Catch fraud by comparing against confirmed cases, without moving your data anywhere.

Banks, fintechs, and insurers need to know when a transaction or claim looks suspiciously like known fraud patterns. We search for that similarity in the same database where your customer data already lives, not in a separate service.

Let's talk about your case

Here's what changes against the typical stack:

Usual stackWith Gorigami

Storing the behavior embeddingA vector store separate from the data warehouseThe same table as the rest of your transactional data

Finding similar transactionsA separate ML pipeline with its own infrastructureDirect SEARCH / COSINE_SIM over the table

Explaining why something was flaggedHard to trace across separate systemsLineage and query plan visible with EXPLAIN

The mechanism and the boundary, together:

How we implement it

We turn transactional behavior (amount, frequency, location, device) into a Vector column; with CREATE VECTOR INDEX + SEARCH we find transactions similar to already-confirmed fraud cases, combinable with your traditional business rules in the same WHERE.

What we don't solve

We don't replace a trained fraud scoring model (an XGBoost, a neural network): we solve retrieval of similar cases, not the final automated decision. You'll still need a traditional ML model or business rules on top of it.

Who this is for: Fintech · Banking · Insurance · E-commerce: checkout fraud · Payment platforms

Frequently asked questions

Does this replace our current rules engine?

No. It combines with it in the same query: your business rules and the similarity search run together.

Do we need a data scientist to use it?

For the final scoring model, yes. For finding transactions similar to already-confirmed fraud cases, no.

Does our customer data leave our infrastructure?

No. It runs embedded wherever you decide: inside your own infrastructure if your regulation requires it.