Cybersecurity is a growing area of risk in healthcare, and organizations are grappling with the vulnerabilities and the ways patient data can be used against patients and organizations. From identity theft to healthcare fraud, waste and abuse, cybercriminals breached 642 accounts of 500 or more patient profiles in 2020. That's a rate of more than 1.76 per day, reports HIPAA Journal, adding up to 29 million healthcare records breached last year. Security breaches cost healthcare companies $6 trillion dollars by the end of 2020. According to Health IT Security, three security data breaches in 2020 alone affected almost 2,000,000 records, opening opportunities for identity theft and online fraud.

Because COVID-19 made it difficult for consumers to venture out and run their usual errands, FIs needed to find other ways to provide their services. The only way for them to really keep up with the speedy digitization was through the implementation of AI systems. To further discuss all things AI, PaymentsJournal sat down with Sudhir Jha, Mastercard SVP and head of Brighterion, and Tim Sloane, VP of Payments Innovation at Mercator Advisory Group. Jha believes that there were two fundamentally big changes that occurred in banking during the pandemic: the environment began constantly shifting, and person-to-person interactions were abruptly limited. "Every week, every month, there were different ways that we were trying to react to the pandemic," explained Jha.

The team from the university's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx developed the new platform that can identify cyberattacks 85% of the time and even reduce the amount of false positives by a factor of five. AI2 goes through data and then spots suspicious activity through unmanned machine learning. From there, human reviewers check for signs of a security breach, a solution that can predict attacks with precision and eliminate the need to pursue bogus intelligence leads. AI2 uses three machine learning algorithms for detecting suspicious events, but just like other AI systems it also needs human feedback to verify its findings, so the system is constantly being enhanced through the team's so-called'continuous active learning system'. For computer science professor Nitesh Chawla of University of Notre Dame, the research is a potential'line of defense' against fraud, account takeover, service abuse, and other attacks faced by consumer-oriented systems today.