Argyle Data vs Outerthought Lily comparison

Argyle Data has had the privilege of working with global leaders and visionaries on their strategies for revenue threat analytics, big data, and machine learning. What consistently comes up is that best-in-class carriers know the revenue threats that they have been attacked with in the past. What they don’t know is how to prepare for future attacks that will likely incorporate new types and methods of revenue threats.

What is critical to understand is that a) criminals are continually innovating; b) each subscriber will have many devices, many channels, and many potential attack points; and c) we need a better way to detect new fraud and protect customers and carriers in this new world – today in 2015, not in 2020.

This requires an effective strategy for the use of big data and machine learning in the areas of:

Fraud Threats

Analytics apps for identifying threats from various types of domestic fraud and roaming fraud

Profit Threats

Analytics apps for identifying threats from arbitrage, negative margin, high usage, and bill shock

SLA Threats

Analytics apps for identifying threats from network vulnerabilities and from roaming partners not meeting their SLA windows

Forensic Threats

Graph analysis application for analyzing 1st to 5th degrees of separation between data assets

Lily Enterprise sits on top of the Cloudera or Hortonworks Hadoop platforms, and considers the entire Hadoop stack and all data streams, including data warehouses, data lakes, CRM systems, mobile/online or social media activities, contact center applications and POS systems. Lily combines them into one cumulative, organized and actionable solution, and delivers real-time execution of predictive analytical models to help create individual customer profiles, Lily Customer DNA, to give organizations the most accurate, atomic-level view of their customers.