Lekha
लेखाthe ledger · the reckoning — Financial-Crime & Money-Laundering Detection
What if your investigators could skip ten thousand false alerts and open the fifty cases that are actually laundering money?
An anti-laundering team faces a flood of alerts, almost all false, and the real laundering hides in the noise — and the data is too sensitive to send anywhere.
The situation
A financial-intelligence unit
Millions of transactions a day, and a handful are layering dirty money through mule accounts. Lekha learns the shape of normal on the unit's own servers, then surfaces the few patterns that don't fit — structuring here, a sudden mule network there — each one ranked and backed by the trail, ready to act on.
How it plays out
Step by step
- 01
It learns 'normal'
Lekha builds a picture of ordinary transaction behaviour from the institution's own data — nothing sent out.
- 02
It spots the anomaly
Structuring, rapid layering and mule-account fan-out stand out against that baseline.
- 03
It ranks the leads
Instead of ten thousand alerts, investigators get a short, ranked list of the strongest cases.
- 04
It shows the trail
Each flag comes with the evidence behind it — explainable enough to put before a court.
The system itself
Under the bonnet
Reads transaction and account data, learns what normal looks like, then flags the structuring, layering and mule-network patterns that signal laundering — ranking leads by strength and showing the trail behind each, so investigators chase cases, not noise.
What it means for you
The bottom line
- ▸Cuts false alerts to a ranked shortlist
- ▸Surfaces structuring, layering & mule networks
- ▸Every flag is court-ready evidence
- ▸Sensitive financial data never leaves the institution