Machine Learning Call Forwarding Validation Platform
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Solution Overview
Problem
Call forwarding systems lack effective mechanisms to differentiate between legitimate and illegitimate call forwarding requests, leading to potential fraudulent activities and wastage of resources in investigating and resolving theft.
Innovation Solution
A machine learning-based validation platform that processes network and account data to determine the authorization of call forwarding, assigning scores to determine the legitimacy of calls and enabling automated actions to prevent fraudulent activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If call forwarding is allowed without validation, then call forwarding functionality is maintained, but fraudulent activities increase and resources are wasted on investigating theft
Solution Approach 1:
The system performs preliminary validation of call forwarding requests by analyzing network data and account data before the call is actually forwarded. A machine learning model predicts whether the call forwarding is legitimate or fraudulent, and the server device is notified in advance to block suspicious calls, preventing fraudulent activities before they occur and avoiding wasted investigative resources
Solution Approach 2:
The server device acts as an intermediary between the calling party and the called party. It receives call forwarding requests, validates them through the machine learning model, and either permits or blocks the call forwarding based on the prediction results, thereby controlling and monitoring call forwarding activities to prevent fraud
2Reliability
If machine learning validation is implemented, then fraudulent call forwarding is prevented, but system complexity increases
Solution Approach 1:
The machine learning model serves multiple functions: it analyzes network data, evaluates account data, predicts fraudulent behavior, and provides validation results to the server device. This multi-functional approach consolidates various validation tasks into a single system component, managing complexity while enhancing fraud prevention capability
Solution Approach 2:
The machine learning model automatically processes and validates call forwarding requests without requiring manual intervention. The system self-manages the validation process by receiving data, making predictions, and providing results, thereby reducing operational complexity despite the sophisticated validation logic
Data Source
AI summary
A device receives, from a server device, a request to determine whether a call is authorized to be forwarded, where the call is intended to be established between the server device and a first client device via a network, and the call is forwarded from the first client device to a second client device. The device receives, from the network, network data associated with the call and account data associated with a first user of the first client device. The device determines that the call is forwarded based on the network data, and processes the account data and information indicating that the call is forwarded, with a machine learning model, to determine a score indicating whether the call is authorized to be forwarded. The device provides the score to the server device to cause the server device to perform one or more actions.