Fraud caught while the call is still live.
For a digital bank in Argentina, we put fraud-pattern detection on the collections and support lines — scoring every call in real time, flagging suspicious patterns, and alerting the risk team the moment they appear. Patterns no human could catch by sampling recordings by hand.
Thousands of calls a day, reviewed by sampling a handful.
A digital bank ran its collections and support operation over the phone — thousands of conversations every day, each one a place where fraud could surface. The only control was a QA team listening to a tiny sample of recordings after the fact, days later, with no realistic way to scale.
By the time a suspicious pattern showed up in that sample — if it ever did — the call was long over and the damage already done. The patterns that mattered lived in the calls nobody had time to listen to. Fraud doesn't announce itself in the 1% you happen to review.
A detection layer that listens to every call.
A real-time scoring engine wired into the live collections and support lines — reading every conversation, scoring it for fraud risk, and pushing alerts straight to the people who act on them.
Every call, as it happens
Each conversation is scored for fraud risk while it's still on the line — not sampled, not reviewed days later. Coverage went from a handful of recordings to one hundred percent of calls.
Signals no human would catch
The models surface the combinations of language, behavior, and account signals that mark a call as suspicious — patterns spread across thousands of conversations that no sampling-by-hand could ever connect.
Straight to the risk team
When a call crosses the threshold, the risk team is notified the moment it happens — alerts land directly in their messaging tools, so action starts while the context is fresh, not after the next QA cycle.
Flags worth acting on
We tuned the scoring against the bank's own labeled cases until it reached 91% detection precision — high enough that the risk team trusts the flags instead of drowning in false alarms.
Embedded with risk, then handed over.
Embed
We sat with the risk and QA teams to learn what fraud actually looks like on their lines, and went through their labeled cases to define what a flag should mean.
Build
We built the scoring engine against live call audio, then tuned it on the bank's own data until the precision was high enough for risk to act on every flag.
Wire in
We connected detection to the lines and to the team's messaging tools, so scoring runs on every call and alerts reach risk the instant a pattern appears.
Transfer
The system, the thresholds, and the playbook run in the bank's own hands. The risk team owns the detection layer — we stay available, not necessary.
It paid for itself on the fraud it caught.
Fraud detection moved from a slow, after-the-fact sample to a control running on every single call. The risk team stopped guessing which 1% to listen to and started getting flagged the moment a suspicious pattern showed up — with the precision to trust what landed in their inbox.
The detection layer caught patterns the bank had no way of seeing before, and the value it returned covered the cost of the engagement on its own. The system runs as part of the bank's Voice AI Infrastructure — owned by their team, not rented from us.
"The fraud detection alone paid for the service. We caught patterns we never would have seen listening by hand."
What's hiding in the calls you never review?
A 30-minute call. We'll map where fraud and risk are slipping past your sampling, and what real-time detection on your lines would actually catch — built on Voice AI Infrastructure your team owns.