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Revelatory robots

How futuristic technology is shaping the anti-fraud profession



Data analytics has been helping organizations detect fraud for years. But the future of fraud prevention and detection is looking toward new technologies, like artificial intelligence and machine learning, to stop fraudulent behavior before it costs companies millions.

Robots have been playing chess for decades. On May 11, 1997, IBM supercomputer Deep Blue made history as the first computer to beat Garry Kasparov, the world chess champion at the time, in a six-game match under the World Chess Federation standard time controls. (See Twenty years on from Deep Blue vs Kasparov: how a chess match started the big data revolution, by Mark Robert Anderson, May 11, 2017, The Conversation.)

Computers now are taking on other computers or robots. On Dec. 7, 2017, Google’s artificial intelligence (AI) subsidiary DeepMind challenged then world champion chess-playing program Stockfish 8. AlphaZero, the game-playing AI created by DeepMind, taught itself how to play chess in less than four hours and beat Stockfish 8 in a 100-game match up. AlphaZero won or drew all 100 games. (See AlphaZero AI beats champion chess program after teaching itself in four hours, by Samuel Gibbs, Dec. 7, 2017, The Guardian.)

According to The Guardian article, DeepMind’s programmers didn’t give AlphaZero any human input except for the basic rules of chess, but it achieved a superhuman level of play in chess and shogi (a similar Japanese board game) within 24 hours. The difference between AlphaZero and its competitors is that DeepMind doesn’t give its machine-learning approach any human input apart from the basic rules of chess. It learns the rest by playing itself over and over using “self-reinforced knowledge.”

Self-reinforced knowledge is coming in handy not just for chess-playing robots — the anti-fraud profession is using the same technology to help fraud examiners detect fraudulent behavior before it costs organizations millions in losses.

 


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By Rhea_Reinbold