What fraud and abuse do fintech platforms face?

Most fintech fraud and abuse comes down to two patterns: one person operating many accounts, and outsiders getting into accounts that are not theirs. Both surface at the signup and login layer, before any money moves. Fake account creation and multi-accounting (signup): one person opening many accounts to farm welcome bonuses and referral payouts, bypass per-account limits, or get around regional rules. Account takeover (login): someone signing in from a device or connection the real customer has never used. Bonus and promo abuse (promotions): the same person collecting a signup incentive again and again under fresh identities. ShieldLabs surfaces patterns indicating one person behind many accounts and logins from unrecognized devices, using a persistent visitor and device identifier that holds across sessions regardless of email, phone, IP, or cleared cookies.

How does ShieldLabs detect fake accounts and multi-accounting?

ShieldLabs links accounts to the one person behind them by running a single JavaScript snippet on your signup, login, or onboarding page and returning a risk score and signals on the first visit. When many signups trace back to one device, browser, or persistent identifier, the multi-accounting pattern fires. On every signup and login it returns: a 0-100 risk score; a persistent visitor and device identifier that survives cleared cookies, incognito, and rotated IP; anonymity signals such as VPN, proxy, Tor, anti-detect browser, and IP reputation; and active patterns in analytics, indicating multi-accounting, account farms, and related abuse. ShieldLabs delivers the score and signals via API and Webhooks, so your code can flag, step up, or hold an account before a bonus is paid or a limit is bypassed.

Does ShieldLabs do KYC or identity verification?

No. ShieldLabs does not run KYC or identity verification, it identifies the device and connection behind a signup and links many accounts to the one person operating them, which is a different question from whether a person is who they claim to be. KYC and identity-verification providers confirm a real-world identity: they scan government IDs, match selfies, run liveness and biometric checks, and check SSNs, credit-bureau records, or synthetic-identity databases. ShieldLabs answers a different question: is one person quietly running many accounts, even when every account uses a fresh name, email, and phone number? ShieldLabs sits alongside a KYC stack, not in place of it, and most fintech platforms run both.

Does ShieldLabs do transaction monitoring or AML?

No. ShieldLabs works at the signup and login layer, not in the transaction stream, it does not analyze payments, transfers, deposits, withdrawals, or money movement, and it is not an anti-money-laundering or transaction-monitoring platform. Transaction-monitoring and AML platforms watch what an account does with money after it is open. ShieldLabs scores the visitor and the device at account creation and at login, before any transaction happens, so the fake account and the account-takeover login surface earlier in the flow. ShieldLabs and a transaction-monitoring layer are complementary, and many fintech platforms run both.

How does ShieldLabs help with account takeover?

ShieldLabs identifies the device and connection behind every login and compares it to the ones the real customer has used before, raising the risk score when a login does not match. Because the visitor and device identifier is persistent, the match holds even when the login attempt clears cookies or rotates IP. An unrecognized device, a login from a device the real customer has never used, raises the score; an anonymized connection, a fresh anti-detect browser, or a VPN, proxy, or Tor connection, shows in the signals; and your rules decide whether to allow a familiar device through, step up to 2FA on a borderline score, or hold a high-risk login for review. ShieldLabs returns the score and the signal breakdown on the login event, so your code can act on a hijacked-account attempt before it reaches the customer's money.

Does ShieldLabs work for neobanks, lending, and payment platforms?

Yes. The mechanism is the same wherever one person can profit by opening many accounts, or by getting into an account that is not theirs, so the same signup-and-login layer fits each fintech vertical. Neobanks and digital banks: surfaces patterns indicating one person opening many accounts to farm welcome bonuses. Lending and credit platforms: shows when many applications trace back to a single device. Payment and money-transfer platforms: shows when a login comes from a device the real customer has never used. Trading and crypto-adjacent platforms: surfaces patterns indicating account farms and repeat referral signups. ShieldLabs does not label any of this fraud on its own; it identifies the visitor and device behind each account, links the accounts that belong to one person, and leaves the prevent-or-allow call to your rules.

How do I integrate ShieldLabs into a fintech platform?

Add one JavaScript snippet to your signup, login, or onboarding page and ShieldLabs returns a risk score on the first visit; integration takes about 5 minutes with no mobile SDK or backend rewrite. Install the snippet on the signup, login, or onboarding flow. Pass an optional UserHID (a hashed account or user identifier from your own system) once a user signs up or logs in. Read the risk score, anonymity signals, and patterns via REST API and Webhooks for server-side flows. ShieldLabs includes a free tier of 5,000 one-time identifications with no credit card required, so you can test on real traffic before enabling production rules.

Will ShieldLabs block customers automatically?

No. ShieldLabs blocks nothing automatically, it returns a risk score and a full signal breakdown, and your code decides what to do. Real customers keep moving unless your own rules act on the score. You set the thresholds: for example, flag an account for review or step-up verification above one score, hold a signup or login for manual review above a higher one. You own the action: allow, step up, or hold, on your side. ShieldLabs includes 5,000 free identifications so you can test thresholds on real traffic before enabling rules in production.

Fintech Fraud Prevention

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systems from fake accounts and account takeover

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Keep fake accounts out

Stop one person opening many accounts to farm signups and bypass limits

Protect signup incentives

Keep welcome bonuses, referral payouts, and promo budgets with real customers

Keep accounts in real hands

Catch account takeover logins from unrecognized devices and anonymized connections

Keep real users moving

Legitimate customers sign up and log in without extra verification steps

How ShieldLabs prevents fintech fraud and abuse

ShieldLabs recognizes the real visitor behind every anonymous session

Accurate Identification

Identify returning visitors and users across sessions, cleared cookies, incognito mode, and rotated IP, even when they sign up as new ones

Anonymity Signals

Detection of VPN, proxy, Tor, anti-detect browser, IP reputation and other anonymity signals on every visit, with up to 99% accuracy

Risk Score

A ready-to-use score reflecting the risk of each visit, with the weight of every signal behind it

Ready-Made Patterns

Detection of the hidden connections between accounts that may signal multi-accounting, account farms, account takeover, and more

Real-Time Analytics

See how much of your traffic is masked, with an overall quality score and a breakdown of the sources sending your visits

Prevent fintech fraud in 5 minutes

Easily integrate into any signup, login, or onboarding flow

1
Create your account
Sign up and get 5,000 free identifications.
2
Add the snippet
It identifies every visitor and returns their anonymity signals and a risk score
3
Check your traffic quality
See how much of your traffic is masked, with an overall traffic score
4
Use API & Webhooks
Apply the risk score and anonymity signals in your rules to stop fraud and abuse

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api.shieldlabs.ai/v1/visits/latest

{
  "request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
  "visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
  "device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
  "user_hid": "u_9f2a41c7",
  "public_ip": { "ip": "62.197.149.124", "country": "United States" },
  "local_ip": { "ip": "45.83.91.7", "country": "United States" },
  "connection_type": "vpn",
  "os": "Windows",
  "browser": "Chrome",
  "device_type": "desktop",
  "risk_score": 85,
  "signals": [
    { "name": "antidetect_browser", "weight": 60 },
    { "name": "vpn", "weight": 15 },
    { "name": "timezone_mismatch", "weight": 10 }
  ]
}

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