Fraud and alert queue review, worked around the clock

Sweat AI is an AI-native BPO for banks and fintechs, and fraud and alert queues are one of the back-office workflows we start with, alongside onboarding review. Our analysts, working with AI tools, triage and investigate the alerts your systems generate, write each one up with the evidence behind it and recommend a disposition. Your team decides what happens next.

Why alert queues back up

Transaction-monitoring and fraud systems are built to over-alert. A rule that catches the pattern you care about also catches ordinary customers who happen to look similar that week. The result is familiar: a queue where most alerts close after a short look, a few need real investigation, and the analyst cannot tell which is which until they open it.

Queues also have deadlines. For US banks, a suspicious activity report is due no later than 30 calendar days after the initial detection of facts that may support one, extendable by another 30 days only to identify a suspect when none was identified at detection (31 CFR 1020.320). An alert that sits unopened for a week has used a week of that clock. Fraud alerts are shorter still: if an account takeover is under way, the useful window can be minutes.

What we review

Transaction-monitoring alerts

• The alert, the rule or scenario that fired and the transactions behind it.
• The customer's expected activity from onboarding and any later reviews, compared with what actually happened. Your program's ongoing due diligence already rests on this comparison: bank AML programs must include understanding the nature and purpose of customer relationships and ongoing monitoring to identify and report suspicious transactions (31 CFR 1020.210).
• Counterparties, geographies and timing, and whether the pattern has an ordinary explanation in the customer's business.
• Prior alerts and cases on the same customer or counterparties.

Fraud alerts

• Alerts from your fraud rules or vendor models on payments, card activity, transfers and account events.
• The sequence of events around the alert: logins, device and contact changes, beneficiary additions, first-time payees.
• Whether the customer, the counterparty or both look like the victim, and what that means for next steps.

Account-takeover signals

• Changes to credentials, contact details or devices followed by unusual payments or withdrawals.
• Session and device evidence your systems make available, laid out as a timeline.
• A clear statement of what points to takeover and what does not, so your team can decide whether to lock, contact or release.

Money mule signals

• Accounts that receive funds from many unrelated senders and move them out quickly.
• New accounts whose activity does not match their stated purpose, occupation or business.
• Links between accounts through shared identifiers, devices or counterparties, where your data shows them.
• For business accounts, the onboarding file compared with current behavior; see business account review for banks.

What you get back: the case write-up

Each alert comes back as a write-up your investigator or approver can act on without redoing the work:

1. Summary. The alert, the customer, the period reviewed and the recommended disposition in two or three sentences.
2. Activity reviewed. The transactions and events examined, with the date range and data sources.
3. Customer context. What the customer said they would do, what their profile and history show, and prior alerts.
4. Analysis. The facts that support suspicion and the facts that weaken it, each tied to the record it comes from. Where something could not be checked, it is listed as a gap.
5. Recommended disposition. For example: close with rationale, escalate for investigation, consider a SAR, request information from the customer, or act now on suspected fraud. With the reason.
6. Open questions. Anything your team needs to find out that we could not, such as information held only by another department.

Write-ups follow your templates if you have them, so they drop into your case management tool without reformatting.

What stays with you

Alert review sits inside your AML and fraud programs. We do the investigative work; the program decisions are yours:

• Closing or escalating the alert.
• Restricting, freezing or closing an account, and reversing or holding payments.
• Deciding whether to file a suspicious activity report, and filing it.
• Contacting the customer, unless you authorize us to prepare or send a specific message.
• Changing rules, thresholds or models.

Our Terms of Service state that every decision and every adverse action remains the customer's.

How AI is used

Analysts use AI tools to pull together the alert, the transactions and the customer record, draft the timeline, check counterparties against public sources and write the first version of the narrative. The analyst reviews each statement against the underlying record before the write-up goes to you. We record what was read and when, so you can see the basis for every line.

Coverage and escalation

The team works 24/7. Alerts that land overnight or over a weekend are worked before your team logs in, which keeps them from eating into your deadlines. Cases that meet your urgent criteria (for example, a takeover in progress or funds about to leave to a known mule pattern) go to your named escalation contact immediately. More on the model in 24/7 onboarding review; the same coverage applies here.

Where it fits

• Banks and credit unions with small BSA and fraud teams and growing alert volumes.
• Neobanks and BaaS programs where sponsor banks review how alerts are handled.
• Payments platforms monitoring merchants after onboarding.
• Stablecoin and crypto platforms with round-the-clock activity.

Talk to us about your alert queue

Tell us which alerts pile up and when, and we will show you how we would work them and what the write-ups would look like. Pricing depends on volume, alert types and coverage; talk to us and we will walk through it. The full flow is on how it works.