Owing to the proliferation of artificial intelligence (AI), virtually every industry doing business throughout the world is starting to look substantially different today than it did even a year ago. It’s happening at a pace not seen since the emergence of the internet, and collections is no different.

While you are undoubtedly feeling pressure to act, you also know the concerns around AI adoption are very real:

• Move too slowly, and you risk falling behind.
• Move too quickly, and you introduce compliance risk, operational gaps, and consumer friction.

This tension was addressed during a recent Finvi webinar hosted by insideARM, AI in Collections: Ethical Frontiers and Future Possibilities. (Listen to the full replay here.) The panel included legal, academic, and operational experts who shared a consistent message: AI can deliver real value, but only when it’s applied with discipline, tested rigorously, and grounded in human oversight.

As you move forward, here’s what you’ll need to consider.

AI Is Powerful, but It Inherits Your Risks

AI does not create insight from nothing. It identifies patterns in existing data and “translates” them into actionable conclusions.

This creates both opportunity and risk. If your data is strong, AI can help you move faster and make better decisions. If your data is incomplete or biased, AI will reinforce those weaknesses at scale. Anything missing from the data cannot be modeled, no matter how advanced the system becomes. In the past, this was limited to structured versus unstructured data, but today it means any critical data element, variable, or field that is not adequately or appropriately accounted for.

In collections, this matters more than in most industries.

Payment behavior is not always predictable. External factors such as job loss, medical events, or life changes often sit outside structured datasets. Even common indicators like credit scores can fail to reflect actual willingness or ability to pay — a sentiment not easily discernible in simple, linear data. But it exists nonetheless and, more importantly, can be captured as a signal with the right data usage.

At the same time, you’re operating in a highly regulated environment. Accuracy is not optional, and disclaimers do not remove your responsibility. Existing frameworks like FDCPA and Regulation F still apply, regardless of how decisions are made.

Bottom line: AI should never operate in isolation. It must be paired with human judgment, data awareness, and clear accountability.

The Human Element Still Drives Outcomes

One of the most important insights from the panel discussion is also one of the simplest: people respond differently to other people than they do to machines.

That difference shows up clearly in collections performance. Research shared during the webinar found that AI voice bots are less effective than human agents at securing payment, especially early in the collections lifecycle. Even brief exposure to a bot can reduce long-term repayment rates, regardless of whether human agents take over later.

The reason comes down to human psychology.

When consumers interact with a person, they feel a greater sense of accountability. A verbal commitment carries more weight. There’s a social and emotional dimension that simply does not exist in a machine interaction.

When that interaction shifts to a bot, promises feel less binding, and engagement becomes more transactional.

This doesn’t mean AI has no role in consumer interaction; rather, it simply means timing and context matter.

Where AI fits best today

• Supporting inbound interactions, especially after hours
• Handling simple, low-friction requests
• Assisting agents rather than replacing them

Where caution is needed

• Early-stage outreach
• Sensitive conversations
• Situations requiring empathy or nuance

Bottom line: The goal with AI is not to remove the human element, but to strengthen it.

The Biggest Wins Are Operational, Not Customer-Facing

Much of the conversation around AI in collections focuses on bots and automation. That’s where the risk is highest and results are still inconsistent.

The more immediate opportunity is behind the scenes. Organizations are already seeing strong results by applying AI to operational workflows that improve how agents work, boosting efficiency and recovery without additional headcount.

Examples include:

• Call note summarization that reduces administrative burden
• Real-time compliance monitoring to identify risk quickly
• Post-call documentation checks to improve accuracy
• AI-driven prioritization to focus agents on the right accounts

These use cases are relatively low risk, but high impact. Agents can spend less time on manual tasks and more time on meaningful conversations — applying human expertise and know-how to the situations that demand that skillset as opposed to rote execution. When this occurs, teams perform more consistently, which in turn reduces compliance exposure.

One panelist described this approach as wrapping AI around the agent, rather than inserting it between the agent and the consumer. That distinction is critical.

Want a deeper dive on where AI is actually delivering results in collections? Read the white paper The Smartest Collector Isn’t the One You Hire to explore practical use cases, compliance considerations, and what responsible adoption looks like in the real world.

More Data Can Improve Results or Undermine Trust

AI makes it easier to aggregate, connect, and act on data across systems. But more data does not automatically lead to better outcomes. Used incorrectly, it can create uncomfortable or even risky consumer experiences.

During the discussion, panelists described scenarios where systems surfaced personal details or past interactions in ways that felt invasive. This was referred to as “creepy collections,” and it highlights a growing concern as AI capabilities expand.

There are also real regulatory implications, especially when dealing with sensitive data like PII or PHI.

Cross-referencing or over-personalizing interactions can violate privacy expectations, introduce compliance risk, and damage trust with consumers. The line between helpful and intrusive is thinner than it appears.

Bottom line: Use data to inform strategy, not overwhelm the interaction. Focus on relevance rather than volume.

Governance, Testing, and Accountability Are Nonnegotiable

AI adoption is an operational and compliance commitment. Organizations need clear governance frameworks that define how AI is used, tested, and monitored over time. This includes:

• Documenting each use case and its intended outcomes
• Setting accuracy thresholds and performance benchmarks
• Continuously monitoring results and retraining models
• Keeping humans involved in critical decision points

It also means recognizing that models change. Even small updates can impact outputs, which makes ongoing validation essential.

Structured testing plays a central role here. The most effective organizations are not guessing how AI will perform; they’re using structured A/B testing to measure results, compare approaches, and validate cost-effectiveness before scaling.

At the same time, vendor oversight remains the agency’s responsibility. Panelists emphasized that agencies cannot transfer risk to a third party. Even if a vendor provides the technology, your organization remains accountable for compliance, data usage, and consumer outcomes.

This makes due diligence critical, especially in a market where new AI providers are entering quickly and may lack deep industry understanding.

Before you commit to AI, make sure you can defend every decision. Download AI in Collections: The Essential Buyer’s Checklist for the key questions to ask vendors on explainability, bias mitigation, data security, governance, and oversight.

The Biggest Takeaway: Confidence Comes from Control

AI can absolutely improve collections performance by increasing efficiency, reducing manual work, and uncovering insights that were previously difficult to access. But it’s not a shortcut.

Organizations that succeed with AI tend to follow the same path:

• Start with controlled, low-risk use cases
• Build strong governance and testing frameworks
• Keep humans involved where it matters most
• Apply data thoughtfully, not aggressively

In short, these agencies focus on control before scale. This is what allows them to adopt AI confidently, rather than cautiously or reactively.

Watch the Full Webinar Replay

This blog recap highlights the major themes, but the full conversation offers deeper insights and real-world perspectives.

Watch the webinar replay to hear how experts are approaching AI adoption in collections today.