False positives for cyber attacks

Example:  Discover patterns of fraudulent transfers and cut the number of false positives.  Photo: iStock (illustration)

Example: Discover patterns of fraudulent transfers and cut the number of false positives. Photo: iStock (illustration)

 

Naomi is the new CEO of a Taiwanese bank – she wants the bank to be Taiwan’s most trusted by improving its defences bank against cyber attacks.

Despite the numerous processes put in place, not all were followed through due to the high number of false positives and the bank recently suffered from a USD 10m fraud. Naomi wants to identify big exposures quickly and implement processes that will be followed through.

By using EyesClear, Naomi discovers that 95% of fraudulent amounts were larger than USD 5k and were transferred to only a handful of countries. By using this information, she can instruct the processes to be followed through only for those categories. This cut the number of false positives and made it easier for her subordinates. They started following the processes and fraudulent payments fell by 99% by the end of the year.