How to review card fraud alerts with an AI agent

Let the agent contain and let analysts conclude. For each alert it assembles the context, asks the customer through an authenticated channel whether they made the transaction, and freezes the card at depth 3 if they say no or do not answer in time. Confirming fraud, deciding refunds and any permanent action on the account stay with an analyst.

What is card fraud alert review, and who owns it?

Your card fraud system scores every authorisation and flags the risky ones. Some are declined outright; others are approved and alerted, or held while the customer is asked. The alert queue is where analysts decide which flags are real. Speed matters: a compromised card can be used many times in the minutes after the first alert.

The fraud operations team owns the queue. Fraud strategy owns the rules and scores that create it. Disputes and chargebacks follow once fraud is confirmed. The agent works the front of the queue, where most alerts are resolved by a quick customer confirmation, and gives analysts a complete picture on the rest. The principle behind the design is that in fraud, an agent should contain, not conclude.

What are the steps?

1. Agent, depth 1: assembles the context. The alerted transaction, the customer's recent spending, merchant and location, device and channel, the strong authentication outcome, and earlier alerts.
2. Agent, depth 3: asks the customer. A message through the bank's authenticated app or an approved channel: "Did you make this payment?" with merchant, amount and time.
3. Agent, depth 3: records a "yes, it was me" given through strong authentication and closes the alert as customer-confirmed, under a rule fraud strategy has approved.
4. Agent, depth 3: freezes the card on a "no", or if no answer arrives within the set window while the risk score stays above a threshold.
5. Agent, depth 2: drafts the fraud case. Every transaction that may be fraudulent, the evidence for each, and a list of other cards or accounts that may be affected.
6. Person: confirms fraud. An analyst decides which transactions are fraud, whether to block permanently and reissue, and whether other products need action.
7. Person: decides on refunds and claims. Reimbursement and chargebacks follow the analyst's decision.
8. Agent, depth 1: watches for repeat patterns. The same merchant or device appearing across customers is passed to fraud strategy.

Which controls apply, and what evidence does each leave?

Authentication result, not conversation. A customer's confirmation counts only if it came through strong authentication in the bank's own channel. Evidence: the authentication result filed with the confirmation. A reply to a text message saying "yes" is a declared claim, and it cannot close an alert.

Freeze, not block. The agent's containment is a temporary freeze, which the customer or the bank can lift. Whether an agent can freeze a customer's card depends on exactly this distinction. Permanent blocks stay with the analyst.

Coaching signals. Some customers confirm transactions because a fraudster on the phone is telling them to. If the context shows signs of coaching, such as an active phone call during authentication or a new device, the agent does not close the alert on a "yes". It holds it for an analyst.

No limit changes. Raising a limit to let a declined payment through is a separate capability. Changing a customer's card spending limits is not part of alert review.

Every alert outcome filed. Closed, frozen, held for analyst: each is a verdict with the bound that decided.

What should you measure?

• Time from alert to customer contact, and to freeze where needed.
• Fraud on alerts the agent closed as customer-confirmed. The key safety measure. Each case goes into the next certification set.
• Alerts with no customer response, and outcomes after freeze.
• Analyst changes to the agent's fraud case. Missing transactions are the commonest gap.
• Genuine customers frozen. Friction is a cost too; watch complaints.

Where does it usually go wrong?

Treating "yes" as final. Social engineering targets exactly this step. A confirmation under coaching is not a confirmation. Build the coaching signals into the bounds, not just the brief.

Freezing too narrowly. The agent freezes the alerted card, but the same details are linked to a digital wallet token or a second card. The fraud case must list every linked instrument for the analyst.

Alert fatigue moved, not removed. If the agent passes every alert it cannot close to analysts with a long write-up, the queue is just as long. Keep cases short, ranked and focused on the decision.

Card fraud mixed with application fraud. A card opened under a false identity is a different problem from a stolen card. The approach in reviewing loan applications for application fraud is closer to that case.

The general bounds for bank agents are in AI agents in banking.

Where Pryme fits

In Pryme, the customer confirmation, the alert closure and the freeze are three separate capabilities, each certified through Attest on your own past alerts, including cases where customers were coached. The authentication result arrives as sealed data, which is why a declared "yes" can never close an alert, and each verdict names the bound that decided.