Verifica que el reclamant és el titular de la pòlissa, bloqueja proves de vídeo deepfake, detecta documents falsificats, filtra contra llistes de sancions, troba xarxes de lesions organitzades, en una sola sessió /v3/. $0.53 per reclamació, 500 gratuïtes cada mes.
Robinhood Ventures
Confiat per més de 2.000 organitzacions a tot el món.
El que es paga i no hauria de fer-ho
La Coalition Against Insurance Fraud estima que les asseguradores dels EUA perden al voltant de 308.000 milions de dòlars anuals. La major part prové de reclamacions sense verificació d'identitat del reclamant i sense anàlisi forense de les proves. Didit tanca ambdues bretxes en un sol flux de treball: 0,53 $ per reclamació, 500 gratuïtes cada mes.
Tria les comprovacions que vulguis: identificació, prova de vida, coincidència facial, sancions, adreça, edat, telèfon, correu electrònic, preguntes personalitzades. Arrossega-les a un flux al tauler de control o publica el mateix flux a la nostra API. Crea ramificacions segons condicions, executa proves A/B, sense necessitat de codi.
Integra de forma nativa amb els nostres SDKs per a Web, iOS, Android, React Native o Flutter. Redirigeix a una pàgina allotjada. O simplement envia un enllaç al teu usuari: per correu electrònic, SMS, WhatsApp, on vulguis. Tria el que millor s'adapti al teu stack.
Didit allotja la càmera, les indicacions d'il·luminació, el traspàs mòbil i l'accessibilitat. Mentre l'usuari està en el flux, puntuam més de 200 senyals de frau en temps real i verifiquem cada camp amb fonts de dades autoritzades. Resultat en menys de dos segons.
Els webhooks signats en temps real mantenen la teva base de dades sincronitzada en el moment en què un usuari és aprovat, rebutjat o enviat a revisió. Consulta l'API sota demanda. O obre la consola per inspeccionar cada sessió, cada senyal i gestionar els casos a la teva manera.
Didit · Claim shapes
Didit · Claimant verification
Claim · 4 / 5
Match against policy
Didit · Document OCR + tamper
Didit · Passive Liveness · iBeta L1 PAD
Didit · AML + Face Search
AML screening
Face Search · prior claimants
Didit · Webhook · X-Signature-V2
{
"session_id": "claim-8821",
"vendor_data": "claim-8821",
"status": "In Review",
"face": { "similarity_score": 0.42 },
"aml": { "status": "In Review",
"hits": [{ "list": "PEP" }] },
"face_search": { "matches": [
{ "session_id": "claim-7710",
"similarity": 0.94 }
] }
}$ curl -X POST https://verification.didit.me/v3/session/ \
-H "x-api-key: $DIDIT_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"workflow_id": "wf_claim_verify",
"vendor_data": "claim-8821",
"metadata": { "policy_id": "POL-44120" }
}'status: Approved.docs →// X-Signature-V2 verified upstream
if (payload.status === "Approved") {
releasePayout(payload.vendor_data);
} else if (payload.status === "In Review") {
routeToSiu(payload.face_search.matches);
}X-Signature-V2 abans de llegir la càrrega útil.docs →You are integrating Didit into an insurance carrier's claim workflow at first notice of loss (motor, health, property, travel, business interruption). Goal: catch ghost claims, deepfake video evidence, forged supporting documents, sanctions hits on the claimant, and organised-injury rings before the payout enters the queue. One API call. One signed webhook. Five fraud signals.
WHY THIS SHAPE
- The Coalition Against Insurance Fraud estimates fraud costs the US industry around $308 billion / year (2022 figures). Most of that walks in through claims with no identity check on the claimant and no document forensics on the evidence.
- Five signals settle the question on most claims: (1) the claimant is the policyholder, (2) the claimant is alive and present (not a deepfake), (3) the supporting documents are not tampered or templated, (4) the claimant is not on a sanctions list, (5) the claimant has not already filed N near-identical claims across your portfolio.
- One Didit /v3/session/ call bundles all five. $0.33 KYC + $0.20 AML = $0.53 per claim. Face Search 1:N is free per search. 500 verifications free every month.
PRE-REQUISITES
- Production API key from https://business.didit.me (sandbox key in 60 seconds, no credit card).
- A webhook endpoint with HMAC SHA-256 verification of the X-Signature-V2 header using your webhook secret.
- A Workflow Builder workflow bundling ID Verification + Passive Liveness + Face Match 1:1 (with the policyholder portrait as comparison target) + AML Screening + Face Search 1:N (gallery scoped to prior claimants on your account).
