Don’t waste money on pricey add-ons.  

SEON is a comprehensive solution, offering protection at every stage of the customer journey, from onboarding to transaction monitoring. ThreatMetrix offers device data; to protect your platform fully, you would need to purchase additional solutions.

SEON vs. ThreatMetrix Comparison Table

SEON Protects the Entire Customer Journey

While point solutions offer fixes for specific fraud scenarios, a fraud solution will protect against all fraud risks, supporting your business’ full fraud needs, protecting your customer’s entire journey, and giving you the confidence that your business is covered from fraud as you scale and grow.

ThreatMetrix offers a point solution for one segment of fraud; however, SEON is the only comprehensive customer intelligence platform with all the digital and social insights for seamless KYC. As SEON is built as a complete solution, you need to add nothing to it.

SEON vs. ThreatMetrix

• ThreatMetrix: Point solution
• SEON: A complete solution offering AML

With SEON, You Stay In Control With Transparent, Customizable Scoring

Customiable risk scoring allows you to control decision-making, even when supported by machine learning. Look for a solution with out-of-the-box rules to get you started and customizable rules you can easily update as your fraud exposure and business needs change.

While both ThreatMetrix and SEON offer industry preset and custom rules so that you can finetune your risk scoring system, only SEON offers whitebox machine learning. Whitebox machine learning gives lightning-fast anti-fraud decisioning with the transparency you need to stay in control. SEON’s blackbox machine learning also delivers greater accuracy. A mix of both gives you fast, accurate, and controllable decisioning to support your custom rules.  

SEON vs ThreatMetrix

• ThreatMetrix: Preset and custom rules and blackbox machine learning
• SEON: Preset and custom rules, plus whitebox and blackbox machine learning

SEON Provides Clear, Understandable Machine Learning Decisions

Machine learning is the best way to spot fraud patterns at scale and speed, improving its accuracy the more it is used and the more you send feedback to teach it.

Transparent and understandable whitebox machine learning means you benefit from AI-powered decisioning while staying in control by having visibility into how risk decisions and scoring work and being able to overwrite them. While effective, blackbox machine learning’s lack of explainability means that you don’t necessarily have the same understanding of risk decisions and the control a whitebox solution can provide.

SEON offers dual machine learning for automated risk pattern identification. Our transparent whitebox algorithm provides rule suggestions based on patterns it detects for proactive fraud prevention. Our blackbox module crunches the numbers behind the scenes and prevents fraud that human eyes are unlikely to catch for enhanced security. As a SEON customer, you get the benefit of both worlds. The goal is always the same: to improve the accuracy of your fraud detection over time based on your real and unique business landscape.

SEON vs. ThreatMetrix

• ThreatMetrix: Blackbox learning only
• SEON: Blackbox + explainable whitebox machine learning

Crowdsourced vs. SEON Internal data

Sourcing data internally ensures a high level of control, security, and accuracy, allowing SEON to provide detailed insights into anti-fraud measures while tailoring solutions to specific customer needs. This approach guarantees greater consistency, reliability, and speed by eliminating the variability inherent in crowdsourced data quality and authenticity.

By prioritizing internal data, SEON maintains confidentiality, protects sensitive information, and upholds compliance with regulatory standards. In contrast, crowdsourcing, reliant on external contributors, often yields less accurate fraud detection. This is because it relies on consortium data from various industries, assuming universal anti-fraud systems’ effectiveness, leading to inaccuracies and non-specific fraud detection

SEON VS ThreatMetrix

• ThreatMetrix: Crowdsourced and internal data
• SEON: Internally gathered data

SEON Utilizes the Value of Real-Time Digital and Social Signals

A customer’s digital and social signals, or their ‘digital footprint,’ can be a powerful way to measure fraud risk. They tell us a lot about a customer in lightning-fast time. These alternative data sources come in especially handy when traditional credit data is not available, which is a common scenario with the younger generation and in underbanked regions. Here are a few examples of digital and social signals that help you assess creditworthiness. 

• Access to Spotify, Disney+, Netflix = The customer is paying regular subscriptions
• Use of LinkedIn = Likelihood of employment (which can be manually verified)
• An account with Airbnb = The customer has passed Airbnb’s KYC check
• The customer has a limited digital footprint = the average email is linked to about 8-10 online accounts; significantly fewer signals present risk

SEON vs ThreatMetrix

• ThreatMetrix: ‘near real-time’ data with no digital and social checks
• SEON: 90+ social and digital checks, uses real-time data for a full and up-to-date picture of your customer

Further Reading

Learn more about:

Browser Fingerprinting | Digital Footprinting | Device Fingerprinting | Best Fraud Detection Software

Try our free tools:

Reverse Email Lookup | Reverse Phone Lookup | Social Media Lookup