Fraud Detection System Using Time Series Clustering
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Solution Overview
Problem
Retailers face financial losses due to fraudulent transactions, such as unauthorized purchases and returns, which existing technologies struggle to detect and prevent in real-time.
Innovation Solution
A fraud detection system that generates time series from sales data, applies an alerting algorithm based on clusters of feature data to identify anomalies, and transmits alerts to prevent or identify fraudulent transactions, allowing retailers to scrutinize and mitigate fraudulent activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional fraud detection methods are used, then false positives may occur and legitimate transactions may be blocked, but fraudulent transactions cannot be effectively detected and prevented in real-time
Solution Approach 1:
The system performs preliminary actions by generating time series data from historical sales information and pre-computing feature data before transactions occur. The alerting algorithm is prepared in advance with pre-defined thresholds and anomaly detection rules, enabling rapid real-time detection without compromising transaction processing speed
Solution Approach 2:
The patent replaces traditional mechanical fraud detection systems with an automated electronic system that uses time series analysis and machine learning algorithms. The computing device automatically generates time series, extracts features, applies alerting algorithms, and sends notifications without human intervention, achieving both high accuracy and real-time processing
2Measurement precision
If manual scrutiny of transactions is performed to identify fraud, then detection accuracy may improve, but processing time increases and real-time prevention is compromised
Solution Approach 1:
The system implements self-service by automatically generating time series data, extracting features, applying alerting algorithms, and sending notifications without requiring manual intervention. The computing device autonomously performs all detection tasks, maintaining high precision while enabling real-time transaction processing
Solution Approach 2:
The system incorporates feedback mechanisms where the alerting algorithm continuously monitors time series data and adjusts anomaly detection based on patterns learned from historical data. When anomalies are detected, the system sends notifications that can trigger further automated actions, creating a closed-loop feedback system that improves detection precision without increasing processing time
3Difficulty of detecting and measuring
If comprehensive sales data is analyzed to detect fraud patterns, then detection capability improves, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex fraud detection task into distinct modules: time series generation from sales data, feature extraction, alerting algorithm application, and notification sending. Each module handles a specific aspect of the analysis, making the overall system more manageable and easier to implement while maintaining comprehensive detection capability
Solution Approach 2:
The patent transforms raw sales data into time series parameters and further extracts meaningful features that capture fraud patterns. By changing the parameters from raw data to structured time series and features, the system reduces computational complexity while improving detection capability through focused analysis of relevant patterns
Data Source
AI summary
This application relates to apparatus and methods for identifying anomalies within a time series. In some examples, a computing device receives sales data identifying a sale of at least one item, and aggregates the received data in a database. The computing device may generate a plurality of time series based on the aggregated sales data. The computing device may extract features from the plurality of time series, and generate an alerting algorithm that is based on clusters of the extracted features. The computing device may apply the alerting algorithm to a time series generated from received sales data to determine whether the time series is an anomaly. Based on the determination, the computing device may generate and transmit anomaly data identifying whether the time series is an anomaly, such as to another computing device.