Behavior-Based Transaction Risk Analysis System
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Solution Overview
Problem
Digital transactions are often compromised by unauthorized or malicious actors, leading to fraudulent activities due to stolen credentials or compromised devices, especially in environments vulnerable to Denial of Service (DoS) attacks, making it difficult to authenticate and authorize legitimate users effectively.
Innovation Solution
A system that utilizes user entity behavior-based information to provide secure access to computer networks by capturing contextual factors, calculating transaction risk, and comparing it to a predetermined threshold to determine approval, employing a context-aware risk-based approach that includes machine learning and artificial intelligence to predict and classify user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication methods are used to verify user credentials, then security against unauthorized access is maintained, but friction increases for legitimate users and vulnerability to DoS attacks persists
Solution Approach 1:
The system dynamically adjusts authentication requirements based on real-time risk assessment of user behavior patterns. Instead of static authentication rules, the system continuously monitors contextual factors and adapts security measures, allowing legitimate users to experience minimal friction while maintaining strong security for suspicious activities.
Solution Approach 2:
The system changes security parameters dynamically by calculating risk scores based on multiple contextual parameters (device characteristics, network conditions, user behavior patterns). Authentication requirements are adjusted based on these parameter changes, reducing friction for low-risk users while maintaining security for high-risk scenarios.
2Reliability
If strict authentication and authorization protocols are implemented to prevent fraudulent transactions, then security against malicious actors is improved, but transaction processing time and system complexity increase
Solution Approach 1:
The system performs preliminary risk assessment by continuously monitoring and analyzing user behavior patterns before transactions occur. By establishing baseline behavior profiles in advance, the system can quickly evaluate transactions against these pre-established patterns, reducing processing time while maintaining fraud prevention capabilities.
Solution Approach 2:
The system implements continuous feedback loops where transaction outcomes and user responses are fed back into the behavior analysis model. This real-time feedback mechanism allows the system to learn from each interaction and improve its fraud detection accuracy over time, reducing false positives that would otherwise increase processing time.
3Measurement precision
If comprehensive user verification processes are applied to all transactions, then detection of unauthorized access is improved, but system complexity and computational resources increase
Solution Approach 1:
The system applies different levels of verification intensity to different users and transactions based on their risk profiles. Instead of uniform comprehensive verification for all users, the system tailors the depth and type of analysis to local conditions, applying detailed anomaly detection only where necessary while using simpler methods for low-risk scenarios.
Solution Approach 2:
The system performs partial verification for routine transactions by focusing on key behavioral indicators rather than comprehensive analysis. For transactions that fall within established normal patterns, the system uses streamlined verification, reserving full comprehensive analysis for cases that exhibit anomalous characteristics.
Data Source
AI summary
Aspects of the disclosure provide techniques for using behavior based information for providing and restricting access to a secure website, or computer network and its assets to a user. Components of the system may include the following. Client remote computing device, network and browser unique attribute data collection and fingerprinting. Method for capturing user habits and fingerprinting with ability to detect abnormalities through AIML using mobile and wearable device applications. System for detection of normality of user behavior based on habits, and cyber transactions, device access and determining a confidence score associated with each transaction. Method for calculating individual transaction risk based on contextual factors such as user behavior, device, browser and the network traffic and request for authentication by account owner when risk greater than allowed threshold. Method and system to identify user device, browser, and behavior unique attributes, storing and later matching to infer change upon consequent transactions and measuring transaction risk through a search and match against classified set of static and dynamic attributes using a user, browser traffic, device search and match engine.