Fraud tanks your game’s reputation and directly impacts your bottom line. 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!

Don’t miss out!

Registration is free.

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.

As games evolve, the kinds of threats evolve too, leaving developers scrambling to keep up. Any of the hardware and software required to scam at scale are available for a price – and that price is usually low enough to keep scammers coming. Fraudsters are launching increasingly sophisticated attacks, and are able to pivot and change tactics often too quickly to catch. And since no player wants to trust a game that’s consistently riddled with hazards, once your game is targeted you’ll see a slow leak in subscriber numbers, which can become a major exodus when scammers start to overrun your game environment.

Even if you’re trying to stay on top of issues and take action as problems develop, legacy fraud detection methods can take days or weeks to sift through the damage – an endless amount of time for a gaming audience.

Machine learning and AI is changing the game for fraud fighting, with advanced processing, increased data availability, and the promise of far lower costs for far more sophisticated technology leading the way.


Machine learning leaves no stone unturned. The best way to detect fraud is to gather and analyze data on all transactions, events, and user data. The only way to crunch that amount of data is to add a machine learned algorithm to your fraud prevention mix, and set it loose to uncover patterns, flag dangers, and keep you ahead of the next attack, not cleaning up behind.

Algorithms evolve too. Machine learning systems not only comb through enormous amounts of data, they apply what they learn to their own algorithms, updating in real time to keep fraud signals current and evolved attacks from causing damage. That’s a big change from the current static rules-based systems that don’t learn, and become obsolete quickly.

Machine learning can’t be fooled. To the human investigator, or to a rules-based-system, both fraudulent accounts and real ones appear similar on the surface. AI-informed algorithms can dive deeply to uncover the fraudsters and avoid false positives along the way.

With highly accurate results that reduce the need for manual review, machine learning systems are ideal for reducing and preventing fraud for less money, and without impeding the user experience. To learn more about automating your fraud prevention, locking your game down tight, and keeping your reputation sparkling clean, don’t miss this VB Live event!

Register here for free!

In this webinar, you’ll learn:

  • How the gaming industry can secure gamer data and build trust
  • How account takeover, fake licensing, spam, and scams pose a particular challenge to gamers and gaming platforms
  • What policies your company should have in place around data breach ransom
  • How to combat trolling


  • Jeff Sakasegawa, Trust and Safety Architect, Sift Science
  • Dean Takahashi, Lead Writer, GamesBeat
  • Scott Adams, CEO, Former Director of Fraud & Risk, Riot Games
  • Rachael Brownell, Moderator, VentureBeat

Sponsored by Sift Science




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