Fraud detection
Catch synthetic identities, mule accounts and coordinated rings before money moves. Behavioural, device and network signals resolved in one call.
Powering fraud prevention, credit intelligence and digital trust for modern businesses.
Trusted by risk and growth teams across regulated industries
Products
Start with the check you need today. Add the rest without touching your integration — every product resolves into the same decision object.
Catch synthetic identities, mule accounts and coordinated rings before money moves. Behavioural, device and network signals resolved in one call.
Write policy once, run it on every application. Approve, refer or decline with a full reason trail.
Read, validate and tamper-check documents in 40+ formats with field-level confidence.
Fingerprint every session. Detect emulators, rooted devices, SIM swaps and recycled numbers before onboarding completes.
Match a selfie to an ID photo with passive liveness. Deepfake and replay attacks flagged in the same response.
Infer income bands from bank, employment and alternate data — without asking for another payslip.
Separate real installs from bots, click farms and attribution fraud before you pay for them.
Onboard individuals and businesses, then screen against global sanctions, PEP and adverse media lists on a schedule you set.
How it works
Resolve the person or business behind the application from documents, biometrics and government sources.
Enrich with device, email, mobile, bank and behavioural signals, then weigh each one against your policy.
Models resolve the signals into a single risk score, with the reasons that drove it ranked by contribution.
Return approve, review or decline — logged, versioned and exportable for your next audit.
Decision returned in 112 ms with every input, model version and reason code written to the audit log.
Developers
One REST endpoint, typed SDKs and a sandbox that returns realistic fraud patterns. Key to first production call in an afternoon.
// one call, every signalconst decision = await iscoreit.risk.score({ reference_id: "app_9F2KQ4", identity: { pan: "ABCDE1234F", aadhaar: "xxxx-xxxx-4417", mobile: "+91 98••• ••821" }, document: { type: "passport", file: buffer }, selfie: { file: selfie, liveness: true }, device: { fingerprint: req.device_id }, policy: "lending.retail.v4"});
{ "outcome": "approve", "risk_score": 94, "identity": { "pan": "verified", "face_match": 98.6, "liveness": "passed" }, "signals": [ { "code": "device.clean", "w": 0.18 }, { "code": "income.confirmed", "w": 0.24 } ], "latency_ms": 112}
AI intelligence
Every call arrives as raw inputs — documents, biometrics, device, email, mobile, bank and behavioural history.
Hundreds of derived features are computed per application, normalised across geographies and product types.
Gradient-boosted models vote alongside graph and sequence models, retrained weekly on realised outcomes.
One score, one outcome, and the ranked reasons behind it — the same contract on every route.
Industries
Security & compliance
Encrypted in transit and at rest, isolated per tenant, retained only as long as your policy says. Every decision is logged with its inputs, model version and reason codes.
Customers
Tell us what you're onboarding and we'll run the decision flow on your own data in a 30-minute working session.