AI Debt Collection Software: How to apply it in your company

For any company, especially a financial institution, it is necessary to have software that allows you to maintain control of debts and collection notifications to your clients, among other functions. This is precisely what an AI-powered debt collection software is used for.

Artificial intelligence is no longer a novelty, or something out of a science fiction movie—it has become the best assistant and tool for any professional or company worldwide. That’s why we should not fear it, but instead integrate it into the most important processes of the company.

The growth of AI usage has increased exponentially across several industries, and banking should be the next one. As indicated in a Statista study: “IT services in the technology industry were the most intensive AI users worldwide in 2024. In general, the technology sector, as well as the media and telecommunications sectors, are the most prolific. Within functions, IT, marketing, and service operations are the ones using AI the most. This is because these functions can benefit from different levels of AI, both advanced and basic, to improve multiple processes.”

Why evolve into AI-powered debt collection software?

A basic debt collection software is very useful—we won’t deny it. But taking it to its maximum evolution by integrating artificial intelligence will help you overcome certain limitations you may have already noticed: impersonal communication, low predictability, and little capacity to prioritize intelligently.

Let’s list the reasons why you should migrate to AI-powered debt collection software:

Key Benefits:

• Predictive prioritization: Machine learning models can assign a “payment propensity” to each debtor, identifying who is most likely to respond. This way, human resources are focused on accounts with the highest expected return. Studies report improvements of 15–20% in recovery when applying these techniques.
• Smart omnichannel segmentation: An AI system can decide whether to contact a debtor via SMS, email, call, WhatsApp, or another channel, adapting to their profile, schedules, and response likelihood. Messaging also adjusts: tone, frequency, and recommended channel.
• Automation of repetitive tasks: AI allows delegating operational tasks (sending reminders, verifying payments, answering simple inquiries) autonomously. In fact, financial teams using automation have reduced manual effort by up to 40% according to The Hackett Group.
• Improved customer/debtor experience: Instead of massive and impersonal communications, AI enables a more empathetic and personalized approach. This not only helps collect but also preserves the relationship and avoids negative brand repercussions.
• Operational efficiency and cost reduction: AI collection platforms often multiply productivity by 2x to 4x and reduce operating costs by 30% to 50%, according to market estimates.
• Compliance and risk control: A critical element in collections is legal limits (allowed hours, appropriate language, local regulations). AI-powered software can incorporate automatic compliance rules to avoid sanctions and filter risky interactions.

Rootlenses: the AI tool you need for collection software

One of the artificial intelligence tools you can implement is Rootlenses, with its Voice functionality which allows you to create and train an AI agent to make both automatic and scheduled calls to all clients with pending payments.

With its ability to adapt to the client, maintain a fluid conversation, and persuade effectively, Rootlenses is an ideal tool to integrate into a collection software, maximizing its functionalities and bringing benefits to the company.

How can AI-powered collection software be implemented?

This must be done step by step—rushing implementation could result in mistakes. First, data and information must be collected for the AI to analyze, such as delinquency rates by aging, payment habits, frequent communication failures, channels used, and customer profiles. You need quality data: payment history, previous contacts, responses, and rejections.

Once this is set up, we identify the goals to be achieved after implementing the tool. With this in hand, we then choose a tool that adapts to the needs of your collection software and its functionalities.

There are two paths:

• Buy or license a specialized solution: Some platforms on the market already combine predictive models, automation, collection workflows, and ready-to-use APIs.
• Build the AI layer internally over your collection software: At Rootstack, we could develop predictive scoring modules, rule engines, and conversational agents tailored to your specific industry.

With these steps established, pilot testing begins before full implementation of the tool.