leading to unfair or ineffective segmentation.
  • Overlooked customer motivations and behaviors: Behavioral science insights are critical to understanding how past-due customers make decisions, yet horizontal AI lacks this perspective, often treating delinquent customers like generic data points.
  • Complexity and expertise gaps: Building, fine-tuning, and validating models requires specialized skills and significant resources—making in-house segmentation efforts prone to errors and inefficiencies.  

The costly mistakes of DIY customer segmentation

The risks involved with DIY customer segmentation can undermine your entire debt recovery process:

  • Generic messaging: Poor segmentation produces one-size-fits-all communication that alienates customers, reducing engagement and jeopardizing repayment rates.
  • Eroded trust: Customers who feel misunderstood or mistreated are less likely to engage with your collections process—or your organization in general.
  • Missed opportunities: Tailored, delinquency-specific strategies are critical to improving recovery rates. With DIY segmentation, these opportunities often go unrealized.  

Stop wasting time with DIY—purpose-built AI delivers what you need

DIY segmentation with horizontal AI may promise speed and scalability, but its limitations can derail your debt recovery strategy. The hidden costs include more than additional overhead—they risk eroding trust and alienating customers.

In contrast, purpose-built—or vertical—AI for debt recovery is fine-tuned to get you the results you want. Purpose-built AI is trained specifically on delinquency-focused data combined with behavioral science insights. This allows you to unlock segmentation strategies that deliver real results—boosting recovery rates, building customer trust, and driving measurable outcomes.  

Read Is ChatGPT sabotaging your collections strategy? for more insights on AI in delinquency management.