Machine Learning-Based Real-Time Threat Detection For Banks - AI Summary

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.