RED HAT : HOW SYNTHETIC DATA CAN IMPROVE CYBERSECURITY IN BANKING
Synthetic data refers to artificially generated data that mimics the statistical properties and patterns of real-world financial data . By leveraging synthetic data , financial institutions can train AI models on large , diverse datasets without compromising sensitive information , enabling them to develop more robust and accurate AI applications for tasks like fraud detection and risk assessment .
Red Hat ’ s Monica Sasso and Richard Harmon discuss how synthetic data can be used to improve cybersecurity in banking and financial services .
Harmon highlights that AI is a huge focus for Red Hat . “ We ’ re this infrastructure layer of enabling customers to have all of their various AI applications running in a very consistent , simplified , secure , resilient manner ,” he notes .
“ It ’ s more about how you manage and simplify access for modellers . Every bank is modernising and monitoring its realtime payments system . The risk systems are real-time , but it ’ s also getting much more complex .”
Harnessing hybrid cloud and automation can allow banks to have a choice . “[ Red Hat ] is focusing on automation and how we can bring AI into some of our automation processes ,” Sasso says .
Harmon agrees , saying that growing synthetic data will make things simpler and more transparent for the financial sector .
He adds : “ AI is going to generate synthetic data that doesn ’ t exist . In areas like financial crime , you can generate new types of crime that criminals haven ’ t found , or more importantly , you create different ways of committing a certain type of crime .
“ When you have the synthetic data , you can share data , but can ’ t identify each other ’ s customers . Most importantly , you can share data about fraud and risk . That ’ s the future as a key driver . A middle ground .” fintechmagazine . com 71