Using AI, ML to prevent cyber crime

He says reports reveal that the amount of successful ransomware attacks grew between 50 and 100% last year.

“A great example for this is banking and credit card systems. These systems are met with multitudes of fraudulent transactions daily. For years, the banking industry has invested trillions in automated tools, and specifically ML models, in combating fraud.” “To reach high levels of accuracy, AI models require immense amounts of diverse data, and computing resources, to train on. Since these aren't always available, many of the resulting models must compromise on some aspect of accuracy. This usually means a high number of false positives, or false negatives.”

He says this can result in reduced trust in the models, and users avoiding or double checking the predictions of the models.

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