AI In Detecting & Preventing Ad Fraud
Ad fraud poses a significant threat in today’s digital advertising landscape, creating potential roadblocks for marketers and advertisers alike. Fraudsters exercise several deceptive practices in a bid to generate a quick buck out of the advertiser’s budget. However, to negate this sophisticated ad fraud and ensure fraudsters do not get paid, a proactive approach is the need of the hour, and AI can be a viable solution.
AI and its role in preventing ad fraud
AI has been around in some form or another for decades, but recent algorithmic improvements, as well as the exponential increase in processing power and storage capacity, have propelled this technology into the mainstream. In terms of combating ad fraud, it brings a level of efficiency and precision when compared to manual analysis. ML, or machine learning, as a subset of AI, can help advertisers detect fraudulent activities in real-time and has the potential to take immediate action before any grave impact. According to research by Statista, ML solutions helped save $3.5 billion of ad spend in the Asia-Pacific region in 2022.
This data is a testament to what AI and ML can do in terms of protecting advertisers from fraudulent activities and saving their ad spend. It can be leveraged essentially for a thorough analysis of data with speed, accuracy, and proactiveness, which can help them detect anomalies and prevent some serious damage.
Providing through analysis
Ad fraudsters have evolved and are employing sophisticated techniques to mimic human behaviour. For instance, bots can be used to emulate human actions such as click patterns, scrolling, and more, making it hard to distinguish between real and fake. Manual ad fraud detection is typically only capable of assessing data in two to four dimensions, which can be time-consuming and subject to detection gaps. In this aspect, ML can uncover links that are hidden from two-dimensional analysis and investigate all additional possible dimensions of a data set. This multidimensionality allows for a deeper knowledge of the data, making it effective to spot ad fraud that may pass muster in a manual analysis. Due to the ML models' fast capacity to alter, adapt, and make predictions, marketers can stay ahead of the curve. This allows fraud protection to operate in close to real-time and at scale.
Enhancing anomaly detection capabilities
Prior to the widespread use of ML solutions, advertisers used methods such as blacklists and rule-based detection and mitigation to negate ad fraud. The blacklists swiftly identify the resources that do not have any human traffic. However, fraudsters can change their IP addresses to circumvent them, and blacklists can isolate IP addresses rather than the ad fraud itself, resulting in high volumes of false positives. In addition, rule-based detection and mitigation tend to identify thresholds that, when exceeded, can block traffic sources. However, this methodology only succeeds with the ad fraud tactics that have been identified
before. ML-based solutions in this regard can be a viable solution as they are contextual and more sympathetic to the conditions where rules are static and based on generalisation. Moreover, ML models are constantly evolving in conjunction with the norms related to valid traffic, making them efficient for anomaly detection.
Enabling a proactive approach
ML is proactive by nature and can be effectively trained to segregate valid traffic from invalid traffic. It can examine traffic patterns and other pertinent data in order to find probable ad fraud indications. As a result, advertisers may more easily recognise IVT (invalid traffic) and therefore get a good picture of what clean traffic looks like. With this information at their disposal, suspicious activity can be reduced by restricting and redirecting advertisements on a website or mobile application to genuine users. Additionally, ML-powered tools can be used as a proactive defence to instantly initiate preventative measures before ad fraudsters reach the ad budget.
All things considered
As technology has advanced, so has the potential for advertisers to communicate with their target audiences. Additionally, fraudulent activities are also getting sophisticated, and billions of dollars are being funnelled out of the digital advertising industry. According to digital ad fraud statistics from the ANA (Association of National Advertisers), the cost of digital ad fraud is an astonishing $120 billion annually.
However, AI and ML technologies have also matured, resulting in their efficient use in detecting and preventing ad fraud. ML not only provides a thorough analysis of data but also enhances fraud detection capabilities and prevents ad fraud at its earliest, supporting advertisers' efforts to prevent ad fraudsters from getting paid.
(Himanshu Nagrecha is the Vice President, India & South Asia at TrafficGuard)
