Fraud Prevention

Fraud Has Outpaced Most Verification Stacks. It’s time for smarter fraud prevention solutions

Fraud is evolving across synthetic identities, deepfakes, forged documents, account takeovers, and organised fraud rings. Shufti’s 40+ ensemble AI models detect risk signals across the full customer lifecycle, from onboarding to ongoing monitoring.

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Fraud prevention — 40+ ensemble AI models detect synthetic identities, deepfakes, forged documents, account takeovers, and organised fraud rings across the customer lifecycle

Trusted By 2000+ Clients Worldwide

THE THREAT LANDSCAPE

Three Operational Failures That Let Fraud Through

Verification Gaps Let Fraud Through

Legacy verification stacks were built for older risk patterns. Fraud teams now face threats that move faster than these systems can detect. The gap is no longer a rare edge case. It creates live exposure. Shufti’s detection is built against the fraud methods that current verification stacks cannot catch.

Fragmented Tools, Incomplete Risk View

Identity, AML, transaction, and device checks often sit in separate tools. This leaves analysts without a full view of user risk. Shufti connects verification, device, behaviour, and AML signals in one user record.

False Positives Are Consuming Compliance Capacity

Legacy AML engines can produce 90 to 95% false positives at tier-1 banks. MLROs spend 80% or more of their time on Level 1 triage. Shufti checks each AML alert against the verified identity before analyst review.

Solution Hub

fraud prevention Across the Full Customer Lifecycle

Every Session Monitored to Block Account Takeover

Fraud does not stop at onboarding. Behavioural signals, device intelligence, and biometric step-up verification monitor every session after login, blocking account takeover and bot-driven abuse without disrupting verified users.

Behavioral Biometrics

Monitors keystroke dynamics, mouse movement, and session interaction patterns continuously after login. Detects credential-stuffed sessions, bot-driven automation, remote access tools, and account sharing without adding friction to verified users.
Device Fingerprinting

Builds a persistent device profile from hardware signals, browser attributes, and network characteristics. Identifies the same device operating under multiple identities and flags clusters linked to organised fraud rings.
1:1 Authentication

Triggers biometric step-up re-verification at high-risk moments: large transactions, new device registration, and account changes. Compares the live selfie against the original verified identity record, blocking account takeover through stolen credentials.
Fraud Hub

Aggregates identity, device, behavioural, and transaction risk signals into one configurable risk score per session. Fraud teams calibrate thresholds and decisioning logic without engineering tickets.
MFA

Configurable multi-factor authentication layered on top of primary identity verification. Applies OTP, TOTP, or biometric challenge at defined risk thresholds without disrupting low-risk user journeys.

Every Synthetic Identity Stopped Before Account Activation

Synthetic identities, deepfakes, and AI-generated documents enter platforms at onboarding. A 9-layer forensic engine and iBeta PAD Level 3 certified liveness detection stop them before account activation.

Facial Biometrics

iBeta PAD Level 3 certified passive and active liveness detection defends against printed photos, screen replays, 3D silicone masks, and Gen 5 virtual camera injection attacks. Passive mode analyses a single frame with no user challenge required.
Document Verification

iBeta PAD Level 3 certified passive and active liveness detection confirms the person at onboarding matches the submitted identity document. Defends against impersonation and synthetic identity fraud across the compliance evidence trail.
NFC Verification

Reads the cryptographically signed chip in ICAO-compliant ePassports and national eID cards. Chip-level validation confirms the document is genuine and unmodified, regardless of how convincing the physical or digital forgery appears.

Fraud Patterns Caught Before Enforcement Ever Begins

Fraud patterns often emerge weeks after onboarding. Continuous AML screening, transaction monitoring, and adverse media surveillance detect financial crime risk as it develops, not after enforcement begins.

AML Screening

Screens against 4,000+ watchlists covering 215+ sanction regimes and 2.6M PEP profiles across 215+ jurisdictions, refreshed every 15 minutes. Every alert is bound to a biometrically verified identity. The MLRO AI Agent reduces Level 1 triage time by up to 60%.
Transaction Trust Monitoring

Detects 10 distinct financial crime patterns in real time, including structuring, round-tripping, money mule coordination, chargeback patterns, and velocity anomalies. Each transaction evaluated in under 5 seconds.
Adverse Media Monitoring

Screen investor applicants against global news sources for links to fraud, financial crime, or regulatory enforcement action. NLP entity disambiguation filters false positives from common investor names, so your compliance queue stays focused.

Built for Every Role on the Fraud and Compliance Team

Fraud detection platform covers the full fraud lifecycle. Each role gets the signals, controls, and evidence it needs, without separate tooling.

Compliance Officer

Audit-ready evidence on every verification, structured for regulatory inspection across every jurisdiction you operate in.

Interest Compliance Coverage AML Regulatory Audit

Product Manager

Configurable verification flows that balance speed against risk tolerance, without rebuilding the integration each time.

Interest Platform Configuration Journey Builder Risk Modes

Developer

REST API, mobile SDKs, and sandbox access. First verification call within hours of integration start.

Interest Integration Docs REST API Mobile SDKs

Fraud Analyst

Pre-scored evidence and fraud signals on every flagged case, so your team reviews decisions, not raw submissions.

Interest Fraud Detection Fraud Scoring Liveness Detection

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FRAUD COVERAGE

Full Fraud Taxonomy, Manager’s Complete List Covered

Deepfake

AI-generated faces and synthetically forged documents bypass legacy liveness checks at scale. Shufti’s passive liveness & document forensics detects synthetic media before it reaches your onboarding flow.

