Fraud rarely looks suspicious alone.
Niridis connects financial signals across transactions, accounts and institutions to reveal patterns that isolated systems miss.
- Signals
- Cross-entity
- Scope
- Network-wide
- Output
- Investigation-ready
A transaction moves through the system. Nothing about it looks unusual.
A forensic view of financial activity, not a dashboard of alerts.
Most entities in the network stay quiet — ordinary accounts, devices and transactions behaving as expected. Niridis surfaces the clusters where relationships, not single events, warrant a closer look.
One transaction can look normal. The network around it may not.
Individually, each transaction clears standard review. Correlated across accounts, devices and timing, they describe a single coordinated relationship — the kind that isolated, per-transaction systems are not built to see.
Ask a question. Get a network, not a score.
Niridis surfaces the relationships behind a result and explains what it found in terms an investigator can verify — not a black box confidence number.
Each institution sees part of the picture. Together, more of the pattern becomes visible.
Fraud networks rarely stay inside one institution's walls. Niridis lets participating institutions contribute to and draw on shared risk signals — under data controls each institution defines — so a pattern invisible to any single view can still be recognized across the network.
Sees its own accounts and transactions in full. Everything outside its boundary stays out of view by default.
Sees its own accounts and transactions in full. Everything outside its boundary stays out of view by default.
Sees its own accounts and transactions in full. Everything outside its boundary stays out of view by default.
Signals such as pattern indicators and relationship flags move through the network layer under each institution's authorization. Raw account and transaction data is not broadcast to other participants.
Intelligence without unnecessary exposure.
Seeing a pattern across institutions does not require seeing everything about everyone. Niridis is built around limiting exposure at every step of that process.
Data minimization
Pattern detection runs on the signals it needs — timing, relationships, behavior — rather than requiring full raw records to move between systems.
Authorization
Access to shared intelligence is scoped and permissioned per institution. Nothing is exposed by default.
Secure collaboration
Institutions contribute to and benefit from network-level intelligence without exposing their underlying customer data to one another.
Controlled sharing
Each institution defines what leaves its boundary, in what form, and under what conditions it can be used.
Privacy-preserving architecture
The system is built so raw financial data stays close to its origin, and what travels across the network is limited to what pattern analysis requires.
Network-level visibility, institution-level control.
From a single event to an open investigation.
A card-not-present transaction clears standard, single-event review.
The receiving account shows activity consistent with a linked device.
A second account surfaces, sharing timing and device signals with the first.
Correlated timing, shared device and account relationships form a single cluster.
The cluster is routed to a case, with the full relationship graph attached.
Fraud intelligence for the systems you already use.
A single conceptual endpoint takes the context around a transaction and returns the signals, related entities and patterns behind it — ready to route into your existing case management or authorization flow.
{"transaction": {"id": "txn_9f21a","amount": 214.50,"currency": "USD","timestamp": "2026-08-13T09:44:27Z"},"account_ref": "acc_7c118","device_ref": "dev_0c1f2","context": "authorization"}
{"risk_signals": [{ "type": "device_reuse", "weight": "elevated" },{ "type": "timing_cluster", "weight": "moderate" }],"related_entities": [{ "type": "account", "ref": "acc_1198a" },{ "type": "device", "ref": "dev_0c1f2" }],"patterns": [{ "id": "pat_44b1", "label": "shared_device_cluster" }],"investigation_context": {"cluster_size": 4,"recommended_review": true}}
Built for anywhere financial relationships form.
Banks
Correlate activity across accounts and channels beyond what per-account monitoring sees alone.
Fintechs
Add network-level context to fast-moving product flows without slowing down legitimate users.
Payment processors
Surface relationships between merchants, devices and counterparties across the transaction stream.
Marketplaces
Identify coordinated buyer and seller behavior that spans multiple accounts and listings.
Insurance
Trace connections across claims, claimants and related parties over time.
Financial institutions
Contribute to and draw on shared risk signals under your own data controls.