Threat Detection with StreamAnalytix, Anomaly Detection

Case Study

Real-time insider threat detection using machine learning for a Fortune 500 bank

5X scope expansion | 10X cost reduction | 4X performance improvement | 10X faster application development and production

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.


  • 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


StreamAnalytix 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.


  • 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

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Anomaly Detection with Machine Learning at Scale (India)

Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets.


Detect and prevent insider threats with real-time data processing and machine learning

Insider threats are one of the most significant cybersecurity risks to banks today. These threats are becoming more frequent, more difficult to detect, and more complicated to prevent.

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