Usage Fraud Analytics

Prevent Fraud on your Network

Eliminate known frauds with real-time action, discover unknown frauds in near real-time, and significantly reduce fraud runtimes with Mobileum’s industry-changing, intelligent counter fraud solution.

Big Data

Big data technology enables integration with diverse data sources and can effectively handle several terabytes of transactions along with historical data, accurately detecting fraud in real-time

Data Science

Advanced Machine learning capabilities utilize supervised and unsupervised machine learning techniques to detect and solve various known and unknown fraud scenarios

Active Testing

Active Testing agent that is completely configurable and can be scheduled to generate test call results that can be further analyzed, resulting into a self-correcting solution and significantly reduced false positives

Integrated Action

Integrated action systems significantly reduce the time to take corrective action (blocking a fraudster/service/route etc.) on detected frauds. This significantly reduces the overall fraud runtime opportunity

Collaborative Approach

Mobileum’s world-class Data Scientists and Business Analysts collaborate with operators to help tune the solution to their requirements as well as discovering new frauds and fraud scenarios

Product Overview

Mobileum’s Counter-Fraud analytics enables eliminating of known frauds, uncovering of unknown frauds and fraudulent methods in near real-time, as well as enabling real-time counter fraud actions thus allowing faster time-to-response and minimizing overall fraud run time.

The solution guides operators from an era of increasingly inefficient rule-based solutions to the world of predictive machine learning, without the need to understand complex algorithms to fight fraud and ultimately arrest revenue leakage.

The solution combats the major voice and data frauds such as International Revenue Share Fraud (IRSF), Domestic Revenue Share Fraud (DRSF), By-Pass, CLI Refiling, OTT By-Pass and Data Tunneling Fraud.

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