AI Monitoring: How to Detect Risk & Fraud in Real Time

Key Takeaways: 

• Real-time AI monitoring is already deployed at scale in many call centers. But most real-time solutions are looking for the wrong things. They generate a large volume of low-value data and miss the fraud attempts, escalation risks, and compliance violations that matter.
• Voice-native AI digs deeper than keywords and transcripts. It identifies behavioral and acoustic signals such as changes in tone, hesitation, and unnatural language to flag risks before it’s too late to take action. 

AI drives nearly every aspect of the call center today, yet organizations are failing to convert adoption to impact. 88% of organizations have adopted AI, such as AI monitoring, in at least one business function, according to McKinsey, but Gartner says only 20% of AI projects are meeting expectations. 

Though 81% of businesses have adopted AI in contact centers, only one-quarter have actually deployed AI and integrated it into daily tasks. As businesses continue to adopt AI monitoring and other AI solutions with the hope of improving customer service (62%), creating customer experiences (42%), enhancing data analytics (49%), and providing faster access to information (67%), the expectations of what these solutions will need to do will continue to accelerate. The challenge isn’t access to AI, but extracting actionable intelligence from the conversations already happening. 

What is AI Monitoring? 

AI Monitoring means applying artificial intelligence to analyze what’s happening within a call center conversation in real time. That’s different from analyzing call transcripts or applying post-call scoring. Monitoring with AI takes that information and allows you to act on it as it’s happening.

Why Traditional Monitoring Fails in Modern Call Environments 

Legacy call monitoring approaches generally fall into three main categories, often used together: 

• Manual sampling and reviews: QA teams listen to a small sample of recorded calls and manually score agents on predetermined metrics such as handle time and closed deals. 
• Retroactive batch analysis: Recorded calls are searched for keywords, flagged phrases, and patterns in speech transcripts. 
• Metadata analysis: Call volume, length, and CSAT scores are reviewed for patterns across a broad range of calls. 

Many contact centers still employ all three methods today. But however you combine these approaches, the underlying issue remains: you’re looking at call data well after the interaction occurred. 

These traditional call monitoring techniques were designed for a different time, when there were fewer channels to monitor and call volume was only a fraction of what we see today. There was also far less risk involved.

Customer experience expectations are growing exponentially. 83% of businesses are investing in CX transformation initiatives to achieve measurable results. 41% are implementing generative AI, virtual assistants and chatbots to improve CX. Yet AI monitoring is lagging behind. How your teams monitor calls is not evolving fast enough to meet the demands of high-risk conversations.

Legacy monitoring does not work in modern call environments. Here’s why: 

• The bulk of call monitoring happens after the fact. Coaching teams are listening to agent calls after they’ve been completed, scored for performance, and tagged for missed opportunities. 
• It lacks context. You can read a transcript all day long, but you’ll never know how it sounded. Handle time and first-call resolution percentages can tell you what happened on a call. They won’t tell you why it went wrong or how your agent skillfully worked through an escalated moment.
• You’re missing important moments. Moments like a change in tone or a long pause can indicate fraud or opportunities for your team to change the outcome of a conversation. If you’re unable to pinpoint when these occur, you’re likely missing the most important moments of your calls.

You’re getting half of the story. You learn about results but not how they happened. 90%+ of conversations never get monitored. 

You don’t know what happened during the call until after the fact, and that’s only some of the conversations.And when you uncover a problem, it’s usually too late.

Early AI Call Monitoring

Automation arrived with first-generation AI. Transcripts are generated, and keywords are flagged. Simple sentiment analysis identifies positive and negative conversations. Dashboards visualize trends over a group of calls.

Many of these solutions relied on pattern-based transaction analysis. “These solutions look at transactions after they occur, identifying suspicious patterns or unusual activities based on historical data,” explains Brandon Wilson, Senior Account Executive at Modulate. “This method focuses exclusively on past behaviors and can't detect live attempts to manipulate an agent during a phone call.”

