• Products

    Digital Trust & Safety Platform

    Fight fraud without sacrificing growth

    Learn more

    Platform solutions

    • Payment Protection
    • Account Defense
    • Dispute Management
    • Content Integrity
    • Sift Connect
    • Passwordless Authentication

    Sift innovations

    • PSD2 Solution
    • New Releases & Enhancements
  • Industries

    One solution, any industry

    Learn how Sift can work for your industry

    Learn more

    Featured Industries

    • Fintech
    • Payment Service Providers
    • Retail
  • Customers

    Case studies by industry

    See how leading brands succeed with Sift

    Learn more

    Featured Customers

    • DoorDash
    • Uphold
    • Paula’s Choice
  • Partners
  • Fraud Center
  • Resources

    Fraud-fighting resources

    Explore fraud trends and insights

    Learn more

    • Blog
    • Demos
    • Infographics
    • Ebooks & Reports
    • Videos
    • Podcasts
    • One-Pagers
    • Webinars
    • Trust & Safety University
  • Company

    Why leaders choose Sift

    Technology, community, and partnership

    Learn more

    Our mission: Help everyone trust the internet

    • About
    • Careers
    • News & Press
Request a demo
Products
  • Digital Trust & Safety Platform
  • Payment Protection
  • Account Defense
  • Dispute Management
  • Content Integrity
  • Sift Connect
  • Passwordless Authentication
  • PSD2 Solution
  • New Releases & Enchancements
Industries
  • Fintech
  • Retail
  • Payment Service Providers
Customers
Partners
Fraud Center
Resources
  • Blog
  • Ebooks & Reports
  • One-Pagers
  • Demos
  • Videos
  • Webinars
  • Infographics
  • Podcasts
  • Trust and Safety University
Company
  • Search Careers
  • Our Company
  • Contact Us
  • Engineering Blog
Request a Demo Sign In
  • Blog Home
  • Data & Insights
  • Fraud
< prev / next >
Share this article on LinkedIn
Tweet this article
Share this article on Facebook
SOCIALICON
Share this article via email

5 Factors for Selecting a Fraud Prevention Solution

By Kevin Lee  / 

20 May 2021

Insights Featuring Gartner

During a recent webinar hosted by Sift, featuring Akif Khan, Senior Director and Analyst at Gartner Research, we presented the key factors online retailers should consider when selecting a machine learning vendor for fraud prevention. Dr. Khan’s areas of expertise include fraud prevention using both rules-based and machine learning (ML) systems, behavioral biometrics, device identification and bot mitigation, in addition to traditional and evolving techniques to validate a consumer identity in digital interactions. He also advises clients on prevention of fraud via account takeover.

As fraud continues to evolve, most online retailers are overwhelmed by choice and frozen by indecision when it comes to selecting a fraud prevention vendor. As a former merchant myself, I can attest to how overwhelming vendor selection can be. It took me and my team a lot of bumps along the way to learn these best practices. 

Although many focus their vendor selection on machine learning capabilities, the truth is that all vendors are ML vendors to some degree. They either embrace rules along with ML, resent rules and focus more on ML, or don’t use rules at all and rely solely on ML. So the true differentiators between vendors don’t lie in their ML capabilities—which are oftentimes proprietary anyways—but in other more targeted considerations that can yield more valuable insights. 

When choosing a vendor, it’s important to take a holistic approach and consider all of the factors—pricing, ease of integration, outsourced vs. internal operating models, references, dashboard and reporting visibility, and machine learning capabilities—to determine which solution is right for your business. Keeping all of this in mind, Khan recommends five key factors for selecting a machine learning fraud prevention vendor, detailed below. 

1. How much data the vendor processes

When comparing fraud prevention solutions, the more data, the better. Larger, more comprehensive datasets are more likely to be accurate because it’s easier to spot trusted or risky data. So while it’s important for any vendor to have advanced ML algorithms that are ever-evolving, it’s even more crucial to have access to as much data as possible. Khan provides a spot-on analogy for comparing vendors with varying amounts of data:

“A real-world analogy to this would be perhaps imagine you have an illness and you’re going to two different doctors. Those two doctors might have had exactly the same training, and they might use the same methods. But if one doctor has seen a lot more patients than the other one, you’re probably going to want to see the one that’s seen more patients, because they’re probably going to be more likely to make an accurate diagnosis, taking advantage of all of that data they’ve accumulated from seeing all of those patients,” explains Khan. 

