If your fraud pipeline uses embedding similarity, an attacker who can modify those embeddings can steer the model. Mnemo verifies that every embedding feeding a decision came from your pipeline.
Only if your fraud pipeline uses vector embeddings. Tabular fraud stacks are out of scope.
02Why it matters
Fraud models depend on the data they receive.
Behavioral embedding forgery. An attacker inserts synthetic normal behavior to shadow real fraud patterns. Similarity flags nothing. Precedent poisoning. For fraud copilots, planted embeddings cause misleading precedent retrieval and silently shift analyst decisions. Cross-account contamination. On marketplace and platform fraud, one account's signals can leak into another's embedding space.
03What this does
Verifiable evidence under every fraud decision.
Every embedding carries a UID identifying its source and account. The fraud model calls verify() before similarity. Unverified embeddings are excluded or flagged.
04Scope
What Mnemo does not do.
Does not replace your system
Does not score risk
Does not handle case management
Does not apply to tabular fraud stacks
Mnemo focuses on one layer: attaching identity to embeddings and verifying them when they are used.
05Who this is for
Head of Fraud Engineering. VP Risk at fintech, marketplace, and e-commerce. Head of Trust and Safety. CRO at insurance carriers.
06Why teams adopt it
Customer-facing fraud decisioning under regulatory scrutiny.
Multi-customer fraud signal isolation for marketplaces.
Fraud copilots requiring precedent provenance.
Embedding-based behavioral fraud model validation under MRM.
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