The Missing Piece in Data Security

The problem

Financial crime does not stay inside one bank. Mule networks, fraud rings and sanctioned parties deliberately spread activity across institutions, because no single bank can see the whole picture. Your analysts see only your side of it. A customer who looks clean to you may already be flagged at the bank down the road, and today you have no way to know. Catching this needs a view across institutions. But you cannot share customer data, and you cannot pool it.

Why the usual approaches fall short

Pooling was tried and shut down. The Netherlands built TMNL, a shared transaction database across its largest banks, and regulators wound it down in 2024, calling it mass surveillance with no legal basis. Red-flag-only utilities like Singapore's COSMIC do work, but they are closed clubs of a few large banks, and most institutions are not inside them. And ordinary privacy tooling decrypts your data inside someone else's server, which no compliance team will accept for financial crime data.

What we do

We run the check on data that never leaves its owner and is never decrypted. Each side encrypts its own records on its own machine, with its own key. The computation runs on the encrypted data, on infrastructure that never holds plaintext, and only the result comes back. Only the owner can read it. You collaborate on financial crime without anyone, including us, ever seeing the underlying records.

What you can do

• ›Screen your customer base against a partner's confirmed mule list, or against a sanctions or watchlist, and receive only the matches. No browsing, no fishing, just the hits.
• ›Count how many flagged or high-risk customers you share with a partner institution, learning the number and the overlap and nothing else about their book.
• ›Run it on a schedule, so newly identified mules and newly sanctioned parties surface as soon as a partner flags them.
• ›Start one-sided, screening your customers against a single shared list, with no second institution needed to get value on day one.

The problem

The most valuable medical questions need data from many sites. Does this treatment work better for a subgroup. How many patients does our trial share with another registry. What is the real outcome rate across a region. Answering them means combining data that lives in separate hospitals. But patient records cannot leave the institution that holds them, consent and law forbid it, and the data is far too sensitive to hand to anyone, even briefly.

Why the usual approaches fall short

Sending records to a central party is a non-starter, legally and ethically. De-identification leaks and is often reversible. And the privacy tooling that decrypts data inside a secure enclave still asks you to trust that enclave, and whoever runs it, with raw patient data, which an ethics board and a data protection officer will not accept.

What we do

Each site keeps its records encrypted, on its own systems, under its own key. We compute the shared result on the encrypted data, and only the aggregate comes back. No patient record is ever pooled, moved, or decrypted by anyone outside its own institution.

What you can do

• ›Compute a shared statistic, such as an average outcome or a rate, across several sites, as if the data were in one place.
• ›Find how many patients or subjects two registries have in common, learning only the overlap, never the identities.
• ›Let a sponsor or a consortium get a cross-site number without ever receiving the underlying records.
• ›Keep every record inside its own institution, encrypted end to end, with the key never leaving the building.

The problem

Attackers reuse the same mule accounts, devices, and infrastructure across many companies. Fraud rings hit one fintech after another with the same identities. You would catch far more if you could compare notes with a peer or an industry partner. But you cannot. Sharing your indicators means exposing your customers, your full block list, and the detection logic that took you years to build. So everyone defends alone, and the same bad actors keep working their way down the list.

Why the usual approaches fall short

Industry sharing groups move at the speed of email and legal review, and members hold back their best signals for exactly the reasons above. Handing your list to a central aggregator means trusting them with your customers and your secrets. And the usual privacy tooling decrypts your data inside someone's server, which defeats the point when the data is your detection footprint.

What we do

Both sides keep their lists encrypted, on their own systems. We match them on the encrypted data and return only what they have in common. Neither side sees the other's full list, and nothing is decrypted by anyone but the owner. You get the shared signal without giving away your data or your edge.

What you can do

• ›Check which compromised accounts, devices, IPs or hashes you share with a partner, and act on just those.
• ›Match your customers against a shared abuse, mule or fraud list, returning only the hits.
• ›Run it continuously, so a signal a partner adds today reaches you the moment it appears.
• ›Reveal only the overlap, never your full list and never your detection logic.