Caller ID Credibility Verification via Network Data Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The widespread ability to falsify Automatic Number Identification (ANI) and Caller ID information has compromised the integrity of telephone transactions, leading to significant financial and non-financial fraud, as the deregulation and decentralization of telecommunications networks have made it easy for individuals to manipulate ANI data, undermining trust and security in financial services and other industries.
Innovation Solution
A method and system that analyze the credibility of ANI information in real-time by using a combination of real-time telephone network status, forensics, network data, and predictive analytics, which decomposes the calling party number, checks its validity, and compares it against historical patterns to determine the authenticity of the call, providing a confidence metric for validating the ANI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If ANI control is implemented to display main telephone number on outgoing calls, then ease of operation is improved, but reliability deteriorates due to ability to falsify ANI
Solution Approach 1:
The system performs preliminary validation of ANI information by analyzing call patterns, network routing data, and historical credibility metrics before the call is answered. This advance verification establishes a credibility score that is displayed to the called party, allowing them to make informed decisions about answering the call while maintaining legitimate ANI control functionality.
Solution Approach 2:
An intermediary validation system is introduced between the calling party and the called party. This intermediary analyzes ANI information through multiple data sources including network routing verification, call pattern analysis, and cross-referencing with known fraudulent number databases. The intermediary provides a credibility assessment without blocking legitimate calls, thus maintaining ease of operation while improving reliability.
2Adaptability or versatility
If telecommunications network is deregulated and decentralized to allow new telephony services, then adaptability is improved, but reliability deteriorates due to ANI spoofing
Solution Approach 1:
The system implements dynamic validation that adapts to different call types, network conditions, and risk profiles. Rather than a static blocking approach, the system continuously adjusts validation stringency based on real-time analysis of call patterns, network routing consistency, and historical data. This dynamic approach maintains network flexibility while providing adaptive protection against spoofing.
Solution Approach 2:
The system changes multiple parameters simultaneously including analyzing call timing patterns, network routing paths, geographic consistency, and historical credibility metrics. By monitoring and evaluating multiple parameters rather than a single criterion, the system can distinguish between legitimate calls from diverse telephony services and fraudulent spoofed calls, maintaining adaptability while ensuring reliability.
3Reliability
If ANI validation is performed by placing test calls to verify number authenticity, then reliability is improved, but loss of time increases due to additional call placement
Solution Approach 1:
The system performs partial validation by analyzing a subset of available data sources rather than executing complete test calls for every incoming call. It uses quick checks including network routing verification, call pattern analysis, and database cross-referencing that can be performed in seconds. Test calls are reserved for high-risk scenarios where partial validation is inconclusive, thus minimizing time loss while maintaining reliability.
Solution Approach 2:
The system performs preliminary analysis using readily available data such as network routing information, call pattern history, and geographic consistency checks before committing to time-consuming test calls. This preliminary filtering identifies low-risk calls that can be validated quickly and reserves test calls for high-risk scenarios, significantly reducing the overall time loss while maintaining high reliability through selective deep validation.
Data Source
AI summary
A method of and system for discovering and reporting the trustworthiness and credibility of calling party number information, such as Automatic Number Identification (ANI) or Calling Number Identification (Caller ID) information, or for inbound telephone calls. The disclosed method entails the use of real time telephone network status and signaling, network data, locally stored data, and predictive analytics. Practice of the disclosed method is neither detectable by nor intrusive to the calling party, and the method can be implemented into existing enterprise, telecommunications, and information service infrastructures.