Subnetwork Solutions: How Machine Learning is Tackling Affiliate Fraud

In the previous edition of our subnetwork series, in which we unpacked the problems the affiliate industry is facing, we uncovered the following:

• How a lack of transparency in sub-affiliate networks can open doors for rogue affiliates to commit malpractices that could harm brands and their revenues.
  
• Blind subnetworks, which do not disclose the identities and activities of their sub-affiliates, generate the majority of these cases. However, the issues can be prevented by implementing stricter policies for affiliates and providing full transparency to networks and advertisers.
  
• There are subnetworks working to regain a positive reputation by focussing on premium publishers with influential traffic, as well as implementing several policies to share more data with partners and drive towards more transparency.
  
• Policies also ought to be put in place preventing brands and agencies from poaching publishers, so subnetworks can ensure that increased transparency won't precipitate a loss of affiliates.

Machine learning offers a quick fix

The growth of affiliate marketing has brought about an increase in fraudulent activity, with fraudsters constantly devising new methods to steal from unsuspecting advertisers. As Kalen Bushe, VP of Growth at TrafficGuard, notes that the total cost of ad fraud in 2022 was $81 billion, and is predicted to increase to $100 billion by 2023, with affiliate marketing accounting for a small but growing part of this.

The cost of fraudulent activity is significant. For instance, according to a CHEQ report around 15% to 30% of clicks through affiliate platforms are invalid, and between 10% to 15% of conversions are fraudulent.  This calls for robust solutions that can accurately identify and weed out fraudulent activity.