Fraud alerts prepared from the systems you already run

The question this file answersHow much of my fraud officer's day is spent rebuilding context that already exists somewhere in our systems?

Fits: banks, payment firms and fintechs where compliance files are still assembled by hand.

Typical day

What the desk looks like today

Typical pattern, not a measured desk; the finance playbook covers KYC onboarding, not alert handling, and gives no alert volume. List hits, unusual payments and noise share one queue; for each, an officer pulls customer record, history and list entry. It hurts on Monday, after a weekend of alerts.

What changes

What Monday looks like after

By the time the officer logs in, each open alert already has its customer history and list evidence attached, so the morning starts with the decisions rather than the searching. Every closure still carries the officer's name, and you can see per alert what was assembled and what was decided.

Typical, not a measured client result. Every figure here comes from the playbook source named below.

One file

the same assembly build as KYC onboarding — no published figure for fraud alerts

Before: an officer rebuilds context for every alert from scratch. After: the context arrives assembled and logged; the yes or no is still the officer's. No percentage — the KYC figure is about onboarding, on the parent file.

No published figure for this desk. The range lives on the parent file: KYC: documents scored before the officer opens the file →

How this file is built

McKinsey KYC study (2023) measures onboarding case time, not alert handling; that 50–70% stays on the KYC file and this desk carries no percentage. Not a fraud-catch rate.

What we install

What we put in front of the systems you already run

Your core system and screening tools stay — Temenos, FIS, Finastra, World-Check or the ones you run. This is the same file-assembly build as our KYC onboarding, pointed at alerts:

ordinary postings that trip no rule go through with a logged reason
for anything that does — a list hit, a new beneficiary, an odd pattern — the customer record, recent activity and the matching list entry are gathered into one prepared file
that file goes to the officer, with every step the model took written down.

No alert is closed by the model.

What stays human — and what this will not do

The decision. The suspicious activity report. The customer call. Questions from the regulator. No model files the report.

Not a fraud-catch rate.

What can go wrong — and what we do about it

If customer records are incomplete or duplicated in the core system, the prepared file is incomplete too and the officer is back to searching — cleaning that is audit work first. Regulators expect a reasoned trail for every decision, so nothing here closes an alert or files a report; the boundary is assembly, and the judgement stays entirely human.

What it costs to get there

The path: free 60-second estimate → free 20-minute review → paid audit of this one process (€1.5–3K, typically two weeks) → pilot with your people in the loop (€10–20K, weeks, not quarters). No transformation programme. Prices are public, on the services page →

Scoped in the audit — the playbook has no estimate for this exact desk.

This is about you if…

Does an officer still gather customer history and list evidence by hand for every alert?
Do your alerts come from a monitoring or screening tool that can be read, not only a shared inbox?

What does this mean in euros?

That depends on your volumes and wage costs — this page will not invent the number. The free 60-second estimate runs that calculation from your answers, with every multiplier sourced.

Get your free savings estimate 60 seconds · no sales call Or write first → Map a finance process like this one — free, 60 seconds →

Not a named Aperanda client. Process file · Finance.

Short process file. Same build as its parent file; the playbook has no separate volume or benchmark for this desk.

All process files