AI is about to go mainstream. It will show up in the connected home, in your car, and everywhere else. While not as glamorous as 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 where 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 that is used to fight fraud, corresponding to how AI developed as a field.
With online criminals getting highly skilled at attacking enterprises every day, it's getting difficult for businesses to tell the difference between legitimate and fraudulent activity. Fraud detection has always been a cat and mouse game. With improvements in techniques to detect and prevent frauds, fraudsters keep changing their attack patterns to unearth new holes and vulnerabilities – which the detection solutions then shore up. However, the nature of this game is changing. The days where enterprises were able to keep fraudsters at bay by using static detection rules are long gone.
It's also been notoriously difficult to combat – until now. Learn about how artificial intelligence can keep your game and players safe from increasingly aggressive online criminals, when you join this VB Live event! There are over 2 billion gamers in the world. Almost half of them are shelling out cold, hard cash in those games – rounding up somewhere around $108.8 billion in revenue across platforms, devices, and game types. And all of them – from players to platforms – are incredibly vulnerable to the insidious types of fraud that infest every online game out there, which includes account takeovers, game hacks, credential ripoffs, and bots.
Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insurance policy could lead to heavy financial losses. In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information. In this paper, we introduce a device-sharing network among claimants, followed by developing an automated solution for fraud detection based on graph learning algorithms, to separate fraudsters from regular customers and uncover groups of organized fraudsters. This solution applied at Alibaba achieves more than 80% precision while covering 44% more suspicious accounts compared with a previously deployed rule-based classifier after human expert investigations. Our approach can easily and effectively generalizes to other types of insurance.
This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.