The project will identify and test combinations of AI (machine learning, neural network) algorithms for detection of suspicious activity (anomaly detection) on the IT network of a large bank or company. The project will benchmark which algorithms work best to identify instances of insider fraud based on high-volume continuous streaming event and content data from all endpoints (computers, laptops, tablets, phones) on a corporate IT network. Applying these algorithms will make it possible for banks and companies to detect and mitigate insider fraud (theft of money or data, compliance violations) as it happens, in real time.