- Reference to the policy ID and policyholder portrait on file from your Policy Administration System (PAS) — passed as metadata on the session.
STEP 1 — Open the claim-verification session
POST https://verification.didit.me/v3/session/
Headers:
x-api-key: <your api key>
Content-Type: application/json
Body:
{
"workflow_id": "<wf id bundling ID + Liveness + Face Match + AML + Face Search 1:N>",
"vendor_data": "<your claim id, max 256 chars>",
"callback": "https://<your-app>/claims/verify/callback",
"metadata": {
"policy_id": "<your policy id>",
"line_of_business": "motor",
"loss_date": "2026-04-12"
}
}
Response: 201 Created with a hosted session URL. Send it to the claimant by email / Short Message Service (SMS) / inside the claims app. The claim stays in HOLD on your side until the signed webhook lands.
STEP 2 — Read the signed webhook
Didit POSTs the verdict. Verify X-Signature-V2 (HMAC SHA-256 of the raw body) BEFORE reading the JSON.
Payload (excerpted):
{
"session_id": "<uuid>",
"vendor_data": "<your claim id>",
"status": "In Review",
"id_verification": { "status": "Approved" },
"liveness": { "status": "Approved" },
"face": { "status": "Approved", "similarity_score": 0.92 },
"aml": { "status": "In Review", "hits": [{ "list": "PEP" }] },
"face_search": {
"matches": [
{ "session_id": "claim-7710", "similarity": 0.94, "vendor_data": "claim-7710" }
]
}
}
Session status enum (exact case, Title Case With Spaces): Approved | Declined | In Review | Resubmitted | Expired | Not Finished | Kyc Expired | Abandoned.
STEP 3 — Branch on the verdict
Approved → release the claim into the standard payout queue.
In Review → route to the Special Investigations Unit (SIU) with the per-module signals + face-search match list as the case file.
Declined → decline + open file. Block the payout. The decision payload is the audit pack.
Not Finished → resend the session link.
STEP 4 — Document forensics on supporting evidence (separate sub-flow)
For each supporting PDF / image uploaded (police report, repair quote, medical invoice, photo of loss), run Didit Document Optical Character Recognition (OCR). The OCR response surfaces:
- Field-level extracted text (claim amount, names, dates)
- Tamper score per region (pixel-level edits)
- Template match against prior submissions (reused PDF skeleton)
- EXIF / metadata mismatch (date in photo vs date of loss)
A high tamper score or a template match against a prior claim flips the case status to In Review.
STEP 5 — Pull the full decision for the case file
GET https://verification.didit.me/v3/session/{session_id}/decision/
Headers:
x-api-key: <your api key>
Returns the full decision payload — per-module signals, raw face-similarity scores, AML hit list with source list per match, face-search candidate list with scores. Use this as the audit pack for any dispute.
WEBHOOK EVENT NAMES
- Sessions: standard session webhook. One endpoint, status field tells you the lifecycle.
- Verify X-Signature-V2 (HMAC SHA-256) on every payload.
CONSTRAINTS
- Session statuses use Title Case With Spaces. Never UPPER_SNAKE_CASE — that's the Transactions API.
- The Face Match comparison target is the policyholder portrait from your Policy Administration System (PAS). A deepfake of the policyholder cannot pass when Passive Liveness is also in the workflow.
- Face Search 1:N gallery is scoped to YOUR account — Didit does not search across carriers. To collaborate across an industry pool, use a shared workflow_id pointing at a multi-carrier gallery you own.
- 200+ fraud signals are surfaced on every session at no extra cost — read them off the decision payload, don't re-query.
- Default retention is the standard 5-year insurance horizon; adjust per workflow if your jurisdiction differs.
Read the docs:
- https://docs.didit.me/sessions-api/create-session
- https://docs.didit.me/core-technology/face-match/overview
- https://docs.didit.me/core-technology/aml-screening/overview
- https://docs.didit.me/core-technology/face-search/overview
- https://docs.didit.me/integration/webhooks
Start free at https://business.didit.me — sandbox key in 60 seconds, 500 verifications free every month, no credit card.0 $ / mes. No es requereix targeta de crèdit.
Paga només pel que utilitzes. Més de 25 mòduls. Preus públics per mòdul, sense quota mínima mensual.
MSA i SLA personalitzats. Per a grans volums i programes regulats.
Comença gratis → paga només quan s'executa una comprovació → desbloqueja Enterprise per a un contracte personalitzat, SLA o residència de dades.
Didit is infrastructure for identity and fraud, the platform we wished existed when we were building products ourselves: open, flexible, and developer-friendly, so it works as a real part of your stack instead of a black box you integrate around.