2. How applicable the data is to your business

Having a large base of data is only one part of the equation, though. It’s also a matter of how relevant the data is to your business. There are four models vendors may be using:

  • Retailer-specific: Data only from your business.
  • Industry-specific: Data from other retailers in the same industry.
  • Region-specific: Data from other retailers in the same geographic region.
  • Universal: Data from all retailers using the vendor’s solution.

It’s important to ask vendors what kind of data they process and from which buckets. To create the most accurate set of data, you’ll want a combination of all these models.

3. How vendors retrieve data

Another factor to consider is where the outcome data is sourced. Most vendors rely on supervised machine learning, which uses algorithms trained on historical data to flag which transactions are fraudulent. When asking vendors about where they get their source-of-truth data, make sure you’re familiar with the options:

  • Manual review decisions: Rely on human judgment, making it more time-intensive and less useful.
  • Chargebacks (via retailer): Rely on retailers to label chargebacks, making it a bit more useful than manual review decisions. 
  • Chargebacks (via acquirer or processor): Rely directly on the acquirer or processor to get chargeback information. This is a more automated option that means less effort for the retailer. 
  • Card scheme reports: Not all vendors will have access to this, but it offers the most reliable and comprehensive way to understand transaction outcomes.

4. How models are adapted and retrained

It’s best to steer clear of stagnant models when considering any vendor—if they’re not refreshing their ML algorithms regularly, their data may not be accurate or useful. ML models should learn and adapt in real time, not simply push out a score in less than a second or have to wait a quarter to get a refresh. 

Vendors should constantly be adapting and retraining their models to ensure they’re utilizing the most recent historical data, which in turn impacts the accuracy of weightings, scoring, and penalties within the model. As an online retailer, you should ask how adaptive a vendor’s models are and how retraining may impact data outcomes.

5. The transparency and usability of model outputs 

Just like working with any vendor, transparency is key. Having insight into ML models can make a huge difference when trusting a vendor to handle your business’s fraud prevention. If you have an internal manual review team, you can utilize these outputs to make decisions, drive accountability, and build trust with customers. Khan offers insight into how fraud prevention vendors can provide the most value to a business:

“I find that the vendors that I think are offering more value today are those which are really looking beyond the payment fraud, looking beyond account takeover, and are actually really able to offer value at these different events within the customer journey,” says Khan.

Any vendor can minimize risk—it’s crucial to overall business growth to also look at minimizing friction and false-positive rates to improve the customer experience. This is why so many companies have adopted a trust and safety mindset—to not only protect, but grow. 

If you’re interested in how Digital Trust & Safety can help address your specific online retail challenges, prevent fraud, and help your business grow, I highly encourage taking the Sift Digital Trust & Safety Assessment. You’ll receive custom recommendations from our team of experts with decades of fraud-fighting experience at companies like Facebook, Square, and Google.

Take the assessment.

*Sift webinar featuring Gartner analyst based on Gartner, How to Select a Machine Learning Vendor for Fraud Detection in Online Retail, Akif Khan, Jonathan Care, 1 March 2019. 

Related

chargebacksDigital Trust & Safetyfraudfraud detectionfraud preventionfraud prevention solutionfraud prevention vendorgartnermachine learningmanual reviewonline retailoutcome data

Kevin Lee

Kevin Lee is VP of Digital Trust & Safety at Sift where he helps customers implement strategies that cross-functionally align risk and revenue programs. Prior to Sift, he has spent the last 14+ years leading various risk, chargeback, spam/scams, and trust and safety organizations at Facebook, Square, and Google.

  • < prev
  • Blog Home
  • next >
  • Company
  • About Us
  • Careers
  • News & Press
  • Partner With Us
  • Blog
  • Support
  • Help Center
  • Contact Support
  • System Status
  • Trust & Safety University
  • Fraud Management
  • Developers
  • Overview
  • APIs
  • Client Libraries
  • Integration Guides
  • Tutorials
  • Engineering Blog
  • Social

Don’t miss a thing

Get industry trends, insights, and actionable fraud-fighting tips.

You're on the list.

You can unsubscribe at any time. Please see our Website Privacy Notice.
Do Not Sell My Personal Information

If you are using a screen reader and are having problems using this website, please email support@sift.com for assistance.

© 2022 Sift Science, Inc. All rights reserved. Sift and the Sift logo are trademarks or registered trademarks of Sift Science, Inc.
Privacy & Terms

Secure your business from login to chargeback

Stop fraud, break down data silos, and lower friction with Sift.

  • Achieve up to 285% ROI
  • Increase user acceptance rates up to 99%
  • Drop time spent on manual review up to 80%
Your information will be used to contact you about our service and subscribe you to our direct marketing communications. You can, of course, unsubscribe at any time. Please see our Website Privacy Notice.