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
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.
- 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
The only all-in-one data pipeline platform
- One platform to do it all - ETL, ELT, ingestion, CDC, ML
- Self Service, zero-code, drag and drop interface
- Built-in DataOps, MLOps, and DevOps tools
- Cloud-agnostic and interoperable
Gathr is an end-to-end, unified data platform that handles ingestion, integration/ETL (extract, transform, load), streaming analytics, and machine learning. It offers strengths in usability, data connectors, tools, and extensibilty.
Gathr helped us build “in-the-moment” actionable insights from massive volumes of complex operational data to effectively solve multiple use cases and improve the customer experience.
IN THE SPOTLIGHT
"In-the moment" actionable analytics
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