Mobile Call Fraud Detection Using Webhooks and Shared Risk Ratings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Fraudulent telephone calls and impersonation have become prevalent due to the widespread availability of customer information online, necessitating systems and methods to proactively identify potential fraud in telephonic communications.
Innovation Solution
A method and system using a fraud detection application on a mobile device that employs machine learning algorithms to analyze telephonic communications, generate webhooks with metadata, and leverage a network of mobile devices and an entity server for real-time fraud detection and alerting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection methods are used, then system complexity is low, but fraud detection capability is insufficient
Solution Approach 1:
The patent introduces machine learning models as intermediary components between the telephonic communication data and fraud detection decisions. These models process audio, text, and metadata through multiple layers of analysis, acting as intelligent mediators that enhance detection capability while managing system complexity through modular architecture.
Solution Approach 2:
The fraud detection system is segmented into multiple independent components: audio analysis module, text analysis module, metadata analysis module, and risk scoring module. Each segment handles specific aspects of fraud detection, allowing the system to achieve high reliability through specialized processing while maintaining manageable complexity through modular design.
2Measurement precision
If real-time analysis of all call data is performed, then fraud detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs partial analysis by focusing on specific fraudulent indicators rather than analyzing all call data equally. The machine learning models are trained to identify key patterns and anomalies that are most indicative of fraud, allowing accurate detection without the need to process every single data point in real-time.
Solution Approach 2:
The fraud detection system operates continuously throughout the call duration, constantly analyzing incoming data streams. This continuous monitoring allows the system to maintain high detection accuracy while processing data in real-time batches, balancing precision with processing efficiency.
3Reliability
If multiple data sources are integrated for fraud analysis, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal fraud detection platform that handles multiple data sources (audio, text, metadata, third-party data) through a single integrated system. The machine learning models are designed to process various data types uniformly, and the risk scoring mechanism universally evaluates all inputs, thereby improving detection reliability while managing integration complexity through standardized processing pipelines.
Data Source
AI summary
A method for using a fraud detection application running on a mobile device to monitor, in real-time, a telephonic communication on the mobile device. Data may be continuously extracted from the telephonic communication by the fraud detection application. The method may include determining from the extracted data, that the data includes a fraud indicator. In response to the determining, a webhook may be generated that includes a payload storing metadata of the telephonic communication. The method may include identifying a group of mobile devices that may be associated with the data and further transmitting the webhook to each of the group of mobile devices and an entity server supporting the fraud detection application. The method may include receiving a rating of the telephonic communication from at least some of the mobile devices and from an entity server and using the ratings to determine a threshold level of fraud.