One API covers verifying people (KYC, know your customer), verifying businesses (KYB, know your business), screening crypto wallets (KYT, know your transaction), and monitoring transactions in real time, on a stack built to be:
The footprint underneath: 14,000+ document types in 48+ languages, 1,000+ data sources, and 200+ fraud signals on every session. The Didit infrastructure dynamically learns from every session and gets better every day.
The Coalition Against Insurance Fraud estimated US insurers lose around \$308.6 billion a year to fraud across all lines (2022 study). The European insurance body Insurance Europe reports similar share-of-premium loss across the EU.
Most of it is opportunistic, small inflated amounts on otherwise legitimate claims. A smaller share is professional rings filing the same loss across carriers. Both shapes share one weakness: nobody checks who the claimant is at first notice of loss.
A ghost claim is when the person filing the claim is not the policyholder. Typical pattern: an attacker buys leaked customer data from a breach, calls the carrier impersonating the policyholder, files a small loss claim, and asks for the payout to a new bank account.
The attack succeeds when there is no biometric check tying the caller to the person on the policy. Face Match 1:1 against the policyholder portrait closes this gap in two seconds.
The full flow normally takes under 30 seconds end-to-end, pick up the ID, snap the document, snap the selfie, done. That is the fastest in the market. Legacy KYC providers usually take more than 90 seconds for the same flow.
On the back end, Didit returns the result in under two seconds at p99, measured from the moment the user finishes the selfie to the moment your webhook fires. Mobile capture is tuned for slow phones and slow networks: progressive image compression, lazy software development kit load, and a one-tap hand-off from desktop to phone via QR code if the user starts on web.
On every supporting document the claimant uploads, police report, repair quote, medical invoice, photo of the loss, Didit runs:
A high tamper score or a template match flips the claim from Approved to In Review automatically.
Every session lands on one of seven clear statuses, so your code always knows what to do:
Approved, every check passed. Move the user forward.Declined, one or more checks failed. You can allow the user to resubmit the specific failed step (for example, re-take the selfie) without re-running the whole flow.In Review, flagged for compliance review. Open the case in the console, see every signal, decide approve or decline.In Progress, user is mid-flow.Not Started, link sent, user has not opened it yet. Send a reminder if it sits too long.Abandoned, user opened the link but did not finish in time. Re-engage or expire.Expired, the session link aged out. Create a new session.A signed webhook fires on every status change, so your database always stays in sync. Abandoned and declined sessions are free.
Production data is processed and stored in the European Union by default, on Amazon Web Services. Enterprise contracts can request alternative regions for jurisdictions whose regulators require it.
Encryption everywhere. AES-256 at rest across every database, object store, and backup. Transport Layer Security 1.3 in transit on every API call, webhook, and Business Console session. Biometric data is encrypted under a separate Customer Master Key.
Retention is yours to control. Default retention is indefinite (unlimited) unless you configure shorter, between 30 days and 10 years per application, and you can delete any individual session at any time from the dashboard or the API.
Certifications: SOC 2 Type 1 (Type 2 audit in progress), ISO/IEC 27001:2022, iBeta Level 1 PAD, and a public attestation from Spain''s Tesoro / SEPBLAC / CNMV that Didit''s remote identity verification is safer than verifying someone in person. Full report at /security-compliance.
Didit ships compliant by default for the regulators that matter to identity infrastructure:
Detailed memo, every certificate, every regulator letter: /security-compliance.
Three integration paths, pick whichever fits your stack:
Same dashboard, same billing, same pay-per-success price for all three. Step-by-step guide at docs.didit.me/integration/integration-prompt.
Per claim:
Around \$0.53 per fully-screened claim. The first 500 verifications every month are free on every account, pilot the workflow inside the free tier.
No minimums, no annual commitment, no per-seat pricing.
The signed webhook is the routing signal. On status: In Review, your back-end routes the case to the Special Investigations Unit (SIU) with the per-module signals + face-search match list as the opening case file.
Pull the full decision payload via GET /v3/session/[id]/decision/ for the analyst, face similarity scores, AML hit list with source list per match, face-search candidate list with scores, document forensics output. The decision payload is the audit trail for any dispute.
Biometric vectors are special-category data under the EU's General Data Protection Regulation (GDPR). The lawful basis is the standard claims-investigation legitimate interest plus the consent flow at session start.
Default retention is the standard 5-year insurance horizon, adjust per workflow if your jurisdiction or supervisor differs. The gallery for Face Search 1:N is scoped to your account by design, no cross-carrier search unless you join a consortium and run a shared workflow_id.
Una API per a KYC, KYB, monitorització de transaccions i anàlisi de carteres. Integra-la en 5 minuts.