Automated Anomaly Detection in E-commerce Data Streams
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Solution Overview
Problem
Current digital fraud detection solutions rely heavily on human intervention and prior knowledge, are inefficient, and fail to automatically identify anomalous patterns in digital data streams, making them ineffective in detecting digital frauds in e-commerce environments without manual effort.
Innovation Solution
A system and method that automatically identify anomalous patterns in e-commerce data by receiving a data stream, determining monitoring metrics at a target time, comparing them to benchmark metrics, and flagging outliers as potentially fraudulent transactions, allowing users to define metrics and logic for anomaly detection without prior knowledge of frauds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human intervention is used to detect fraud and create rules, then detection accuracy can be maintained through expert knowledge, but the system requires continuous manual effort and cannot operate automatically
Solution Approach 1:
The system performs self-learning by automatically analyzing transaction data streams to identify anomalous patterns without requiring manual rule creation. The machine learning model continuously adapts to new fraud patterns autonomously, eliminating the need for continuous human intervention while maintaining detection accuracy
Solution Approach 2:
The patent replaces manual human analysis (mechanical system) with automated machine learning algorithms. The system uses computational models to detect fraud patterns that would traditionally require human experts to manually identify and codify into rules, thereby achieving both automation and accuracy
2Reliability
If prior knowledge of fraud patterns is used to create detection rules, then detection can be guided by experienced insights, but the system cannot detect novel fraud patterns without previous knowledge
Solution Approach 1:
The system employs dynamic machine learning models that continuously adapt to new fraud patterns as they emerge in the data stream. Unlike static rule-based systems, the model can learn and adjust to novel fraud techniques in real-time, providing both reliability for known patterns and adaptability for new threats
Solution Approach 2:
The system performs preliminary analysis of data streams to establish baseline patterns of normal behavior. By pre-processing and understanding normal transaction patterns, the system can more reliably detect deviations (anomalies) that indicate fraud, whether known or novel patterns
3Adaptability or versatility
If manual rule creation is used for fraud detection, then detection logic can be customized based on domain expertise, but the process is time-consuming and reduces productivity
Solution Approach 1:
The system automatically generates detection logic by analyzing data patterns themselves, eliminating the time-consuming manual rule creation process. The machine learning model self-adapts to customize detection parameters based on the specific characteristics of the transaction data, providing both customization and high productivity
Solution Approach 2:
The system creates virtual models (copies) of normal transaction behavior through machine learning. These digital twins of normal patterns enable rapid automated detection without requiring manual copying of rules from expert knowledge, significantly improving detection speed while maintaining customization
4Productivity
If automated pattern detection is implemented, then detection speed and productivity improve, but the system complexity increases requiring advanced algorithms
Solution Approach 1:
The patent replaces complex manual analytical processes with machine learning algorithms that, while computationally sophisticated, automate the complexity management. The system handles algorithmic complexity internally through automated model training and pattern recognition, delivering high productivity without requiring users to manage the underlying complexity
Data Source
AI summary
A system and method for automatically identifying an anomalous pattern. The method encompasses receiving, a stream of data. The method further comprises determining, a monitoring metric for at least one of one or more dimensions and one or more groups of dimensions associated with the stream of data, at a target time and at a benchmark time period. Further the method comprises identifying, the monitoring metric at the target time as an outlier to the monitoring metric at the benchmark time period based at least on a threshold value. The method further comprises automatically identifying, the anomalous pattern based at least on said identification of the monitoring metric for at least one of the dimension(s) and the group(s) of dimensions at the target time as the outlier to the monitoring metric for at least one of the dimension(s) and the group(s) of dimensions at the benchmark time period.