Raising the bar: AI agent safety and security in financial services

The stakes are higher than ever—here’s what to demand from your AI agent

When an AI agent makes a mistake in scheduling a dental appointment, explaining a retailer’s return policies, or upgrading a customer to a premium mobile service plan, it’s frustrating for the customer and the business. But the damage is typically small. 

For banks, investment firms, and other financial institutions, the stakes are higher. It all comes down to trust, and losing a customer’s trust is just one of the risks of using an AI agent that lacks sufficient safety and security measures. There could also be legal and compliance ramifications, not to mention a financial hit.

When the stakes are higher, the standard for AI agent safety and security must be higher, too. What does a higher standard for safety and security with an AI agent look like? It’s a critical question you need to answer before launching a generative AI agent to talk to your customers. 

Getting beyond AI safety basics

Since the first AI agents for customer service hit the market, many solution providers have steadily improved their safety. They’ve added guardrails to keep the agent on task and within scope, mechanisms to prevent jailbreaking, and QA models to evaluate responses before they reach customers. 

That’s great, but it’s just the bare minimum of what you should expect from a credible vendor with a reliable AI agent solution. You’ll need to raise the bar for AI safety. The level of trust required for an AI agent in the financial services industry is impossible to achieve with typical safety mechanisms alone. Your team must also be empowered to monitor performance, measure impact, and fine-tune your AI agent quickly.

What you really need is a vendor who also enables your visibility, oversight, and control of your AI agent.

Visibility into your AI agent’s performance

You can’t manage what you can’t measure. And you can’t measure what you can’t even see. So solutions that don’t provide sufficient visibility into how the AI agent is performing fall short when it comes to safety and security. Look for a solution with these critical capabilities:

A clear record of the AI’s actions and reasoning

A generative AI agent that’s committed to safety and security should create an audit trail for every conversation. This record should document every utterance and action it performed, as well as its reasoning throughout the interaction. The AI’s reasoning is especially important for understanding not just what it did, but why. That’s the kind of information that makes or breaks a root cause analysis when you’re investigating inconsistencies or performance issues.

Robust performance reporting and analytics

To generate useful insights into your AI agent’s performance, you’ll need to see the big picture, and dig into the details. So, you’ll need a solution with robust performance dashboards and automated reporting to identify patterns in your metrics, prioritize improvements, and track quality over time. To extend your visibility, you’ll also need the ability to extract custom data feeds for additional analysis.

Real-time monitoring of every customer conversation

Analyzing the previous week’s metrics can help you spot performance trends. But it’s even better to identify issues in real time—before they become bigger problems that affect your customers and your business. Look for an AI agent that includes automatic monitoring of every conversation to flag suspected inconsistencies for your review. Immediate alerts for issues with high-impact potential will equip your team to respond quickly and stop a brewing problem before it gets any bigger.  

Flexible human oversight and collaboration

Every AI solution vendor acknowledges the importance of keeping a human in the loop. But not every vendor has built a solution that enables true human-AI collaboration for live operations. For many vendors, the active role of a human in the loop ends after the initial testing and optimization. After that, humans are no more than escalation points for when the AI fails.

That’s a missed opportunity for you—and a serious gap in AI safety. Look for a vendor who has designed and built an AI agent with human-AI collaboration in mind. Look for these advanced capabilities:

Collaboration, not just escalation

Some solution providers tout their AI agent’s ability to hand off a call or chat with enough context to help the human agent step in seamlessly. But even a smooth transfer dings your containment rate. And it’s a sign of how much the AI can’t handle safely. 

Instead, look for an AI agent that can consult a human when for information, guidance, or to perform a task in a system it cannot access – without transferring the customer. This type of AI agent simply asks a human for help when it hits a roadblock, then continues serving the customer once it has what it needs. That expands the use cases you can automate safely.

You should also have the option to require the AI agent to ask a human for approval to perform certain high-stakes actions by policy. You might not want your AI agent to independently make a decision about crediting a customer’s account when there’s a disputed transaction. But if the AI agent can request approval from a human agent, you’ll still get the benefits of automation without sacrificing human judgment.

Fast-tracking the AI’s learning for new use cases

The first weeks after launching a new use case for your AI agent are critical. Even with rigorous testing, you won’t be 100% sure that it will perform well in the wild. You can’t always anticipate everything. This is the perfect time for an elevated state of human oversight.

A solution that allows your team to review and revise every response before it reaches a customer during this post-rollout period provides a crucial safety net. When this human oversight is paired with AI that learns from their feedback, you accelerate this initial optimization period. This type of AI agent quickly learns from your best human agents so you can be confident that it will be both reliable and safe.

A full suite of tools for testing and fine-tuning

Deploying updates you’re not entirely sure you can trust undermines safety and security. And when a change is necessary, every hour of delay creates additional risk. As fast as conditions change in customer service, you’ll need to be able to fine-tune, iterate, and release modifications to your AI agent without delay whenever the need arises. To do that, your contact center will need to be equipped with no-code tooling to avoid the complication of waiting on IT or development resources for every update. And they’ll need the tools to fully test before releasing any modifications. 

Testing and simulation

Testing a generative AI agent can be tricky. It’s unscripted and doesn’t follow deterministic paths. The gold standard approach is to run realistic simulations of a wide range of scenarios, from simple to complex. That goes far beyond testing for specific questions or actions. 

Look for a solution that includes a suite of testing tools that allow you to define simulations and automate testing of complete scenarios for different intents and customer personas, including API calls, knowledge retrievals, and multi-turn conversations. That’s as close as you can get to live operations—without risk. This kind of testing is a critical component of maintaining safety and security as you add use cases, modify policies, and update task instructions for your AI agent. 

Optimization with no-code tooling

Deploying a generative AI agent isn’t a set-it-and-forget-it operation. It requires fine-tuning over time. If you have to depend on your IT or development resources every time you need to modify something, some of your anticipated ROI will evaporate with added costs, lost time, and a degraded customer experience. And if the need for fine-tuning is related to a safety or security issue, you’ll also incur increased risk.

No-code tooling empowers non-technical users in your customer service operation to monitor and optimize performance on their own. That leads to quicker adjustments and safer operations.

Raising the bar on AI safety and security for financial services

The first step in ensuring safety and security with an AI agent is to choose a vendor who’s built a solution with safety and security mechanisms embedded from the ground up, and by design. But your pursuit of safety and security shouldn’t stop there. To ensure safe and secure performance in the real world, your team must be empowered and equipped with the tools to monitor, optimize, and scale your AI agent on your terms and at your pace.

A solution provider who puts you in control every step of the way isn’t just asking you to trust them and their AI agent solution. They’re giving you a good reason to.