Transaction Anomaly Training Using Synthetic Context Swaps
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Solution Overview
Problem
Existing transaction processing systems face challenges in real-time, high-volume processing due to the need for minimal latency, security constraints, data siloing, and the complexity of implementing machine learning models on outdated infrastructure, leading to low accuracy and high false positives in fraud detection.
Innovation Solution
A machine learning system tailored for real-time transaction processing, utilizing neural network architectures and dynamic training methods, capable of handling large transaction flows and data segregation, providing fast inference and sub-second processing latencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rule-based systems are used for transaction processing, then implementation is straightforward, but accuracy in fraud detection is low and false positives are high
Solution Approach 1:
The patent replaces traditional mechanical rule-based systems with machine learning models that automatically learn patterns from data. The system uses neural networks and other ML algorithms to detect fraud, eliminating the need for manually programmed rules and significantly improving detection accuracy while reducing false positives.
Solution Approach 2:
The machine learning system performs self-training by continuously learning from transaction data without requiring manual reconfiguration. The system automatically adapts to new fraud patterns and adjusts its detection criteria, making it self-updating and eliminating the need for continuous manual intervention to improve accuracy.
2Measurement precision
If machine learning models are applied to real-time transaction processing, then fraud detection accuracy improves, but processing speed and latency constraints are violated
Solution Approach 1:
The system performs preliminary actions by pre-computing features and preparing data in advance, then using these pre-processed inputs for rapid real-time inference. Critical calculations and data transformations are done beforehand so that during actual transaction processing, only lightweight prediction operations are needed, meeting latency requirements.
Solution Approach 2:
The patent applies partial action by using simplified ML models for real-time processing that capture essential fraud patterns without requiring full computational power. The system uses selective feature extraction and optimized model architectures that provide sufficient accuracy for real-time decisions without the computational burden of more complex models.
3Reliability
If data is stored in siloed partitions for security, then security is maintained, but ability to perform large-scale analysis is reduced
Solution Approach 1:
The patent introduces an intermediary layer that sits between siloed data partitions and the machine learning models. This intermediary aggregates and pre-processes data from multiple security-partitioned sources, creating a unified view that enables comprehensive analysis while maintaining the security boundaries of the original siloed systems.
Solution Approach 2:
The system segments the analysis process into separate stages: data collection from siloed sources, centralized aggregation in an intermediary layer, and model processing. This segmentation allows the system to respect security partitions while still achieving large-scale analysis capability through the coordinated operation of segmented components.
4Productivity
If transaction processing volume increases, then business growth is achieved, but system infrastructure becomes overloaded and performance degrades
Solution Approach 1:
The patent replaces traditional mechanical infrastructure with machine learning-based processing that scales differently. Instead of linearly increasing infrastructure capacity to handle more transactions, the system uses intelligent algorithms that can process variable volumes efficiently, adapting computational resources to actual processing needs rather than peak capacity requirements.
Data Source
AI summary
A method of training a supervised machine learning system to detect anomalies within transaction data is described. The method includes obtaining a training set of data samples; assigning a label indicating an absence of an anomaly to unlabelled data samples in the training set; partitioning the data of the data samples in the training set into two feature sets, a first feature set representing observable features and a second feature set representing context features; generating synthetic data samples by combining features from the two feature sets that respectively relate to two different uniquely identifiable entities; assigning a label indicating a presence of an anomaly to the synthetic data samples; augmenting the training set with the synthetic data samples; and training a supervised machine learning system with the augmented training set and the assigned labels.