Lavastorm Transaction Warehouse

Detecting fraud and performing other usage-based analytics requires the ability to acquire, enrich, organize and analyze very large volumes of complex data from a variety of sources. The Lavastorm Transaction Warehouse (LTW) allows organizations to load, enrich, aggregate and analyze high volume transaction data, such as CDRs, IPDRs, and interval usage data.

LTW overcomes the trade-off between high transaction volumes and deep-inspection, extracting and gathering CDRs and other event data from different sources such as switches, billing systems, rating systems, mediation systems, probes, and IP networks. It enriches and analyzes these records via business rules, and transforms the data into new categories of useful information to facilitate fraud detection, interconnect settlement and usage analysis.

 
  • LTW enables users to detect issues or uncover patterns often hidden in large volumes and, in concert with the Lavastorm Resolution Center (LRC), receive and manage alarms, and perform work-flow enabled case management to ensure timely and effective resolution
  • The LTW aggregation engine, coupled with advanced fraud and usage profiling capabilities, enables rich forensics while greatly minimizing the false positives commonly resulting from high-volume analytics, improving analyst efficiency and maximizing results
  • Operational managers can, themselves, configure detection rules, thresholds and analytics, to adapt the system as quickly as the business needs change – without the traditional IT or vendor cost and time impacts
  • LTW provides near real-time analytics for Near Real-Time Roaming Data Exchange (NRTRDE) and other time-critical analytics, enabling timely intervention to minimize losses
  • LTW is incrementally scalable to acquire and analyze more than 3 billion records a day without sacrificing deep-inspection rigor, minimizing up-front investment while protecting long-term growth

Features

High volume data load, parsing and federation

Provides control across multiple record-types, offering scalability on both volume and complexity

Aggregation Engine

Provides the ability to adapt to any data source dimension

Business Rules Engine

The engine offers the ability to transform, enrich, and analyze data through conditional logic for trusted and meaningful data

Transaction correlation

Provides the ability to manage duplicate or inconsistent data across multiple sources for trusted and meaningful data