Catch the mule, the deepfake, and the synthetic identity before signup completes.
MNRL, face liveness, device intelligence, bank ownership match, PEP / sanctions / adverse-media — chained into one fraud decisioning call. Each signal scored, then combined.
One Workflow, Every Fraud Vector
Mule accounts, deepfake selfies, recycled phone numbers, synthetic identities, sanctions hits — each leaves a different signature. Deepvue runs the full signal stack in one parallel call and returns a combined score.
Three reasons fraud detection stops being an afterthought.
Fraud at signup breaks across three axes — recall (catch the bad actor), latency (don't slow the funnel), and explainability (defend the decision). Deepvue's fraud chain is built around all three.
Single-vector fraud checks miss. Mules pass face match; deepfakes pass MNRL; synthetic IDs pass each vendor in isolation. Combined signal scoring catches what individual checks don't.
Sequential fraud checks add minutes; parallel chains run in under 2 seconds. Real-time scoring at signup beats batch screening hours later when the mule has already cashed out.
When a customer disputes a rejection or your fraud team A/B tests thresholds, the response carries per-signal scores — not a black-box verdict. Every decision is reproducible and audit-ready.
Most teams treat fraud detection as a separate vendor — face vendor, device vendor, sanctions vendor, MNRL vendor. The signals never combine. Deepvue collapses them into one chain with combined scoring, so a deepfake selfie + matching mule bank account is caught even if neither would fail in isolation.
Why single-vector fraud checks miss the mule.
If your funnel is leaking 25%+ between "Apply" and "Approved" — or your audit team is rebuilding decision history from logs — here's where the breakage usually starts.
The fraud signal stack — six primitives, one combined score.
Every fraud signal that catches signup-time bad actors, wired to call in parallel — combined into a single score with per-signal contribution shown.
How the fraud chain runs.
From a signup hitting submit to a fraud verdict back to your stack — sub-2-second median, parallel signal scoring.
End-to-end median: under 2 seconds. Run a sample fraud cohort through both stacks in a 15-min walkthrough.
A fraud scoring call, end-to-end.
What a single fraud chain call looks like — parallel signals, combined score, replayable trace.
// All parallel — sub-2-second median 1. POST /v1/fraud/mnrl-mobile-intel 2. POST /v1/fraud/face-liveness-deepfake 3. POST /v1/fraud/device-intel 4. POST /v1/fraud/bank-ownership 5. POST /v1/fraud/pan-aadhaar-link 6. POST /v1/screening/pep-sanctions 7. POST /v1/fraud/score
{
"signup_id": "sg_8a4f2b9e",
"verdict": "CLEAR",
"fraud_score": 0.08,
"latency_ms": 1412,
"signals": {
"mnrl": "0.02",
"face_liveness": "0.04",
"device_intel": "0.11",
"bank_ownership": "0.05",
"sanctions": "0.00"
},
"trace_id": "trc_2026_04_29_17_42",
"ruleset_version": "v3.4",
"data_residency": "IN"
} Compliance map for fraud-chain decisioning.
The frameworks fraud-decision pipelines get challenged on — DPDP, sanctions, IT Act, sectoral fraud-prevention rules.
Where fraud signals get missed — and where Deepvue catches them.
From signup submission to first chargeback — the missed signal usually fits one of these five patterns.
Wire it in over a coffee.
Most teams drop the Deepvue fraud chain into their signup flow in under a week. One sandbox key, one chained endpoint, webhooks back to your existing fraud / risk stack.
What you get out of the box.
Capabilities every fraud-decision pipeline needs — without stitching together five fraud vendors.
Five fraud vectors, one combined score.
The signal mix per fraud vector — what catches each, and why a combined score outperforms single-vendor verdicts.
Built for fraud-decision defensibility.
DPDP, IT Act, RBI fraud-prevention norms, dispute redressal — fraud rejects need to be defendable. Deepvue's per-signal trace satisfies all of them out of the box.
Deepvue is not a regulator and does not represent itself as RBI, SEBI, UIDAI, or any government authority. Customers retain full responsibility for their fraud thresholds, dispute resolution, and rejection decisions. Deepvue provides the signal infrastructure and replayable trace; the fraud decisioning is yours.
All API interactions are protected using encryption, role-based access controls, and audit logging.
Volume pricing per fraud decision.
Fraud-chain bundles tier with monthly signup volume. Pay per scored decision, not per individual signal. INR-first invoicing.
Most fraud teams running 50k-5M signups per month land in a per-decision range materially under the cost of stitching individual fraud vendors — no per-signal surcharges.
Common questions from compliance and product leads.
Real questions, asked in evaluation calls. If yours isn't here, book a 15-min walkthrough — we'll answer it live.
We already have a face-vendor and a device-vendor. Why consolidate?
Can we tune thresholds and signals per vertical?
Is the fraud score explainable for dispute defence?
Where is signup data stored?
How fresh is the MNRL list?
How long does integration take?
How does Deepvue's "Detect fraud at signup" workflow compare to single-vendor fraud checks?
Deepvue's fraud chain runs MNRL + face liveness + device intelligence + bank ownership match + PAN-Aadhaar consistency + PEP/sanctions in parallel and combines them into a single 0-1 fraud score with per-signal contribution shown. Single-vendor face or MNRL checks miss combined-signal fraud (deepfake selfie + matching mule bank account). Sub-2-second median, replayable trace per decision, threshold + ruleset versioned per vertical. Catches mule, deepfake, synthetic ID, sanctions, and bot-farm vectors in one call.
Score signup fraud in
under 2 seconds.
Real-time. Combined signal scoring. Replayable trace per decision.