Another alert, and the same customer again
Your transaction monitoring rules run over payments and card activity and raise alerts: a large incoming payment, a burst of transfers to new payees, activity that does not fit what the customer said they would use the account for. Each alert lands as a row in a spreadsheet or a list in your monitoring tool.
An analyst opens one, then goes to the admin panel to look at the customer, then to the transactions screen, then to onboarding records to remind themselves what the customer said. Most alerts turn out to be ordinary once the context is clear. The next alert is the same customer, triggered by the next day's activity. It gets reviewed from scratch.
Where the review time goes
The rules are rarely the whole problem. The time goes on gathering context and repeating work.
- Each alert is reviewed on its own, even when several relate to one customer and one pattern.
- Context sits in other tools, so every alert starts with several lookups.
- Outcomes are recorded in free text, so nobody can see which rules mostly produce alerts that are cleared.
- Escalation to a senior reviewer happens by message, with no link to the case.
- Nobody can see how old the oldest unreviewed alert is.
Which rules you run and what thresholds you use is for your own policy, set with your compliance lead and advisers. We do not change them. We make reviewing their output faster and better recorded.
When alerts outrun reviewers
An alert backlog is uncomfortable to report and hard to clear. The pressure to get through the list encourages quick, thin reviews. Good analysts spend their time on lookups rather than judgement. Customers whose accounts are restricted while a review waits contact support, who cannot tell them anything useful.
A review tool built for context
What we build takes alerts from your existing monitoring tool or rules and reorganises how they are worked.
- Alerts are pulled from your monitoring system or rules engine through its API or exports, as they are raised.
- Open alerts for the same customer are grouped into one case, so the analyst reviews the pattern once.
- The case loads the customer's onboarding answers, expected activity, recent transactions, prior alerts and prior outcomes on one screen.
- If you want it, an AI model can draft a plain summary of the activity for the analyst to check, labelled as a draft.
- Outcomes are recorded from a list you define, with a note, and escalations go to a senior queue with the full case.
- Reports show volume and age by rule, and how each rule's alerts were resolved, so your team has the facts when reviewing rule tuning.
| Before | After |
|---|---|
| One alert, one review | One case per customer pattern |
| Context gathered from several tools | Context loaded on the case |
| Free text outcome in a sheet | Outcome from your list, plus notes |
| Escalation by message | Escalation queue with the full case |
| No view of rule noise | Report of outcomes by rule for your own review |
The analyst's day, rearranged
Analysts open a queue of cases sorted by age and priority. Each case already has what they would otherwise look up. Related alerts are dealt with together. The team lead sees the backlog and its age in real time, and the compliance lead has consistent outcome data to bring to rule reviews.
Signs your alert review needs this
- Alerts are reviewed from a spreadsheet or a basic list.
- The same customer raises several alerts reviewed separately.
- Analysts open multiple tools to gather context.
- Outcomes are free text and cannot be counted by rule.
- Nobody can say how old the oldest open alert is.