Every fraud leaves a shape.

Modus reads your entire network - entities, devices, money, time - and surfaces the shape of financial crime while it is still forming.

PREPARE FOR DESCENT

One account. A whole world underneath.

Keep scrolling. You are about to experience the journey of a single customer.

Hang tight, until the entire story of a fraud reveals itself around you.

Scroll to ride
STOP 00 · THE WHOLE GRAPH

The entire network, at once.

Every account, device, company and payout Modus is holding in view - one continuous graph. Keep scrolling and the camera falls into it, stop by stop, until a single customer fills the screen.

Full network8 stops aheadDepth ×1
01 — Discover / The problem

Risk systems tend to read one row at a time.A mule looks like a customer.A shell looks like a business.Line by line, everything clears.Only the connections give it away.

Row by rowNothing to flag

EntityCounterpartyAmount
User AMerchant 01₹4.2L
User BMerchant 01₹3.9L
User CZenith LLP₹5.1L
User DAtlas Prop.₹2.8L
User ZMerchant 01₹4.0L

Every row passes its own check. Same amounts, different names - no single line is suspicious enough to decline.

As a graphOne coordinated ring

Shared device. Shared address. Funds circling back to a single beneficiary. Five “unrelated” users are one ring.

02 — Connect / The engine

IntroducingModus Graph Intelligence

A graph-native AI system for risk & compliance. Knowledge graphs, graph neural networks, forensic money-flow tracing - running continuously across every entity, account and transaction you hold.

◆ Graph-native, not rules◆ Ring-level detection◆ Traced to the beneficiary
03 — Map / Node explorer

Fly through the graph.

Every dot below is a real thing in your data - a person, a device, a company, an account. Step through them in order to see how Modus turns scattered records into one traceable network.

Fly through the graph.Live
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ENTITY · PERSON

Customer

One customer, one account - on its own, indistinguishable from a million others.

Customer - One customer, one account - on its own, indistinguishable from a million others.
Analyze — four ways to read the graph
METHOD 01

Knowledge Graphs

Link fraud entities & streamline investigations

Your data leaves the table and becomes a graph. Every customer, phone, address and merchant is a node — so shared attributes that read as coincidence in a spreadsheet surface as hard linkage an investigator can follow in one click.

Tabular → graphShared PII1-click investigation
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Get started

Step into the network.

See Modus run against a live web of connected entities — and watch early-warning signals surface before exposure becomes loss.