In a poll conducted by Integral Ad Science (IAS) 69.0% of agency executives said that ad fraud was the biggest hindrance to ad budget growth, compared with more than half (52.6%) of brand professionals who said the same. How much is ad fraud costing advertisers? Nobody knows, but with estimates ranging from $6.5 billion to $19 billion, there's a lot at stake. Marketers are becoming more assertive in their demands for better fraud prevention measures and they are seeking to increase their knowledge of different fraud types – from bots to unauthorised domain reselling – and wider technology adoptions to drive their Marketing strategies overall. Ad tech providers will need to adapt their technology and techniques to meet this demand.
"Machine learning" is a computer science discipline that refers to the ability for machines to learn with data and carry out tasks that would typically require human intelligence. The technology is growing quickly: according to Gartner, more than half of data and analytics services will be performed by machines rather than human beings by 2022, which is 10 percent more than today. The emergence of machine learning and its implementation into consumer facing applications coincides conveniently with today's real-time economy. Machine learning drives a decrease in fraud before it impacts the victim, just as our society has become as impatient as ever. In fact, more than 60 percent of people increasingly feel that waiting for something that should happen instantaneously impacts their perception of the underlying brand -- which is especially true when it comes to identity or financial fraud.
"Machine learning" is not just a buzzword for futuristic applications; it is the concept of machines carrying out tasks on their own that would typically require human intelligence. Its emergence is very much happening now. It is at the top of Gartner's hype cycle. In fact, Gartner predicts that by 2022, more than half of data and analytics services will be performed by machines instead of human beings, up from 10 percent today. And while not all machine learning use cases include real-time analytics, there is a definitive growth trend in the market for real-time decision making powered by machine learning.
Dr Crystal Valentine, the company's vice-president of technology strategy, told iTWire in an interview that it was still the very early days of seeing machine learning and deep learning being put to work by enterprises outside academia. Dr Valentine has a background in big data research and practice and before joining MapR, she was a professor of computer science at Amherst College. She has authored various academic publications in the areas of algorithms, high-performance computing, and computational biology and holds a patent for Extreme Virtual Memory. As a former consultant at Ab Initio Software, working with Fortune 500 companies to design and implement high-throughput, mission-critical applications and as a tech expert consulting for equity investors focused on technology, Dr Valentine has developed significant business experience in the enterprise computing industry. Dr Crystal Valentine: Machine learning encompasses a number of different algorithms for training computers to solve specific tasks, including tasks that are part of larger artificial intelligence systems.
AI is about to go mainstream. It will show up in the connected home, in your car, and everywhere else. While it's not as glamorous as the sentient beings that turn on us in futuristic theme parks, the use of AI in fraud detection holds major promise. Keeping fraud at bay is an ever-evolving battle in which both sides, good and bad, are adapting as quickly as possible to determine how to best use AI to their advantage. There are currently three major ways that AI is used to fight fraud, and they correspond to how AI has developed as a field.