Loss of revenue due to fraud in the telecommunications sector remains the ever-present fear of operators. Fraudsters are always inventive and their practices and behaviors are constantly evolving. They are constantly finding new ways to abuse and/or bypass operators’ networks, which leads operators to redouble their vigilance and to equip themselves with appropriate means that are constantly adaptable to new forms of fraud in order to ensure the protection of their network usage, prevent fraud in real time and ensure the viability of their business models.

Our integrated FRTools system is the most efficient and appropriate solution to fight against the various current and evolving types and forms of fraud and abuse of operators’ networks, regardless of the digital voice or data services targeted.

Our solution ensures minimal turnaround time for fraud on our partners’ networks through real-time blocking.

Our solution combines a traditional rules engine with advanced machine learning capabilities to provide increased coverage of all your services and minimize network fraud execution time with real-time blocking capabilities.

At the heart of our FRTools fraud management system is an engine with a comprehensive set of rules that cover detection techniques such as thresholds, expressions and trends. The rules engine includes a combination of threshold rules, geographic rules, pattern (sequential) rules, combinatorial rules, ratio/proportion based rules, negative rules, alert list based rules, etc. that allow the operator to monitor for advanced threats on the network. The rules built into the solution provide a controlled environment for counter-fraud managers to configure and validate rules before configuring them on the actual network. Intelligent alarm qualification built into the solution allows an analyst to detect at least 90% of fraud by having to analyze only 10% of the alarms in the alarm stack. Our FRTools fraud management system is integrated with machine learning algorithms that leverage historical data of fraudulent behavior to create supervised and unsupervised models. These models allow telecom operators to find hidden patterns of fraudulent activity in the data, significantly reducing fraud detection time.