Propensity to Pay: The definitive metric for Operational Efficiency
Moveo AI Team
February 2, 2026
in
🏆 Leadership Insights
How to stop wasting human hours and focus on the Moveable Middle
Your collections operation invests millions in technology and teams. Yet it still treats all delinquent accounts the same way. The result? Resources wasted on accounts that would pay on their own. And on accounts that will never pay.
Human hours cost too much to be wasted. Every call to the wrong customer represents money that never comes back. Every email sent without criteria dilutes your operation's impact.
Propensity to Pay (P2P) changes this logic. This metric allows you to understand the customer journey to predict the next step, ensuring every cent invested in collections brings the highest possible return.
Propensity to Pay and Ability to Pay are different concepts. The distinction is fundamental.
Ability to Pay measures financial capacity. It looks at income, assets, and credit history. It answers the question: does the customer have the money?
Propensity to Pay measures behavior and intent. It analyzes interaction patterns, payment history, and responses to previous communications. It answers the question: will the customer pay?
A customer may have money in their account and zero intention to pay. Another may be in financial difficulty but prioritizes settling their debts. Static data doesn't capture this difference. Behavioral signals do. This distinction is essential for creating personalized payment plans that increase conversion rates.
P2P models combine historical data, payment patterns, browsing behavior, and previous interactions. The behavioral component reveals the debtor's disposition, indicating not just whether they can pay, but whether they want to pay.
Why P2P is the most critical metric for operational efficiency
Organizations applying predictive analytics report up to 30% higher recovery and 40% lower operational costs. These numbers come from operations that stopped treating collections as a volume activity.
The inefficiency problem is simple to understand. Firing off calls and emails to low-return profiles consumes human hours without generating payments. Every contact represents cost. If the contact doesn't convert, the cost becomes a loss.
P2P enables workforce optimization. When you know who has a high probability of paying, you can redirect the human service team to where it actually makes a difference. Specialized agents focus on complex negotiations. Automation handles the rest.
The impact on cash flow is direct. Identifying who is ready to pay reduces DSO (Days Sales Outstanding). Payments come in faster. The operation gains predictability. Investment decisions become clearer.
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The Moveable Middle Concept: Where the battle is won
Your delinquent portfolio is not homogeneous. Treating it as if it were means wasting resources. Segmentation into three groups reveals where to apply each type of effort.
Self-Liquidating: Customers who pay with a simple digital reminder. An SMS, an email, an app notification. Don't spend humans here. Automation solves it.
Lost Causes: Accounts with very low payment probability. Chronic delinquency history, lack of engagement, clear signs of inability or refusal. Don't waste resources here now. Recovery, if it happens, will come at another time, through other means.
Moveable Middle: Customers who need qualified interaction to convert. A well-conducted negotiation, a personalized payment plan, an incentive at the right time. This is where human expertise and artificial intelligence should be applied with full force.
The Moveable Middle is where the collections battle is won. These are customers who wouldn't pay on their own but respond to the right approach. P2P filters this group, separating who can be moved from who cannot. According to the Health Catalyst case study with Allina Health, this segmentation resulted in a $2 million increase in recovery in just one year.
📊 Is your operation ready to implement Propensity to Pay? Find out in minutes with our free Readiness Tool.
Human Hours vs. Intelligent Automation
Talent is scarce. Putting an agent to call someone with no intention to pay is a capital allocation error. The cost of a human hour justifies only interactions with real conversion potential.
Propensity-based automation solves the extremes. Easy customers receive automatic reminders. Impossible customers enter passive monitoring queues. The human team focuses on the middle: customers who can be moved.
This logic allows scaling the operation without increasing headcount. The same team produces more results. The cost per dollar recovered decreases. Efficiency compounds with each cycle.
Market data supports this approach. According to the Commercial Collection Agency Association, the chances of recovering full payment drop from 68.9% after three months to 51.3% after six months and 21.4% after one year. Speed matters. Intelligent targeting accelerates.
Implementation: From data collection to action
Propensity to Pay doesn't work with static data. The model needs to be dynamic, fed by real-time signals.
Data sources include payment history, demographic data, browsing behavior, and previous interactions. Conversational data is especially valuable. Every conversation with an agent, every response to an automatic message, every click on a payment link generates a signal.
The feedback loop is essential. Every interaction feeds the model. Every confirmed payment (or lack thereof) adjusts the predictions. The system learns and becomes more accurate over time. Experian reports that clients using P2P models achieved 10:1 ROI, with some cases reaching $15 million in recoveries.
For the C-Level, what matters isn't the algorithm. It's the decision dashboard. A clear view of who's ready to pay, who needs intervention, and who should be left for later. Information that dictates the quarter's strategy.
Propensity to Pay and Generative AI
Propensity to Pay is evolving. The question is no longer just predicting who will pay. It's predicting how the customer should be approached.
Tone of voice, preferred channel, discount offer, and optimal contact timing. Every variable can be optimized. Generative AI enables personalization at scale. The same model that predicts propensity can recommend the best approach strategy.
The transition is clear. From the Power Dialer, which fires calls in volume, to the Intelligent Orchestrator, which coordinates each interaction based on data and predictions. Volume gives way to precision. Brute force gives way to intelligence.
AI agents with memory connect every conversation to every decision. Today's signal informs tomorrow's approach. Recovery doesn't plateau. It compounds results over time.
Intelligence that compounds results
Propensity to Pay is the dividing line between operations that merely survive and operations that are profit centers.
Traditional systems treat all delinquent accounts the same. They waste resources on the extremes. They lose opportunities in the middle.
Intelligent systems segment with precision. They apply automation where it works. They direct human expertise where it makes a difference. Every interaction generates a signal that improves the next one.
The future of recovery isn't better isolated tools. It's intelligence that compounds results.
More conversations. More insight. More revenue.
Ready to identify your operation's Moveable Middle? Book a demo and see how Moveo.AI can transform your revenue recovery.