Cybercriminals hunt in packs. Unlike old-school “lone wolf” hackers, today’s digital fraudsters regularly work together to share best practices and new exploitation techniques, and augment their reach across the internet. These exchanges no longer happen solely in secret, gated hubs, but on the open web in social media comment threads and chat forums.

Novice bad actors surface as sophisticated attackers when they deploy the tools and techniques sold and taught by widespread criminal enterprises. So while not every fraud attack is actually the work of a highly organized crime syndicate, a rapidly increasing number of them may as well be—making it more difficult for businesses to detect and combat fraud across channels and vectors. 

Whether directly working together as a criminal enterprise or indirectly working together through sharing of information on the web, the reality is that businesses are consistently under attack by sophisticated networks of bad actors. It takes a network to fight a network, and this is why Sift has invested in ensuring that our solutions help to connect fraud fighters across the globe. 

The Sift Global Network: Data, community, and shared intelligence

Our machine learning-based decision engine is built on top of our global network of deep, diverse data. While other solution providers have opt-in consortium intelligence networks, consortium is at the foundation of our solution. Every day we see bad actors traverse different verticals, which show up as shared fraud signals that our clients benefit from.

This network is updated in real time with each decision fraud analysts make. While a Sift client may be seeing a bad actor for the first time, Sift has often already seen them show up elsewhere in our network—which allows our clients to detect sophisticated fraud attacks on the first attempt, rather than only finding out what they’re up against after multiple compromised transactions have already taken place. 

Scale matters

One particular fraud solution provider advertises that they have the largest network of online retailers. What they don’t tell you is that their network is missing significant presence from other verticals, like fintech or social & dating apps. Both of these verticals are known to attract fraudsters for social engineering scams, and if a solution provider is not serving a diverse set of verticals, their fraud intelligence network is equally incomplete.

At Sift, we serve 16 different sub-verticals, which provide us a holistic view of fraudster activity thanks to the wide array of use cases offered across our platform. While many fraud solutions are limited to payment fraud use cases, Sift continuously evaluates the risk of payments, logins, account signups, and spam and scam threats.  In addition to protecting businesses across a diverse set of verticals and use cases, we are evaluating the risk of 1 trillion events annually—resulting in the largest fraud intelligence network in the industry.

Encouraging partnership & collaboration

We believe our human network is just as vital to the fight against rising fraud as our network of global data. Sifters is our customer community, open to all Sift users. A first of its kind, Sifters allows fraud fighters across the globe to tap into the experience of their peers, share best practices, and get the latest news about Sift product innovations. 

Sift is here to empower fraud fighters—not displace them

When a decision-as-a-service vendor is used as a total solution, it means the business has decided to completely outsource their fraud program. The tools typically offered to the end-users are not for fraud fighting, but for performance monitoring. For passionate fraud fighters, this means taking on a business operations role or seeking a new role elsewhere. Unlike these types of vendors, Sift is focused on empowering fraud fighters, not displacing them.

Get more fraud prevention market guidance in our Evaluation guide to online fraud solutions.

This is the final post in our series on the importance of transparency, control, and community in fraud prevention. Read part 1, or go to part 2.