Document Deepfake Facial Deepfake

Identity Fraud

Credential theft, blended synthetic identities, and manipulated documents exploit gaps in manual review. Shufti’s layered verification surfaces fraud signals before accounts are created.

Account Takeover Synthetic Identity Fraud Document Fraud Impersonation Fraud

Account & Platform Abuse

Duplicate registrations, bot-driven sign-ups, and referral exploits erode platform economics. Shufti links device, identity, and behavioural signals to flag abuse rings at scale.

Multi-Accounting Bonus & Promo Abuse Fraud Networks

Transaction & Payment Fraud

False chargeback claims, money mule networks, and sanctions evasion expose your business to financial and regulatory risk. Shufti ties identity verification directly to transaction context.

Chargeback Fraud Money Mule Activity Party Fraud Regulatory & Compliance Risks

Built for Compliance: Go live in minutes with our flexible API and lightweight SDKs

Single API, Seamless Integration

Build fully customisable verification flows with seamless backend integration.

Gain full control by customising verification flows end-to-end.
Integrate seamlessly with your backend for quick implementation.
Design flexible verification journeys tailored to your users.

Explore API Docs

Launch a native verification experience in your mobile app within minutes.

Launch native verification within minutes on iOS or Android.
Use ready-made UI with camera, capture, and real-time feedback.
Customise flows to fit seamlessly into your mobile app.

Explore SDKs Docs

Run Shufti within your own identical-capability infrastructure for maximum data control and privacy.

Keep all sensitive information in-house to meet strict governance and data residency requirements.
Keep sensitive information fully private and secure in-house.
Deploy in highly regulated sectors without compromising compliance.

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Quickly launch identity verification through a secure, customisable web link, no code required. Learn more.

Start verifying users instantly with a no-code setup.
Deliver a consistent identity experience via a link or embedded iframe.
Deploy quickly via a secure link or embedded iframe.

Explore API Docs

With KYC Journey Builder, create personalised verification journeys without writing a single line of code.

Customise your journey effortlessly with drag-and-drop functionality.
Instantly see how your verification flow looks for your users.
Easily connect with Hosted Verification for a consistent, branded experience.

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INDEPENDENTLY EVALUATED

Validated by Leading Analysts and Certification Bodies

Don't just take our word for it, hear from our customers

The Confidence Our Clients Share

The future of digital identity is defined by trust, interoperability, and regulatory alignment, so our partnership with Shufti reinforces DevCode Identity's commitment to supporting our global customers with the most secure, best-in-class, complaints identity verification solutions available today.

Combining our Conversion Driven Compliance Orchestration Platform with Shufti's global KYC and IDV capabilities allows our customers not only to navigate complex regulatory demands but also to maintain a seamless customer onboarding experience with the highest achievable conversion rates.

Mark Knighton

Chief Global Development Officer - Global Alliances, DevCode

Where It Fits

Built for Every Platform Fraud Targets

Trusted Sellers, Repeat Fraud Blocked

Verify the seller is real at onboarding, then prevent re-joins with duplicate detection and optional 1:N matching across the marketplace.

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Everything you need to know in one place

Frequently Asked Questions

What is the difference between active and passive liveness detection?

Passive liveness analyses a single frame with no user action, detecting GAN signatures and sensor anomalies. Active liveness adds a challenge-response layer for high-risk or regulated sessions. Shufti supports both. Passive is preferred for high-volume flows; active adds a second verification layer where regulation requires it.

Can deepfake injection attacks bypass Shufti’s liveness detection?

Gen 5 injection attacks route synthetic video through a virtual camera driver, bypassing the physical sensor. Shufti counters at three independent layers: GAN fingerprinting, CMOS sensor noise validation, and device-level virtual camera detection. A novel attack defeating one layer is still evaluated by the remaining two.

How does Shufti detect synthetic identity fraud?

Every government-issued document passes a 9-layer forensic engine: MRZ extraction, template classification, font consistency forensics, microprint analysis, holographic detection, deepfake intelligence, and metadata forensics. Template-specific models are trained on verified genuine specimens. In independent testing, Shufti detected 8 of 8 forgeries that legacy systems passed.

How does Shufti detect money mule activity and organised fraud networks?

Transaction Trust Monitoring detects structuring, round-tripping, mule coordination patterns, and velocity anomalies in real time at under 5 seconds per transaction. Device Fingerprinting identifies shared infrastructure across accounts. Behavioural Biometrics flags coordinated bot-driven session patterns. AML Screening screens every user against 4,000+ watchlists refreshed every 15 minutes.

Do fraud signals captured at onboarding carry forward through the lifecycle?

Every session generates one unified record: document images, AI confidence scores, face match result, liveness outcome, device fingerprint, and AML screening result. This record persists and is available to transaction monitoring and ongoing AML screening. When a fraud pattern emerges post-onboarding, analysts see the full identity context, not an isolated alert.

Does Shufti support on-premise deployment for fraud detection?

On-premise and private cloud deployment is available for biometric liveness detection, document verification, and face matching. AML screening operates through regional cloud processing (EU, UK, US, APAC, MENA) with configurable data retention and DPA coverage.

Assess Whether Your Stack Can Detect Modern Fraud

AI-generated documents, advanced deepfakes, synthetic identities, and organised fraud rings are outpacing legacy verification systems. Evaluate your detection coverage across identity, device, behavioural, and transaction signals through one integration.

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