BNPL & Lending Fraud Prevention

Stop Fraud at Every Lending and BNPL Decision Point

Bureau data and static ID checks miss modern fraud. Catch synthetic IDs and loan stacking with first-party signals, and approve genuine borrowers from application to repayment.

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Trusted by the World’s Most Ambitious Companies

Faster Approvals and Fewer Losses for
Lenders and BNPL Providers

8 sec

average loan decision time

90%

drop in fraudulent registrations

25%

drop in fraudulent transactions

Protect Every Step of the Lending Journey

Fraud moves through the entire borrower lifecycle. Protect every decision from onboarding to repayment, whether it happens on a loan application or at buy now pay later checkout.

Application

Reveal the full digital footprint behind every applicant, drawing on email, phone and IP data before KYC begins, so genuine borrowers move through verification quickly and risky applicants are stopped before costly checks.

Login

Pinpoint suspicious logins on returning borrowers in real time using device signals and velocity checks to stop account takeovers before funds or credit lines are exposed.

Repayment & Account Changes

Detect first-party fraud, stolen cards and chargeback risk across installments. Stop suspicious bank detail or device changes before they trigger fraudulent payouts.

Investigations

Replace manual processes with AI-supported case management, explainable decisioning and clear audit trails at every point of the customer journey.

GROW SAFELY

Approve More Thin-file Borrowers with Confidence

  • Improve credit scoring accuracy for thin-file applicants by feeding real-time digital footprint data into your Machine Learning and underwriting models alongside bureau data.
  • Give risk signals actionable context. Replace vague “suspicious” flags with risk probability scores so underwriters can clear low-risk applicants automatically and focus only on complex edge cases.
  • Adapt rules without developer dependencies. Give risk managers full autonomy to test and edit scoring rules directly in the dashboard without submitting engineering tickets.
Alternative credit scoring with digital footprint data

CATCH APPLICATION FRAUD

Stop Synthetic IDs & First-Party Fraud

  • Stop synthetic IDs and stolen identities before triggering expensive KYC and bureau checks by verifying email, phone, IP and digital footprint age prior to onboarding.
  • Identify coordinated fraud rings by linking device intelligence, browser profiles and velocity signals in real time.
  • Keep approval fast for both workflows: Score BNPL checkouts in milliseconds or filter out risky consumer loan applicants before paying for secondary vendor API calls.
How ViaBill cut fraudulent registrations by 90%

MONITOR THROUGHOUT THE CUSTOMER JOURNEY

Fraud Doesn’t Stop at Approval

  • Monitor repeat borrowers and returning shoppers continuously
  • Stop account takeovers and promo abuse on repeat purchases – a growing pattern in buy now pay later, where trusted accounts carry credit
  • Flag repayment fraud and bad-debt signals early, before defaults hit portfolio performance
How Mokka cut fraud rates by 65% in new markets

ONE PLATFORM

Unified Platform for Fraud, Credit Risk & Compliance

  • Combine fraud detection, thin file borrower verification, AML screening and case management in a single workflow, so fraud, credit and compliance teams work from the same data
  • Screen borrowers in real time against sanctions, PEPs, watchlists and adverse media
  • Generate audit-ready reports and consistent SAR narratives without stitching together vendor outputs

DEPLOY QUICKLY & efficiently

Built with Risk and
Technical Leaders in Mind

Fraud prevention that fits your roadmap and protects the borrower experience.

Borrower-first design

Protect the applicant experience, keeping approvals fast and friction low.

Go live in less than 14 days

A single API, minimal integration work and a proven onboarding process.

Flexible deployment

Rules-based or AI-driven scoring that feeds your existing credit models.

Discover How Companies Outsmart Fraud

“When I ask my team, do we really need that extra data? Is the ROI worth it for us? The answer is always yes. Yes, it adds value – especially when we look at SEON’s price per API request, it makes complete sense for Revolut.”

Dmitri Lihhatsov

Fincrime Product Owner at Revolut

Read story

“We now use the returned data [from SEON’s social media profiling] both to confirm identities, and as a debt collection tool to contact non-paying customers.”

Kaspars Magaznieks

Head of Fraud, Sun Finance

Read story
See all customer stories

Managed Risk Services

Partner with SEON’s lending fraud experts to scale your defense using strategies proven across
industry-leading companies.

Find out more

Learn More About BNPL & Lending Fraud Prevention

Frequently Asked Questions

What is lending fraud prevention?

Lending fraud prevention uses real-time risk intelligence to detect and block fraudulent activity across the entire customer lifecycle, not just during the initial application. It protects lenders from synthetic identity fraud, application fraud, account takeover (ATO) on existing borrower profiles, stolen payment methods during loan servicing and first-party default schemes.

What is loan stacking?

Loan stacking is when a borrower takes out multiple loans from different lenders within a short window, often before those loans appear on a credit file. SEON detects it by linking applications across devices, emails and networks in real time, catching stacking patterns that bureau data alone would miss.

How does SEON detect buy now pay later fraud without slowing checkout?

SEON scores each transaction in real time using digital footprint, device and behavioral signals, returning a decision in milliseconds. Legitimate shoppers are approved instantly, while risky applications are flagged or declined, so conversion rates stay high without incurring fraud losses.

Can SEON improve credit decisioning, not just fraud detection?

Yes. Lenders feed SEON’s digital signals into their own scorecards and underwriting models to sharpen approval quality. Customers like tbi Bank and Robocash use SEON’s data to segment applicants by risk and predict repayment likelihood alongside traditional credit data.

Take the First Step Toward Better Risk Decisions

“We now see close to zero successful identity fraud cases. And as a BNPL company, this has a direct impact on other metrics, such as a 90% drop in fraudulent registrations and a 50% drop in chargebacks.”

Claus Linde, Chief Product Officer, ViaBill

  • 93% decrease in false positives
  • 65% drop in fraud rates
  • 75% reduction in manual review time

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