Robocall Steering via KYC Score Analysis
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Solution Overview
Problem
Robocalls, or unsolicited spam calls, have increased significantly, posing a nuisance and unwanted traffic on telephony systems, with existing technologies failing to effectively track, identify, and prevent these calls due to caller ID spoofing and malicious behavior.
Innovation Solution
A system and method that computes a 'Know Your Customer' (KYC) score based on various metrics such as call volume, caller ID matching, disconnection rates, and traceback requests to identify and block robocalls, using a robocall management engine with components like ingress, traceback, STIR/SHAKEN, network analytics, honeypots, and network blocking to dynamically evaluate and manage voice traffic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional call filtering methods are used, then basic call blocking is possible, but accuracy and effectiveness in identifying robocalls deteriorates due to caller ID spoofing
Solution Approach 1:
The system performs preliminary actions by computing KYC scores before call blocking occurs. It continuously monitors call metrics (call volume, caller ID matching, disconnection rates, traceback requests) and pre-calculates risk scores to determine blocking actions in advance, improving identification accuracy before the actual call blocking happens
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring call metrics and adjusting KYC scores based on real-time data. The network analytics component receives feedback from traceback requests and disconnection rates, dynamically updating the risk assessment to improve both identification accuracy and blocking effectiveness
2Measurement precision
If comprehensive monitoring of call metrics is implemented, then robocall detection capability improves, but system complexity increases
Solution Approach 1:
The system segments the complex monitoring function into distinct components: ingress component for call intake, traceback component for origin tracking, network analytics component for metric collection, and network blocking component for action execution. Each component handles specific metrics (call volume, caller ID matching, disconnection rates, traceback requests) independently, making the overall complex system manageable and maintainable
Solution Approach 2:
The network analytics component serves multiple functions simultaneously: it collects call metrics, computes KYC scores, analyzes call patterns, and provides data for blocking decisions. This multi-functionality reduces the need for separate dedicated systems for each monitoring task, thereby managing complexity while maintaining comprehensive detection capability
Data Source
AI summary
Disclosed are systems and methods for robocall steering over voice-hosted traffic networks. The disclosed framework provides novel systems and methods for increasing the accuracy and efficiency in tracking, identifying, blocking and preventing robocalls and robocallers. The disclosed systems and methods provide mechanisms for identifying and removing unwanted voice traffic from networks. The disclosed systems and methods analyze voice traffic over a predetermined period of time (e.g., 1 day or 30 days, for example), and leverage this information into a “know your customer” (KYC) score. This score enables incoming calls to be routed, controlled and/or blocked as they are communicated over voice networks.