SolutionsBanking & finance · AI

Spotting money laundering in millions of payments

Financial sector

A system that helps financial supervisors spot money laundering — when criminal money is hidden by moving it through many accounts. It shows not only what looks suspicious, but why.

  • ~60 %Less time per investigation
  • MillionsPayments checked every month
  • ExplainedEvery warning shows its reason
  • In-houseNo data leaves the institution

In short

For
Banks and supervisors who must find money laundering
The problem
Warnings come by the thousand, and most are false alarms.
What it does
Fewer, clearer warnings — each with the reason attached — and about 60 % less time per case.
Used by
The financial sector

Who it is for

For banks and supervisors who must find money laundering

The problems it solves

  1. Warnings come by the thousand, and most are false alarms.

  2. Computer warnings often cannot explain why something is suspicious.

  3. Each bank only sees its own accounts, not the whole picture.

Built for
  • Banks and payment institutions
  • Compliance and financial-crime teams
  • Regulators and financial intelligence units

How it works

How the system spots and explains suspicious payments

Money laundering is hard to see in one bank account, because the money is split and moved through many. The system looks at all the payments together, like a map of who pays whom, finds patterns that look like laundering, and explains each warning so an investigator can act on it.

  1. Draw the map of payments

    Every payment becomes a line between two accounts, so unusual patterns become visible.

  2. Check known tricks

    Experts write down the patterns they already know; the system looks for them.

  3. Find new patterns

    The computer learns to spot suspicious shapes the rules do not cover.

  4. Learn from every decision

    Each investigator’s verdict makes the next warnings better.

The supervision dashboard, with figures hidden
The supervisor sees why each payment was flagged. Figures hidden.

What changed

Before and after

Before

Thousands of warnings a month that investigators could neither confirm nor explain.

After

Fewer, clearer warnings — each with the reason attached — and about 60 % less time per case.

Used by
The financial sector
Looks at
Millions of payments a month
Shows
Why each warning was raised
Data
Stays inside the institution

Talk to an engineer

30 minutes about your case, with the engineers who built this. No slides.

Prefer email? info@solvesall.com