Imagine you’re running an ad campaign to generate leads for your clothing brand. You have also partnered with an ad fraud detection solution to detect and eliminate invalid traffic. However, one of your legitimate consumers raises a concern. Their card was blocked while making the transaction.
Who could be responsible for this?
You can blame the fraud detection vendor as they might have flagged a genuine consumer assuming it to be a fraudulent source.
While ad fraud prevention is an essential element to eliminate fraudulent sources from ad campaigns, there must be a holistic way to differentiate between a genuine user and a bot without compromising the security of the ad campaigns. This mistake not only costs the brand their revenue, but also the trust of a legitimate user.
Know how brands are impacted by this and what you must look for in an ad fraud detection vendor to avoid the case of false positives.
What is a false positive?
A false positive happens when a legitimate transaction is flagged as suspicious resulting in declining of payment or blocking of a genuine account. As a result, a request from a genuine customer is identified as a fraudulent source.
This error happens when a non-fraudulent transaction is flagged by a fraud detection system resulting in the decline of the transaction.
Why do false positives take place?
The fraud detection systems are programmed to detect fraud patterns in a campaign. However, sometimes the system fails to accurately differentiate between a legitimate and a fraudulent request. As a result, the brand has to bear the collateral damage of false positives.
To reduce the consequences of false positives, organizations have experimented with different approaches to try and differentiate between a legitimate and fraudulent user.
- Based on a checklist
This list includes the details like IP addresses, email addresses, and Device IDs that have been identified and marked as either “safe” or “unsafe”. For example, if an IP address is flagged for being a source of malicious or fraudulent activity, then it will be “blacklisted”. Unfortunately, these lists are no longer viable to detect fast-evolving fraud. These lists require continuous refreshing as they get outdated in a short span of time. And these manually designed lists are often imprecise, corrupted, or at the worst expired. As a result, these reputation lists often lead to an increase in the number of false positives.
- Based on Rules
The rules engines are software that is programmed to take actions based on specific criteria. For example, if a business has made a rule check to analyze the billing country and IP country. In this case, any mismatch will be an indication of a malicious account. These rules can be effective in some cases, but it has many limitations. The rules are highly reactive, and the results are based on past experiences. Furthermore, the rules are hard to manage especially in the case of large-scale data. As a result, the false positive number goes up.
- Based on Rule-based Machine Learning
In this process, a training dataset is processed with the help of AI and ML. In this case, all the possible outcomes are programmed with the correct answers to train the algorithm. With the help of supervised machine learning, the brands can detect certain patterns and insights from a set of data. This is further used to make predictions about future outcomes. This is a strong tool for fraud detection, but it has its own limitations. For example, in SML the algorithms require a certain command to perform their tasks. This limits the ability to detect new and unknown fraud attacks. And as the fraudsters adapt to new techniques at a faster pace, it is impossible for an SML-based solution to keep pace.
Why do brands need to act against false positives?
- Friction in users: Due to false positives, a genuine customer becomes the biggest victim. The most common case is when a customer attempts to pay to make a purchase, but for some unknown reason, the payment gets declined. A decline of a payment for an interested user can turn into a case of inconvenience and they drop out to purchase from a different brand or platform.
- Reputational damage: According to a report, 38% of online shoppers abandon their purchases when asked for an additional security check. They consider switching to a different brand when they experience poor service. Legitimate customers consider multiple layers of security and payment declines as an insult and often don’t take it in a positive light. Due to the inconvenience, sometimes they also end up spreading negative word-of-mouth which is a nightmare for the brands and tarnishes their brand reputation.
- Loss in revenue: Due to false-positive cases, not just the genuine consumers get impacted but also the brands. The brands lose the real customers and the potential revenue from genuine sales. In this case, the credit card companies have to bear the cost as they don’t get their fees.
Questions to Ask your Ad Fraud Vendor to Reduce False Positives
- Do they analyze the entire lifecycle to ensure comprehensive protection?
- Do they look at all the possible types of fraud attacks?
- Do they identify and take preventive actions for new & emerging threats?
- Do they differentiate between legitimate and fraudulent activity in real-time?
How mFilterIt ensure to reduce false positives?
When detecting fraudulent sources in the ad campaigns, we expect an average of 4-5% false-positive cases. However, to ensure that the brand doesn’t have to lose genuine customers to protect its ad campaigns from fraudsters, our ad traffic validation suite ensures to focus on various parameters like:
- Deeper Fraud checks
- Evaluation for every data set to make a decision on
- Prioritization for sources that will convert
- Able to detect sophisticated BOTS and emerging threats
- Analysis based on Behavioural and Deterministic data
Conclusion
A true ad fraud detection and prevention solution must be effective enough to help the brand in different parameters. A successful fraud detection will happen for a brand when it enhances the customer experience and nurtures them while keeping the doors closed for the bad actors.
mFilterIt’s ad fraud solution is committed to providing a seamless experience for the brand and all the stakeholders by bringing down the number of false positives. With the capabilities of AI, ML, and data science, we detect fraud at the transaction level and keep different checks to differentiate between a fraudulent source and a genuine user.
It’s time to keep the fraudsters at bay and let the genuine customers have the best experience resulting in the success of the brand.