The benefit to this approach is scalability, but these solutions fail to consider the full picture. Many platforms still operate solely from a transcript standpoint. Alerts are raised based on pre-set rules. And any meaningful, actionable intelligence is provided either after-the-call or, at best, a few seconds faster.

What’s still missing? Behavior. And why those words are being said. 

Modern (Real-Time) AI Call Monitoring

Real-time AI call monitoring technology monitors calls as they’re happening. By utilizing advanced Natural Language Processing (NLP), AI monitoring solutions listen to how something is said, not just what is said. Tone, speed, hesitation, interruptions, upselling pressure are just some of the factors taken into account. 

Patterns change as the conversation shifts. When a friendly call takes a sharp turn towards anger, it's flagged instantly. Indicators of fraud are identified while the call is ongoing. Compliance risks are identified before your agent ends the call. 

Instead of just reviewing calls, real-time monitoring provides insights into conversations as they occur, ushering in conversation intelligence. 

AI Monitoring Signals That Traditional Metrics Miss

Traditional call center metrics tell you what happened, but they don’t tell you why it happened or how it unfolded. 

AI monitoring detects signals as they happen during a conversation. 

• Behavioral Signals: The flow of conversation can tell you a lot. Is someone being cut off? Who’s talking most of the time? Patterns of aggressive communication may indicate stress, misunderstanding, or bullying.
• Voice-Based Emotional Signals: Emotions like frustration and anxiety are detected in your caller’s voice well before they’re put into words. Text-based sentiment analysis misses this, because you can only analyze what was typed or said, not how it was said. Voice-based sentiment analysis detects changes in pitch, tone, and speed that are not captured in a transcript, giving your agents a chance to deescalate before it’s too late.  
• Intent Signals: Commonly, scams and fraudsters use high-pressure tactics, manipulation, and implied threats. AI can help you spot these clues before typical scam keywords are spoken.
• Acoustic Signals: Sometimes the way something is said is just as important as what was said. Speech hesitation or unnaturally robotic speech may be an indicator of a canned response or a synthetic voice.

Angel Carrillo-Bermejo, a Machine Learning Engineer at Modulate, explains, “Humans have a unique combination of pitch, cadence, and tone that varies in tiny, irregular ways. We emphasize and pause on words in our own way, and we let our emotions shape our pitch. AI-generated voices often miss these subtle features, which is why they can sound robotic and flat, or unnaturally smooth and polished.”

Pappas explains, “In older deepfakes, those flaws were obvious to most of us. But today’s advanced audio generation tools hide them so well that even experts struggle to tell real from fake.”

5 Key AI Monitoring Use Cases

AI monitoring matters when it can affect the outcome of what happens during the call. These are the times when second-by-second situational awareness is critical.

Fraud Detection (Before It Happens)

Fraud starts with behavior long before traditional systems catch obvious cues. Does it sound too scripted? Is the caller demanding urgent action? Is their behavior inappropriate for the situation?

Voice fraud is on the rise as well. In fact, 84% of organizations say they are experiencing moderately to highly sophisticated voice attacks. That’s why spotting cues early is so important.

“One of the most alarming shifts we highlighted is how accessible voice cloning has become,” says Mike Pappas, CEO and co-founder of Modulate. “It no longer takes a sophisticated actor to launch effective voice fraud, voice cloning, or to create a fully synthetic voice. Anyone with minimal to no tech know-how can generate convincing clones of voices.”

Use AI to surface those clues while the call is happening so your teams can stop fraudsters in their tracks before approving any transactions. 

AI Agent Monitoring and AI Guardrails

Never deploy AI agents into production without enabling monitoring. Monitoring allows you to detect hallucinations, policy violations, and unintended answers in real-time so your team can take action.

Customer Experience Monitoring

Customer experience unravels in milliseconds, in a lengthy pause, a frustrated voice, or a missed opportunity to clarify. 

62% of companies are using AI technology to help with customer service. 33% claim reducing wait time as their goal. But wait times and CSAT scores are only as good as what occurs during the call, not after the call has ended.

