To close faster, agents promised things they shouldn't and skipped the terms they were legally required to read. The quality team could only listen to a tiny sample, so it almost never caught them, and when a customer complained it came down to one person's word against another's. The fines arrived anyway.
The sector is tightly regulated on what an agent must tell a customer before a sale, and the firm had taken heavy fines and class actions because agents skipped the required disclosures or promised things the product could not do, all in the rush to close.
Oversight was a rounding error. The quality team could listen to roughly 1% of millions of monthly calls, a sample far too small to catch a pattern or to identify the agents who cut the same corner every day, so the rules existed but nothing enforced them.
And every dispute became unwinnable. With no reliable record of what was actually said, a customer complaint turned into one person's word against another's, and the recordings that could have settled it sat on the servers as dark data no one had the hours to hear.
Transcribe the whole floor in real time, check each call against the rules, and put a manager on the ones that need one.
A speech-to-text and language engine listens to, transcribes and analyses every inbound and outbound call as it happens, so oversight covers the whole operation instead of a 1% sample that never reflected what was really being said.
A compliance rule engine scans each transcript for the disclosures the law requires and raises an alert the moment an agent takes payment without reading the cancellation terms, turning a rule on paper into a check on every single call.
Voice-sentiment analysis picks up tension and anger in the customer's tone and escalates the call to a duty manager while it is still live, so a situation can be rescued before it ends badly rather than reviewed after.
Instead of listening blind, the quality team now works only the calls the algorithm marked as non-compliant or unusual, which turns a hopeless sampling exercise into focused, high-yield review.
The archive of call recordings that used to sit unread becomes a live source of evidence and coaching, so a disputed sale has a record and a repeat offender has a paper trail.
The engine surfaces the breach and the risk; the discipline, the coaching and the customer remedy stay with a supervisor, so enforcement is consistent without handing judgement to a model.
Full compliance coverage, exposure closed, and a quality team that finally aims at the right calls.
Regulatory compliance monitoring reached 100% of calls, which closed the exposure that had driven the mis-selling fines and class actions and drove that risk to zero. What used to depend on catching an agent in a 1% sample is now checked on every call, every time.
The quality team moved from blind random listening to working only the calls the algorithm flagged, a dramatic gain in yield. And because sentiment analysis spots frustration in the moment, the firm now identifies customers at high risk of leaving early enough to keep them.
You cannot police a floor by sampling it; the value is reading 100% of calls against the rules and putting a human on the exceptions the model surfaces. It is the same voice-analytics discipline behind our speech-analytics work, aimed at regulatory compliance rather than customer experience.
A telecom was blind to why subscribers churned. AI that reads 100% of interactions surfaced the root causes, cut repeat contacts and lifted satisfaction across the board.
Read the case → AML onboarding · FinanceManual screening was slow and inconsistent, and the risk slipped through. Automated checks cleared the honest customers fast and held the ones that needed a human eye.
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