Smarter debt management with AI: personal, predictable and efficient

What's the solution: AI that understands payment behavior

A modern approach combines a SaaS platform for credit management with an AI model that actually payment behavior analyses. Not just scoring or automation per se, but linking behavioral data (such as whether a message was seen, opened, and whether it went to the payment page) to messaging and actions.

Key features:

• Behavioral analysis; Instead of just a demographic score, what does a customer do with the payment request?
• Automatic forecasting; of the chance of payment and the most appropriate intervention
• Human in the loop; employees can control and adjust decisions
• Privacy aware; models trained on anonymized datasets

A practical example

Suppose a customer visits the payment page three times but does not complete the payment. This behavior raises a red flag: this customer may want to pay, but faces an obstacle (e.g. payment options or financial space). In that case, human intervention, using insight from the system, is more effective than repeated standard emails.

How it works; the parts at a glance

1. Data collection; each payment request is labeled (paid/unpaid) and events such as opening, click, and payment attempt are recorded.
2. Model training; the AI model learns from hundreds of millions of transactions to recognize predictable patterns.
3. Channel and message optimization; determine which channel, tone of voice and time gives the greatest chance of success.
4. Human in the loop; employees can control model decisions and, in 1% of cases, manually intervene to validate and adjust the model.
5. Continuous improvement; AB tests and monitoring ensure that the approach continues to improve and is in line with changing customer behavior.

Concrete results that show that it works

Implementations at large organizations show measurable results:

• 52% fewer royements (escalations to collection/bailiff) in the first year with a major insurer.
• Increase in customer satisfaction because communication takes place more personally and at the right time.
• Decrease in operational costs because teams focus on cases where human intervention has real added value.

Why this approach is better

The combinations of personalization, predictive models and human control provide multiple benefits:

• More payments with fewer actions; less “shooting hail”, more targeted actions.
• Higher customer satisfaction; through the right tone, the right channel and understanding the customer's situation.
• Flexibility alongside existing ERP systems; the POM platform makes it possible to experiment and optimize without major ERP changes.
• Scalability; through automation and openness to continuous optimization via data.

Implementation tips for organizations

• Start small: Test a specific customer group with predictive messaging before rolling out fully.
• Keep people involved: let employees control decisions and use their feedback to improve the model.
• Label and anonymise data: quality and privacy of training data are crucial.
• Perform AB tests by tone of voice, channel and timing to discover what works for your customers.
• Measure operational and customer-focused KPIs: not only collection rates, but also customer satisfaction and actions required per case.

Key message

The innovative core is simple: data analysis on payment behavior combined with the right tone of voice and channel provides a much higher payment feasibility. Efficiency with a personal touch

Organizations that move from reactive to proactive, supported by predictive AI and human control, save costs, increase customer satisfaction and prevent unnecessary escalations. Time to say goodbye to one-size-fits-all and choose a strategy that really works.