Fingerprint Alternatives & Competitors

Best Fingerprint Competitors

Fingerprint is widely used for device recognition in fraud and bot prevention. However, device fingerprinting alone often isn’t enough to fully understand user intent or stop evolving fraud patterns. As a result, many teams explore alternatives that either offer similar device-level capabilities or take a broader fraud prevention approach by combining device, behavioral, and identity signals.

Below are five platforms commonly considered as alternatives to Fingerprint for fraud detection and prevention use cases.

SEON

SEON is a centralized command center for fraud prevention and AML compliance that helps teams understand risk across the entire customer journey. Rather than relying solely on device recognition, SEON combines device data with identity, behavior, and digital footprint intelligence to deliver richer context behind every interaction.

Built fully in-house, SEON uses over 900 proprietary, first-party data signals to power real-time risk decisions from the very first touchpoint. These signals are enriched and scored within a single platform, avoiding the complexity and inconsistency that can come with stitched-together third-party tools.

SEON’s API-first architecture enables fast deployment, typically going live in around two weeks. Fraud teams can customize detection logic using flexible AI models and more than 240 ready-to-use risk rules, adapting protection to different products, geographies, and fraud scenarios while keeping user experiences frictionless.

Feature	SEON	Fingerprint
Primary Focus	Holistic fraud & AML prevention	Device recognition
Data Coverage	Device, identity, behavior, digital footprint	Device/browser attributes
Data Ownership	900+ first-party signals	Device-level signals
Customization	High – flexible AI and rules	Limited to device risk
Fraud Use Cases	Onboarding, account abuse, payments, AML	Bot detection, repeat abuse

Sift

Sift provides risk scoring and decision workflows that help teams identify suspicious activity across signups, logins, and transactions. It emphasizes network-informed intelligence and operational tooling for reviewing activity, automating responses, and tuning policies as patterns change.

It’s commonly associated with use cases such as account takeover, payment risk, and first-party abuse. While device signals may be part of the overall signal mix, it’s typically positioned as a broader decisioning and operations layer rather than a standalone device identification tool.

ThumbmarkJS

ThumbmarkJS is a browser fingerprinting solution focused on generating stable device identifiers. It is often considered a technical alternative to Fingerprint for teams that want to identify returning devices and detect suspicious activity such as bots or repeat abuse.

As a fingerprinting-first tool, ThumbmarkJS is best suited for organizations that already have fraud logic in place and need device identifiers as one input signal. On its own, it offers limited visibility into user behavior, identity, or intent, which may require additional tools to support more complex fraud prevention strategies.

Signifyd

Signifyd is oriented toward ecommerce order screening and chargeback-related protection. It evaluates orders using a mix of behavioral indicators, network intelligence, and historical patterns to support approve/decline decisions at checkout.

Signifyd is typically used as a broader commerce decision layer rather than a standalone device intelligence tool.

Forter

Forter supports checkout and account-level risk decisions for ecommerce businesses, with a strong emphasis on identifying trusted customers while reducing unnecessary friction. Its approach draws on network-level intelligence and identity-focused signals to assess interactions in real time.

Forter is commonly used for payment decisions, account protection, and abuse-related scenarios. Like other ecommerce-first vendors, it’s typically positioned around transaction and account flows rather than serving as a standalone device fingerprinting solution.

Kount

Kount positions itself around digital trust and risk management, with capabilities that support screening, decisioning, and chargeback-related workflows. Organizations use it to spot risky behavior patterns and reduce loss exposure across different customer touchpoints.

Kount combines multiple signal types to inform decisions and investigations, often as one component within a broader risk stack. Device intelligence may contribute, but the overall focus is typically wider than device identification alone.

What Does Fingerprint Offer?

Fingerprint provides device recognition technology that helps businesses identify returning users by generating device fingerprints based on browser and hardware attributes. This capability is commonly used to detect bots, repeated abuse, and suspicious activity across sessions.

Device fingerprinting can be effective in certain fraud scenarios, particularly when identifying repeat actors. However, as fraud techniques evolve, device signals alone may offer limited insight into user intent, especially when fraudsters rotate devices, spoof attributes, or use virtual environments.

For teams addressing more complex fraud challenges, additional context, such as behavioral patterns, identity indicators, and network-level signals, often becomes necessary to support accurate, real-time decisions.

Why SEON Is a Strong Fingerprint Alternative

For organizations looking beyond device-only fraud detection, SEON offers a broader approach built around context, flexibility, and real-time intelligence. By combining device data with identity, behavior, and digital footprint signals, SEON helps teams understand why a user may be risky — not just which device they are using.

This multi-layered view enables more accurate fraud detection, fewer false positives, and smoother user experiences as businesses scale across products and markets.