Learn how a large US-based bank used predictive analytics and machine learning to identify and prevent insider information security threats across sensitive applications in its retail banking and wealth management divisions.

Challenges

• Simple rule-based alerts proved inadequate for accurate and timely threat detection
• An expensive and inflexible technology stack limited threat detection to only a few applications, exposing the bank to vulnerabilities
• The existing solution was taking too long to develop and move use cases into production

Solution

Gathr enabled the use of predictive analytics and machine learning on a large data set from highly sensitive applications to automatically detect previously unknown threat scenarios and raise appropriate alerts to prevent predicted breaches.

Highlights

• Ingestion and data processing from 5x more applications at a fraction of the cost
• Data transformation in real-time
• Use of machine learning models on the log and complex event data
• Custom alerts to curb fraud in real-time