What are the potential risks of agentic AI?

As we already know, AI is taking over every industry, actively reshaping how businesses interact with customers and make decisions. One of the most powerful and rapidly evolving branches of this technology is agentic AI. These systems are designed to act independently on behalf of a user or organization: making decisions, carrying out tasks, and adapting to context all on their own.

But as with any powerful technology, the benefits come with risks, particularly if agentic AI is implemented without the right safeguards. Financial institutions in particular must be cautious. When you’re dealing with regulated environments, personal financial data, and vulnerable customers, getting it wrong simply isn’t an option.

What is agentic AI?

Agentic AI refers to artificial intelligence systems that act autonomously or semi-autonomously to carry out tasks on behalf of users. Unlike traditional software that follows pre-programmed instructions, agentic AI is dynamic. It can interpret objectives,  assess options, and make decisions or take actions based on real-time data and contextual understanding.

It can be used in collections to summarize customer accounts, assist agents during calls, and even interact directly with consumers through chatbots. The result? Higher efficiency, more personalized service, and faster resolution. But with this shift comes a new set of risks, especially if this powerful technology isn’t implemented with sufficient care.

The compliance and security risks of agentic AI

For banks, lenders, and other financial institutions, the stakes are high. When working with sensitive financial and personal data, compliance and security are mission critical. Here are key risks to be aware of:

Incorrect calculations and misinformation

Agentic AI can interpret and act on data, but if it interprets that data incorrectly in regulated environments like collections, it can do real harm. For example, if an AI incorrectly calculates a customer’s balance, interest, or fees, this could result in:

• Breach of financial regulations
• Customer harm and reputational damage
• Costly remediation efforts

Unlike traditional rule-based software, agentic AI produces non-deterministic outputs. That means it may give different answers to the same question depending on subtle changes in input or context. This makes validation and quality control more complex but absolutely necessary.

Potentially unpredictable behavior

Testing AI systems (especially those powered by large language models) requires a strict and thorough process. This is because you need to be absolutely certain it will answer a huge amount of queries within your predefined set of rules. You need to clearly define what not to do in specific contexts just as much as what to do.

For example, should a virtual agent suggest payment plans? Can it acknowledge hardship claims? And how does it handle requests for sensitive account details? Without strict boundaries and business logic enforcement, agentic AI can act outside acceptable or compliant parameters.

Data privacy and security

Agentic AI relies on access to data to function effectively, but that access needs to be tightly controlled. A common concern is whether these AI systems are storing, sharing, or learning from sensitive customer data.

In collections, this is non-negotiable. Any leak or misuse of customer information could violate data protection laws like GDPR, lead to regulatory fines, and severely damage trust.

There's also the question of customer confidence. People want to know their data isn’t being used to “train” AI or exposed to external systems. Transparency, accountability, and clear boundaries are essential.

Human oversight and governance

Before putting an AI agent in front of a collections team, you need to be confident in its performance. That means rigorous testing, continuous monitoring, and human sign-off. Without that, banks risk deploying AI that behaves inconsistently or even non-compliantly in live environments.

Building it right - the role of trusted solutions

This is where working with established collections solutions makes all the difference. These partners integrate secure, compliant, and production-ready agentic AI developed specifically for collections use cases. Every agent, from account summarizers to intelligent call assistants, is built with:

• ISO 27001-certified security standards
• No data leakage: All customer data stays within your private AWS instance
• No AI learning from private data: AI uses data for answers, but doesn’t store or train on it
• Safe deployment: With dedicated tools like an agent builder, you define exactly what AI can and cannot do

This combination of control, transparency, and built-in compliance is what provides financial institutions with the confidence to adopt agentic AI without putting themselves or their customers at risk.

Confidently integrate agentic AI into your operations 

Agentic AI represents a powerful step forward for collections and recovery. But it needs to be approached with your eyes open. When implemented without proper oversight or infrastructure, the risks are significant.

To build trust and loyalty, you need to partner with platforms that provide secure, compliant, and intelligent agentic AI. C&R Software’s cloud-native solutions are designed for this future, giving you the power of AI with the governance and security your business demands. 

To find out more about Debt Manager and its AI capabilities, contact a member of our team today at inquiries@crsoftware.com.