User Behavioral Biometrics for Real-Time Fraud Anomaly Detection
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Solution Overview
Problem
Fraudulent user activities, such as credit card fraud, account takeover, and promotion fraud, are difficult to detect in online environments due to the anonymity and global reach of bad actors, leading to significant financial and security breaches.
Innovation Solution
A mechanism for detecting anomalous user behavior using user behavior anomaly prediction models, including Isolation Forest, variational auto-encoder, and one-class SVM, to generate probability scores for anomalous behavior, which are aggregated to determine and report anomalous actions in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fraud detection methods are used, then implementation is simple, but detection precision is insufficient for fraudulent activities
Solution Approach 1:
The system segments fraud detection into multiple specialized models: Isolation Forest for anomaly detection, variational auto-encoder for behavior reconstruction, and one-class SVM for boundary definition. Each model processes user behavior data independently and their results are aggregated, allowing high detection precision through division of specialized functions while managing complexity through modular architecture.
Solution Approach 2:
The system changes parameters by transitioning from traditional rule-based detection to machine learning-based detection with multiple probabilistic models. Each model outputs probability scores that are aggregated to determine anomalous behavior, enabling adaptive parameter adjustment and high-precision detection that adapts to evolving fraud patterns.
2Loss of time
If real-time anomaly detection is implemented, then fraud mitigation is timely, but computational resources increase
Solution Approach 1:
The system applies partial action by processing only the most critical behavior features through multiple specialized models rather than analyzing all possible data points. The aggregation of probability scores from selective models enables timely detection with reduced computational overhead compared to comprehensive analysis of all user interactions.
Solution Approach 2:
The system performs preliminary action by pre-training the Isolation Forest, variational auto-encoder, and one-class SVM models on historical user behavior data before real-time deployment. This preliminary training establishes baseline behavior patterns, allowing the system to quickly detect anomalies in real-time without performing heavy computational tasks during actual fraud detection events.
Data Source
AI summary
Techniques for determining anomalous user behavior in connection with an online application are disclosed. In one embodiment, a method is disclosed comprising obtaining user behavior data in connection with a user of an application, generating feature data using the obtained user behavior data, obtaining one or more user behavior anomaly predictions from one or more anomaly prediction models trained to output a user behavior anomaly prediction in response to the feature data. Each user behavior anomaly prediction indicates a probability that the user behavior is anomalous. A user behavior anomaly determination is made using the user behavior anomaly prediction(s).