AI Fraud Detection Pattern Matching Module
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Solution Overview
Problem
Current methods for detecting telecommunication frauds are inadequate in addressing the rapidly changing behaviors of fraudsters and are not generic enough to handle multiple frauds simultaneously, as they rely on static solutions and are not adaptive to new fraud patterns.
Innovation Solution
A computer-implemented method using a pattern matching module trained on reference patterns stored in a database, which allows for real-time detection and adaptation through federated learning and blockchain-based collaboration, enabling the sharing and continuous updating of fraud patterns among users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static detection methods are used, then device complexity is reduced, but adaptability to new fraud patterns deteriorates
Solution Approach 1:
The system transitions from static fraud detection rules to dynamic adaptive detection. The fraud detection module continuously learns from new fraud patterns and updates detection models in real-time, allowing the system to adapt to evolving fraud techniques without requiring manual rule updates. This dynamic approach resolves the contradiction by making the system both adaptable and manageable through automated learning processes.
Solution Approach 2:
The system implements self-service through automated machine learning models that continuously train and update themselves using incoming transaction data. The fraud detection module automatically identifies new fraud patterns and adjusts detection parameters without external intervention, enabling the system to serve itself in maintaining up-to-date fraud protection while managing complexity through automation.
2Measurement precision
If specialized fraud detection systems are developed for each fraud type, then detection precision is improved, but device complexity increases
Solution Approach 1:
The system implements a universal fraud detection platform that handles multiple fraud types through a single integrated machine learning framework. Rather than deploying separate specialized systems for each fraud type, the unified system processes various fraud patterns (transaction fraud, account takeover, identity theft) through common detection mechanisms, achieving both precision and simplicity simultaneously.
Solution Approach 2:
The system merges multiple fraud detection capabilities into a single integrated platform. By combining various detection algorithms, data sources, and analysis methods into one unified system, the patent achieves comprehensive fraud detection precision while avoiding the complexity of managing multiple separate specialized systems. The consolidated approach allows shared resources and coordinated response mechanisms.
3Productivity
If real-time fraud detection is implemented, then productivity is improved, but use of energy increases
Solution Approach 1:
The system segments fraud detection processing into multiple levels: initial filtering of obviously fraudulent transactions, intermediate risk assessment for borderline cases, and detailed analysis only for high-risk transactions. This hierarchical segmentation enables real-time detection of critical fraud while reducing overall computational energy consumption by avoiding intensive processing of all transactions uniformly.
Solution Approach 2:
The system applies partial action by focusing computational resources only on transactions that exhibit suspicious patterns. Rather than performing full-depth analysis on every transaction, the system selectively applies intensive detection algorithms only where needed, achieving real-time fraud detection productivity while minimizing unnecessary energy consumption on legitimate transactions.
Data Source
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AI summary
The disclosure relates to a computer-implemented method for detecting frauds online in a telecommunication network, using a pattern matching module trained beforehand to learn reference patterns, each reference pattern comprising at least one parameter associated with at least one fraud method, said reference patterns being stored beforehand in a patterns database, the method comprising at least the following steps: a. when an outgoing or incoming transaction is detected on said telecommunication network, retrieving at least one parameter associated with said transaction to form a transaction pattern, b. operating said pattern matching module on said transaction pattern, and c. taking an action if a match is found between said transaction pattern and at least one reference pattern, indicating that the current transaction is a fraud.