AI monitoring picks up on those small cues of frustration or escalation and allows your agents to change course before it’s too late.

Compliance and Risk Monitoring

Sensitive disclosures are missed and risky language is spoken in a second. AI can alert your team to these compliance flags as they occur during the call and automatically create a bulletproof audit trail.

Monitoring Agent and Workforce Performance

Agents get confused and feel stressed. Calls don’t go well. AI can surface when these situations are happening so timely intervention can occur.

How Modulate Redefines AI Monitoring

Traditional call monitoring solutions apply AI technology to transcripts. Modulate’s Velma voice intelligence platform actually understands the voice signals themselves. We begin with the audio, and extract insights from the entire conversation in real-time.

Voice-Native AI

Velma accepts raw audio as its input. It doesn’t rely exclusively on transcripts. Velma detects tone, speed, hesitation and pressure directly from the audio. This means you’re learning from the actual conversation, not a plain-text version stripped of valuable context.

Real-Time Detection Within Seconds

Because Velma processes audio directly, it detects fraud, likelihood of escalation, and compliance events in real time. That means your team gets alerts within seconds of an incident occurring, so you can act while the call is still happening.

Behavioral, Acoustic, and Contextual Analysis

Velma doesn’t simply analyze specific words that were said. It’s analyzing behavior by linking behavior signals to acoustic events and important changes in context during the call. Because of this, Velma uncovers true intent.

Ensemble Model Approach

Velma leverages many models that learn continuously from your call environment. Instead of picking one model and hoping it works for every conversation, Velma dynamically chooses the models needed to keep scoring highly accurate without dragging response time down.

Built for 100% Call Coverage

Velma doesn’t cherry-pick a percentage of calls to analyze. Velma scales to your call volume to score 100% of your calls. Only by analyzing every call can you gain deep and actionable insights you can trust.

Real-Time Voice Intelligence for High-Stakes Decisions

Don’t leave anything to chance on high-value calls. Fraud can happen in seconds, and you only get one chance to earn a customer’s trust. Risk happens instantly, making a delayed response (even mere minutes later) entirely ineffective.

Modulate’s real-time voice intelligence platform gives you visibility at that critical moment. Flag fraud as it’s happening. Detect and respond to escalation before it’s too late. Provide your agents with the context they need to act with confidence every time. Watch Modulate in action or request a demo today and see how Velma powers modern AI call monitoring for every conversation.

Frequently Asked Questions

Which risks can AI monitoring identify?

AI monitoring can detect many types of risks, such as:

• Escalation and troubleshooting risks
• Fraud or social engineering attempts
• Deepfakes or synthetic voices being used
• AI hallucinations and unwanted bot responses
• Signals of churn or escalation
• Compliance violations and risky language or phrasing

Can AI monitoring identify fraud signals in real time?

Absolutely. When armed with the proper technology, AI monitoring can pick up fraud risk signals in real time while the conversation is taking place. Monitoring for behavioral or acoustic cues like urgency, hesitation or robotic speech can help platforms take action on suspicious activities prior to transaction approval.

How does voice-based AI monitoring differ from traditional platforms?

Voice-native AI monitoring provides your teams with more than words. By analyzing tone, cadence, pacing, and speech emotion/energy levels, teams can understand intent, stress, and deception.

Is AI monitoring only useful for contact centers?

No. Although call centers are one of the largest implementations of AI monitoring, it’s also used for:

• AI agents and copilots
• Identity verification and fraud prevention 
• Financial services and transaction approvals
• Healthcare and other highly regulated environments 

How can AI monitoring help with customer experience?

AI monitoring detects moments of frustration, confusion, or potential customer escalation during an interaction. Flagging these signals allows teams to react in real time to find a resolution more quickly, resulting in improved CX.

How does Modulate approach AI monitoring differently?

Modulate’s Velma is voice-native and powered by AI detection models that analyze behavioral and acoustic risk signals as they’re happening, not after the fact on a transcript. Modulate’s AI can detect risk, fraud, sentiment, and intent in seconds so teams can respond before it’s too late.