Sift Logo Several blue dots forming a sphere to the left of the word Sift in italic font.
  • Products

    Digital Trust & Safety Suite

    Fight fraud without sacrificing growth

    Learn more →

    Passwordless
    Authentication

    Account
    Defense

    Content
    Integrity

    Payment
    Protection

    Dispute
    Management

    Sift
    Connect

    PSD2
    Solution

    New Releases & Enhancements

  • Partners

    Sift Partner
    Program

    Join the leader in Digital Trust & Safety

    Learn more →

    Commerce platform partners


  • Industries

    One solution, many applications

    Learn how Sift can work for your industry

    Learn more →

    Featured industries


    Fintech

    Retail

    Food & Beverage

  • Customers

    See case studies by industry

    Sift works across every use case and region

    Learn more →

    Featured customers


  • Resources

    Explore our resources

    Access trends, guides, and insights from Sift

    Learn more →

    Blog

    Ebooks

    One Pagers

    Demos

    Videos

    Webinars

    Infographics

    Podcasts

    Trust & Safety University

  • Fraud Center
  • 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 Suite
  • Passwordless Authentication
  • Account Defense
  • Content Integrity
  • Payment Protection
  • Dispute Management
  • Sift Connect
  • PSD2 Solution
  • New Releases & Enchancements
Why Sift
  • Salesforce
  • Magento
  • Shopify
Industries
  • Fintech
  • Retail
  • Food & Beverage
Customers
Resources
  • Blog
  • Ebooks
  • One Pagers
  • Demos
  • Videos
  • Webinars
  • Infographics
  • Podcasts
  • Trust and Safety University
Fraud Center
About
  • Search Careers
  • Our Company
  • Contact Us
  • Engineering Blog
Request a DemoSign In
  • Blog Home
  • Account Fraud
  • Content Fraud
< prev / next >
Share this article on LinkedIn
Tweet this article
Share this article on Facebook
SOCIALICON
Share this article via email

Build vs. Buy: 5 Capabilities You Need in a Fraud Tool

By Kevin Lee  / 

18 Jun 2018

When considering whether to build or buy your fraud prevention tools, there are a lot of criteria to assess. What is your in-house level of technical expertise? What are your time constraints? Would developing this technology be a core competitive advantage? And more…

In our recent webinar, Building versus Buying: Understanding the Right time for Each Option as You Grow, we outline all of the most important questions you need to ask during this important decision. We also run through the five capabilities that will make up your “full stack” for fraud prevention.

Whether you will build or buy, aim for a solution that includes all five of these layers:

Let’s start from the bottom…

Layer 1: Data

The data you collect for fraud prevention will come from in-house. And that’s great! There’s so much rich information that your customers are leaving on your app – from their device information and IP location to the pages they navigate to and the buttons they click on – that your fraud solution will have plenty of data to learn from.

Is homegrown data the only option? Actually, no…for example, if you use Sift Science, you also benefit from access to data from across our global network of customers. That means that anytime a user takes action on another customer’s site or app, their risk score automatically updates.

Layer 2: Machine learning

Speaking of which…now that you have that data, what are you going to do with it? Machine learning is the most scalable and flexible technology for proactively preventing fraud attacks before they happen. It continually learns from new data – in Sift Science’s case, our Live Machine Learning gathers new intel from across our entire customer network, and updates immediately.

If you’re on the fence about whether machine learning is a necessary part of your fraud stack just think: fraudsters are already using it themselves. These days, fraud networks are moving beyond brute force attacks. Instead, they’re using sophisticated technology like machine learning to try and reverse engineer your systems. You need to fight fire with fire, and lean forward with machine learning.

Layer 3: Workflow and rules automation

Some companies think you need to have either rules or machine learning – but you can have both! While machine learning is a “smarter” approach to stopping sophisticated fraud, you probably still have a need to enforce certain policies, like which countries you can and can’t ship to. You don’t need machine learning for this  — you can just use rules in an automated fashion to make sure it doesn’t happen.

Layer 4: Analyst tools and feedback

If you’re using rules and workflows, you’re going to need a robust console or other tool to manage them. Talking with companies, we’ve learned that teams often spend more than 50% of their time just collecting data by logging into multiple tools — rather than spending time on more strategic tasks, like analyzing that data or making decisions. The average merchant uses 9+ tools to fight fraud.

A robust fraud tool can consolidate all of that data into one place, so you don’t have to search for it. Remember that analysts are probably your most expensive line item. They need to be able to make decisions quickly and efficiently.

Layer 5: Reporting

The final piece of your fraud stack? A way to measure your success. You’ll need to track your KPIs and how well you’re meeting your objectives. Having a clean, clear reporting structure in place will help you make better decisions going forward.
Want a deeper dive into whether you should build or buy your fraud solution? Watch our free webinar!

Related

build vs. buydatamachine learningreportingrules automationwebinar

Kevin Lee

Kevin Lee is Vice President of Digital Trust & Safety at Sift. Building high-performing teams and systems to combat malicious behavior are what drive him. Prior to Sift, Kevin worked as a manager at Facebook, Square, and Google in various risk, spam, and trust and safety roles.

  • < prev
  • Blog Home
  • next >
Company
  • About Us
  • Careers
  • Contact Us
  • 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

Our newsletter delivers industry trends, insights, and more.

You're on the list.

You can unsubscribe at any time. Please see our Website Privacy Notice.

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

© 2022 Sift All Rights Reserved Privacy & Terms

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.