Fraud in digital advertising is a growing, worldwide epidemic that affects market players of all sizes and across all industries resulting in multi-billion dollar wasted ad spend, artificially inflated consumer prices, degrading digital user experience, and security breaches at unprecedented scale. This project aims to advance fraud prevention and customer lifetime value (LTV) prediction using artificial intelligence (AI) to prevent ad spending on malicious and low-quality traffic sources. The development of entirely new transparent, resilient, and adaptive fraud detection algorithms combined with accurate and timely predictive LTV modeling could redefine digital media buying and completely reshape the digital life of every company and every individual who is connected to the internet through any type of device. The most significant R&D challenges facing the project are largely related to engineering and artificial intelligence. The scalability required to train models using millions of data points at high speeds is difficult to design for. Another unique challenge is presented by the fact that millions of models will need to be stored for every client to achieve the most detailed level of insights possible. This type of scale is quite rare in machine learning and unprecedented in advertising technology. Also, developing algorithms that combine many artificial intelligence techniques to detect fraud based on fundamental traits and signatures so they are adaptable to all current and future manifestations requires a significant leap in several disciplines of machine learning. Both types of challenges will be overcome through the ingenuity of the project partners, each with a wealth of relevant experience and leading expertise in their respective core competencies.
Project leader: Heiko Hildebrandt
Institution: TARGET CIRCLE AS