Machine learning (ML)-based data flow solutions have made it possible to ingest and process data from a large number of applications at an affordable cost. This not only helps expand the overall scope of threat detection, but also helps significantly accelerate the development and production of threat detection applications. Solutions that offer advanced capabilities like in-memory data transformation and distributed in-memory stateful processing also bolster insider threat detection by enabling faster data quality scoring, cleansing, and enrichment. Recent advances in ML have helped create dynamic models that periodically learn normal baseline behavior and detect anomalies based on both dynamic and static factors such as identities, roles, and excess access permissions; correlated with log and event data. Using ML models on the log and complex event data can help reduce false positives from thousands to tens per day and make the end-to-end process of identifying suspicious behavior automated, accurate, and timely.

The business impact of the COVID-19 pandemic continues to unfold worldwide for the financial services industry. The "new normal" has not only given rise to unprecedented operational challenges, but also provided fertile ground for hackers and threat actors to take advantage of increased vulnerabilities. In June 2020, the Internet Crime Complaint Center at the FBI reported a 75% rise in daily digital crime since the start of stay-at-home restrictions. These cyber-crimes are not only becoming more frequent, but also more difficult to detect and more complicated to prevent. Financial institutions like banks that run hundreds of sensitive customer-facing applications are at extremely high risk.