The impact of machine learning-based fraud detection on Ad performance in programmatic advertising: an empirical evidence from marketing professionals

Owing to the rapid development of programmatic advertising, the number of fraudulent activities has increased, thereby reducing the performance of digital marketing campaigns. Machine learning (ML)-based fraud detection systems have been identified as a solution to such fraud and to enhance advertising performance. This study examines the effects of ML-based fraud detection on the most important ad performance parameters, real-time bidding (RTB) efficiency, and consumer trust. This study utilizes information obtained from 780 marketing professionals to investigate the direct and indirect impacts of fraud detection systems on ad performance. The findings indicate that ML-based fraud detection positively impacts ad performance, such as click-through rates (CTR) and return on ad spend (ROAS), by improving RTB efficiency and decreasing fraud-induced distortions. Nonetheless, consumer trust, although positively influenced by fraud detection, has a less positive correlation with advertisement performance than anticipated. This study also analyzes how data privacy regulations moderate the relationship and increase the efficiency of RTB and consumer trust. The results can be useful to practitioners in the digital advertising field, as they show that it is crucial to combine fraud detection systems and follow the rules of data privacy to make ads more effective. This research adds to the theoretical background of the role of fraud detection in programmatic advertising and provides viable suggestions on how digital advertising results can be enhanced.