Experience matters for fintechs adopting AI native debt collection

There’s a lot of excitement about the “do it yourself” approach to AI. Tools that once required teams of engineers and months of work can now be assembled in an afternoon. Today, a single skilled professional can build a proof of concept for an intelligent agent in minutes. It’s quick, impressive, and feels like the future.

For fintechs that have always thrived on innovation, this is deeply appealing. The idea of building AI solutions in-house and customizing everything down to the smallest parameter resonates perfectly with those who pride themselves on doing things differently. After all, why wouldn’t you approach AI differently, too?

But as the old saying goes, just because you can, doesn’t mean you should. While DIY AI has its place, it’s often riskier and slower than it first appears. Without the right experience, initial momentum can quickly stall. What seems like a fast track to innovation can become a long detour filled with rework, regulatory risk, and operational headaches.

The Real Objectives with AI Agents

When teams deploy AI agents, they usually have two primary goals:

1. Get to market quickly.
2. Drive measurable value.

Ironically, DIY AI can become a roadblock to both. While it’s relatively easy to stand up a POC that looks promising, turning that experiment into a production ready solution is another matter entirely. Teams often find themselves rebuilding from scratch once they realize the gaps between concept and compliance, or the prototype and performance.

Experience shortens that painful learning curve. Without it, developers have to learn the hard way: through trial and error, late night debugging sessions, and costly rebuilds.

Consider a simple example: an AI agent built to manage repossessions. Creating a basic version that issues a notice of default is easy. But have you verified whether that notice complies with state-specific regulations? Can the system confirm that the agent enforcing the action is licensed in that jurisdiction? You can ask an LLM what’s required, but is that information accurate and up to date?

Great ideas can’t survive without operational rigor. Building is only half the battle. Ensuring compliance, stability, redundancy, and security is what turns a clever prototype into a dependable solution.

Four Common Traps of DIY AI

Even when DIY projects make it into production, they often fail to deliver sustained value. Here are four pitfalls that pop up time and again, especially in regulated, data driven environments.

1. Data: The Most Neglected Factor

AI is only as good as the data it’s built on. Yet this is often the most overlooked aspect of any DIY project. It’s easy to prototype with a convenient CSV export or synthetic dataset. The real challenge starts when you try to operationalize it.

• How will the system handle nightly data batches versus real-time syncs?
• What happens when the data model changes or new sources are added?
• Are there secure and compliant access controls built into the system?

And then there’s AI drift, the gradual degradation of performance as business data and customer behavior evolve. Without a deliberate strategy for monitoring and updating models, AI performance starts to erode within months.

A robust framework anticipates these challenges. A DIY system makes you discover them one by one.

2. Compliance: The Moving Target

In the rush to get results, compliance is often treated as an afterthought. Teams focus on the “what” of AI output rather than the “how.” Yet in regulated sectors, how something is done often matters most.

When every team or engineer builds their own AI agents with their own compliance checks, inconsistency creeps in. Say a new regulation comes along: how do you update all those custom models and workflows? Without centralized controls or governance structures, you’re left juggling patchwork fixes.

The challenge isn’t just meeting today’s rules; it’s building an architecture flexible enough to keep pace with tomorrow.

3. Bias and Discrimination: The Hidden Layers

AI systems don’t inherently know what fairness looks like. They learn from data, and data reflects human systems, biases and all. That’s why guardrails and governance are critical.

A DIY setup may produce outputs that appear accurate but hide problematic patterns beneath the surface. Without deep visibility into data lineage, feature weighting, and decision logic, even well-intentioned models can perpetuate bias.

The danger is subtle: biased decisions often look correct, until they don’t. Only when complaints, audits, or reputation damage catch up do organizations realize the cost of not embedding responsible AI principles from day one.

4. Long Term Sustainability: Beyond the Prototype

Scaling AI means managing dozens or even thousands of agents over time. Models need retraining, infrastructure needs maintenance, and documentation has to stay current to ensure future teams can get up to speed fast.

DIY efforts often focus on the short term win of getting something to work, not the long term cost of keeping it healthy. When key contributors leave, undocumented logic and one off scripts become technical debt. The system slows down, breaks more easily, and becomes more expensive to fix.

In contrast, experienced vendors design for scale from the start, building standardized frameworks, documentation, and update workflows that make managing complexity sustainable.

When DIY AI Makes Sense

Despite its pitfalls, DIY AI isn’t always a bad idea. There are legitimate cases where it shines, specifically when you’re developing solutions that are truly unique to your business and unavailable elsewhere.

For instance, if you’ve identified a proprietary workflow or niche process that directly differentiates your strategy, building in-house can be the right move. In those cases, however, it’s best to build on top of existing frameworks rather than from scratch.

This hybrid approach gives you custom flexibility without reinventing every wheel. You get the freedom to innovate where it counts, while relying on proven infrastructure to handle the repetitive but mission-critical parts like compliance, monitoring, and scaling.

Refocusing on the Business of Collections

The central question comes down to this: what business are you really in? If your core objective is, say, to improve recovery rates, reduce delinquency, or enhance customer experience, should your team’s energy go into engineering AI agents, or into refining strategies, optimizing outcomes, and delighting customers?

Experience matters because it lets you focus on what you want AI to achieve, rather than wrestling with how it should work. Vendors who specialize in AI native debt collection or other financial workflows have already solved the infrastructure and regulatory challenges. They’ve built the frameworks, hardened the systems, and learned from every deployment. By leveraging that expertise, your team can move faster and safer, channeling its creativity into business impact instead of technical patchwork.

Leverage C&R Software’s agentic experience in collections

With clients in more than 60 countries, C&R Software has earned a reputation as the leading provider of agentic solutions for collections and recovery. Its flagship solution, Debt Manager, provides an agentic ecosystem purpose-built for creating AI enabled workflows that are secure, compliant, and ready for real world use.

With Debt Manager, collections teams design and deploy AI agents for any use case, supported by a trusted, enterprise grade environment that handles data governance, security, and regulatory requirements from the start. Its flexible AI adoption model lets organizations move at their own pace, tailoring each solution to their specific goals and risk tolerance.

To learn more about agentic solutions for collections and recovery, contact inquiries@crsoftware.com.