Compare SEON vs Sift

About Sift

Sift provides fraud prevention tools to minimize financial loss and brand damage through one single portal.  What can Sift do stacked up against SEON’s solutions? Or could they even work together? Today, we take a closer look.

Fraud is getting increasingly sophisticated and nuanced; opt for an anti-fraud solution with real-time AI decisioning and customizable rules that easily adapt to your changing needs.

Fraudsters move fast and are always adapting. In your fight against fraud, look for an end-to-end solution that provides extensive social and digital checks plus AML. Opt for flexible custom rules supported by machine learning so that you can adapt to new legislation and changing fraud patterns.

Sift Comparison Table

Why Choose SEON over Sift?

SEON Eliminates Advanced Fraud With Transparent, Customized Scoring

Preset rules are great for getting started and detecting the common forms of fraud across your industry, but every business has unique needs. Customizable risk scoring means you can control your fraud decisioning, adapting your rules to changing fraud trends, legislation, and your company’s risk appetite.

SEON offers both preset and customizable rules powered by transparent machine learning – so you can tap into instant AI decisioning while staying in control. With SEON, you can update and adapt your rules whenever you want, ensuring you stay responsive to changing environments.

SEON vs Sift

  • Sift: Custom API fields only  
  • SEON: Preset rules and in-depth custom rule creation via an easy-to-use interface, including comparison, data match, and velocity rules

SEON Suggests 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 Sift

  • Sift: Blackbox automated machine learning
  • SEON: Blackbox + explainable whitebox machine learning

SEON Protects the Entire Customer Journey — End to End

While point solutions offer fixes for specific fraud scenarios, an end-to-end 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.

Sift offers multiple individual APIs for different purposes; however, SEON is the only comprehensive customer intelligence platform with all the digital and social insights for seamless KYC. Unlike Sift, it also includes AML, giving you a full, 360-degree view of your customer’s fraud risk.

SEON vs Sift

  • Sift: No AML solution available
  • SEON: A complete solution offering AML

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 Sift

  • Sift: Very limited social and digital checks
  • SEON: 90+ social and digital checks, uses real-time data for a full and up-to-date picture of your customer

SEON offers a Free Trial and Ensures Ease Of Integration

Platforms that aren’t easy to use or integrate well don’t get used. You need a solution that is up and running fast, helping you block fraud from day one. Ensure that you have trialed the platform before committing to it. Check its functionality and integration with your existing processes. Look for easier integration and a solution provider that offers comprehensive customer support.

Use SEON’s 30-day free trial and speak with our fraud experts to ensure it’s the right platform for you.

SEON vs Sift

  • Sift: No free trial; integration takes months
  • SEON: 30-day free trial available, so you can try the easy-to-use, intuitive interface from day one before integrating the API-first platform.

Disclaimer: Everything you’ll read in this article was gleaned from online research, including user reviews. We did not have time to manually test every tool. This article was last updated in Q3 2023. Please feel free to contact us to request an update/correction.

Further Reading

Learn more about:

Browser Fingerprinting | Digital FootprintingDevice Fingerprinting | Fraud Detection API | Fraud Detection & Prevention

Try our free tools:

BIN Lookup | IP Lookup | Reverse Email Lookup | Reverse Phone Lookup | Social Media Lookup

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