Artificial intelligence is the undisputed future of banking. According to the European Banking Authority, 92% of EU banks are already using AI in some form, and the remaining 8% are either pilot testing or actively exploring use cases.
But increasing AI use comes with increasing regulatory responsibilities. Notably, the EU AI Act classifies creditworthiness assessments and closely related decisioning activities including collections strategies as “high risk” use cases. This means they have to meet strict requirements for risk management, data governance, transparency, and more.
For banks looking to take advantage of this advanced technology, it’s essential to innovate responsibly. This article offers a path forward, looking at real world use cases of what works, what doesn’t, and how to build the right tech stack to modernize collections.
To start, it helps to be clear on what the EU AI Act actually requires from banks looking to use AI in collections. Some key themes include:
In other words, the AI is only the start. You need to have a deep understanding of how it works and why it works the way it does. Explainability, transparency, and accountability: these are the three principles at the heart of responsible AI in collections.
Unfortunately, most existing collections setups weren’t built with these regulatory principles in mind. Instead, many of them were put together over time: a legacy decision engine here, some hard‑coded rules in the core system there, a few bolt-on AI tools plugged in around the edges. On paper it works, but when you look at it through the lens of the EU AI Act, the gaps become obvious.
The result is a collections stack that delivers some automation and efficiency but doesn’t comfortably meet the core themes of the EU AI Act. It leaves banks stuck in an uncomfortable middle ground: worried about falling behind competitors, but hesitant to push ahead with meaningful AI change. AI ends up underused or trapped in small pilots that never scale. It’s a familiar pattern where legacy tech holds innovation back.
So, how can banks move past these legacy limitations and shift from AI that “sort of works” to a solution that’s explainable, compliant, and scalable?
The answer is a structured, capability‑driven approach. Ultimately, it’s the technology foundation you choose that determines whether responsible AI is even possible. Here are some of the core capabilities a modern collections solution needs to support safe, scalable AI under the EU AI Act:
An AI native solution is built from the ground up to support model governance, data quality, explainability, and auditability. When AI is woven into the core architecture rather than added later, the system can adapt as strategies change and as regulatory expectations evolve. This avoids the common trap where AI becomes a bolt-on tool that sits beside legacy logic and quietly turns into legacy software of its own.
AI native design also means the solution supports experimentation and safe iteration. Teams can introduce new models, adjust decision strategies, and respond to emerging risks without re‑engineering large parts of the system. This flexibility becomes essential as the EU AI Act raises the standard for how high risk systems should be monitored, documented, and justified over time.
A centralized orchestration layer brings data, rules, models, and treatments into one controlled environment. It eliminates the scattered logic that often exists across core systems, decision engines, and point AI tools. When everything flows through a single layer, banks gain a clear understanding of what drives each decision and how outcomes vary across segments.
This centralization also strengthens transparency and traceability. It becomes easier to see which data sources influenced a treatment path and easier to adjust strategies without creating conflicting logic in different parts of the stack. For high risk AI, this kind of architectural clarity is key to meeting expectations around data governance, consistency, and accountability.
Different banks have different appetites for automation, and those appetites can evolve over time. A flexible AI model gives banks control over how much autonomy the system should have and where humans should intervene. It also enables teams to blend rules with machine learning and generative AI in a way that aligns with internal policy and supervisory expectations.
This flexibility also builds trust. When leaders know they can set review points, adjust confidence thresholds, or gradually scale automation in specific portfolios, they are more willing to expand AI use beyond small pilots. A solution that supports this range of control helps banks adopt AI responsibly rather than feeling pushed into uncomfortable levels of automation.
The EU AI Act makes clear that humans must stay accountable for high risk AI. That means the solution needs:
In other words, supervisors and collectors need visibility into how decisions were reached to ensure a healthy balance is maintained between AI support and human judgment.
A strong audit trail is essential for compliance under the EU AI Act and for internal governance more broadly. Banks need to be able to reconstruct any decision, including the data used, the model version active at the time, the logic applied, and any human actions taken along the way.
Modern collections solutions can automatically generate:
When this information is captured automatically, it reduces the burden on teams and removes one of the biggest barriers to scaling AI.
Done well, implementing explainability and compliance under the EU AI Act can turn responsible AI into a reality for leading banks. Transparent and well-governed collections support better customer outcomes, faster resolution of arrears, and improved reputational standing at a time when AI practices are under intense public scrutiny.
Because fragmented, legacy stacks weren’t designed with these capabilities in mind, it’s hard to retrofit them cleanly. That’s why more banks are looking to adopt AI‑native collections solutions that bring governance, explainability, and monitoring into the core of the product, instead of treating them as afterthoughts.
C&R Software’s Debt Manager is a compliance-focused, AI-native collections solution managing more than $8 trillion of debt across over 60 countries, giving it broad exposure to regulatory regimes and best practices in highly regulated markets. Its roadmap explicitly emphasizes strengthening compliance and governance frameworks, alongside contextual AI assistance and more sophisticated autonomous functions. For banks looking to modernize their collections stack, this offers a way to accelerate innovation without losing control.
To learn more about AI-native collections, contact inquiries@crsoftware.com.