Memory Management for Real-Time Fraudulent Transaction Analysis
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Solution Overview
Problem
Traditional computer systems face challenges in efficiently and quickly detecting security breaches due to limited computing resources, leading to delayed detection of fraudulent transactions, which hampers immediate remedial measures.
Innovation Solution
Implementing real-time cluster detection techniques that efficiently manage memory resources by determining which transaction entries to store in memory and evicting less relevant ones, allowing for rapid analysis of patterns in transaction data to identify potentially fraudulent transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computer systems analyze vast amounts of transaction data to detect security breaches, then detection accuracy is improved, but detection speed deteriorates due to limited computing resources
Solution Approach 1:
The patent segments the transaction data analysis into multiple processing stages: initial filtering of transaction entries, cluster formation of similar transactions, pattern matching against known fraud patterns, and detailed analysis of suspicious clusters. This segmentation allows the system to quickly eliminate large volumes of normal transactions while concentrating computational resources on identifying potential fraud cases, thereby maintaining high detection accuracy while improving detection speed.
Solution Approach 2:
The patent changes key parameters of the analysis process by adjusting memory allocation dynamically based on transaction volume and complexity, modifying clustering thresholds to balance sensitivity and false positives, and adapting pattern matching criteria based on emerging fraud trends. These parameter changes enable the system to optimize its performance characteristics in real-time, achieving both rapid detection and accurate identification of security breaches.
2Speed
If more computing resources are allocated to analyze transaction data quickly, then detection speed is improved, but resource consumption increases
Solution Approach 1:
The patent applies partial action by performing comprehensive analysis only on transaction clusters that exhibit suspicious patterns, while applying simplified filtering to the majority of normal transactions. This selective depth of analysis allows the system to achieve rapid detection of fraud cases without expending excessive computing resources on analyzing every single transaction in detail, thereby improving detection speed while controlling resource consumption.
Solution Approach 2:
The system implements self-service through automated adaptive optimization where the fraud detection system automatically adjusts its own resource allocation and analysis depth based on observed transaction patterns and system performance metrics. This self-regulating mechanism enables the system to maintain high detection speed while dynamically optimizing resource usage, eliminating the need for constant manual resource management.
3Measurement precision
If all transaction entries are stored in memory for analysis, then detection accuracy is improved, but memory requirements increase
Solution Approach 1:
The patent extracts and retains only the most relevant transaction entries in memory based on their similarity to known fraud patterns and their clustering characteristics. Instead of storing all transaction data, the system identifies and keeps in memory only those entries that form suspicious clusters or exhibit anomalous patterns, while storing less critical data in external storage. This extraction approach maintains detection accuracy by preserving essential fraud-indicative data while dramatically reducing memory requirements.
Solution Approach 2:
The system implements a discarding and recovering mechanism where transaction entries are temporarily discarded from memory after being processed and analyzed, and only those entries that remain relevant for ongoing pattern recognition are retained. This allows the system to manage limited memory resources efficiently by continuously making space for new transaction data while preserving historical data that remains useful for detecting evolving fraud patterns.
Data Source
AI summary
Computer memory management during real-time fraudulent transaction analysis is disclosed. In an embodiment, a method includes receiving a transaction entry and determining whether the transaction entry is potentially fraudulent, including by determining whether the transaction entry is correlated with another transaction entry in the computer memory or an already-identified suspicious pattern of other transaction entries. The method further includes determining whether to evict the transaction entry from the computer memory based on the determination of whether the transaction entry is potentially fraudulent. The method includes providing a result of fraudulent transaction analysis performed using the computer memory that has been optimized.