Citation:

Ankush Singhal, 2022. "Navigating Ad Fraud in the Age of AI: Techniques for Detection and Prevention in Programmatic Advertising", ESP Journal of Engineering & Technology Advancements 2(2): 125-134.

Abstract:

Advertiser fraud is a significant risk factor for the advertising industry, especially for programmatic advertising, since bidding and automation make it possible for fraudsters to manipulate the system quickly without being noticed. These activities include clicking fraud, bot traffic, and domain spoofing, which results in cash losses and affects ROI by channeling the advertisement budget towards fake impressions or fake clicks that do not provide for the traffic from the target audience. Programmatic ad technologies and ad fraud have advanced, with IP blocking and manual approaches inadequate in dealing with its scale and velocity. This position has made the advertising industry need to develop more sophisticated, preventive measures to identify fraud in actual operations. Due to ad fraud, AI has proved valuable when it comes to identifying some of the significant outliers that would have been hard to decipher among the huge amounts of data. Employing ML and deep learning models, these AI tools can scrutinize identification data from many users and analyze performance parameters of campaigns in real-time, looking for emergent signs of fraud. Approaches such as anomaly detection, supervised learning, unsupervised learning, and neural networks improve the accuracy of ad fraud detection and corroborate itself to new forms of fraud. Exploring how these techniques may be used to protect digital ad campaigns, this paper overviews present-day research on programmatic ad fraud detection and provides prevention methods. The conclusions made reemphasize the ability of AI to cut down ad fraud, thus protecting the advertisers’ funds and improving the entire legitimacy of the digital advertising space.

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Keywords:

Ad Fraud, Artificial Intelligence, Programmatic Advertising, Machine Learning, Fraud Detection, Anomaly Detection, Bot Traffic, Click Fraud.