Interactive prototype · Trust & fraud

Which signals catch the fraud?

The thesis of the case study, made clickable: no single signal catches modern fraud — fusion does, and only fusion earns the customer's trust to act.

Pick a real attack. Then switch on the signals the platform can fuse. Watch a threat move from “missed, or flagged too late to matter” to “caught in time to stop the money.” The gap between those two states is the whole product.

Signal-fusion sandboxPick an attack, fuse signals, watch the outcome change.

The attack in progress

Signals the platform fuses

Telecom signalRecent SIM change, port, device swap on the line.Decisive for this attack Behavioral analyticsLogin rhythm, navigation, transfer patterns vs the real customer.Decisive for this attack Voice biometricVoiceprint match & synthetic-speech / deepfake detection.Decisive for this attack Federated cross-institution learningFraud patterns seen at peer banks, shared without sharing data.Decisive for this attack

What happens to the customer

Attack

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Fraud caught in time

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Legit customers wrongly flagged

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Time from signal to action

Signals fusedbaseline

What’s modelled and what’s fake here, honestly. The four attacks are real fraud patterns; the outcomes are the argument, not a live fraud engine, and the numbers are directional targets rather than a specific institution’s production metrics. The point is the mechanism: any one signal alone leaves a gap an attacker walks through, and it’s the fusion — plus doing it fast enough and quietly enough that real customers aren’t punished — that turns a detector into something a bank will actually let act on its customers.