Network Activity Deviation Detection With Segment-Based Remediation
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Solution Overview
Problem
Existing systems fail to effectively detect and mitigate suspicious network activity deviations, leading to potential fraudulent transactions and disputes, without causing unnecessary disruptions to legitimate activities.
Innovation Solution
A system that monitors network activity deviations by categorizing users into segments based on historical data, identifying suspicious activities, and sending alerts to users, providing additional information about merchants to aid decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing monitoring systems are used to detect suspicious network activity, then some fraudulent activities can be identified, but legitimate activities are unnecessarily disrupted and detection accuracy is insufficient
Solution Approach 1:
The system segments users into different groups based on their historical network activity patterns, transaction behaviors, and risk profiles. By categorizing users into segments, the system can apply customized monitoring thresholds and detection rules specific to each segment's characteristics, thereby improving detection accuracy while reducing false positives for legitimate activities.
Solution Approach 2:
The system applies different monitoring strategies, alert thresholds, and detection sensitivity levels to different user segments based on their local characteristics. High-risk segments receive more intensive monitoring with lower thresholds, while low-risk segments experience minimal disruption with higher thresholds, optimizing both detection accuracy and operational ease for each local context.
2Measurement precision
If strict monitoring thresholds are applied to detect all suspicious activities, then detection sensitivity increases, but legitimate user activities are blocked and user experience deteriorates
Solution Approach 1:
The system dynamically adjusts monitoring thresholds and detection sensitivity based on real-time analysis of user behavior patterns, historical data, and current risk assessments. Thresholds are not fixed but adapt to each user's segment and activity context, allowing high detection sensitivity for suspicious patterns while maintaining high transaction throughput for legitimate activities.
Solution Approach 2:
The system changes key parameters such as alert thresholds, monitoring intensity, and detection sensitivity based on user segment characteristics and activity patterns. By adjusting these parameters dynamically, the system achieves precise detection of fraudulent activities without unnecessarily blocking legitimate transactions, thus maintaining both detection sensitivity and transaction throughput.
3Reliability
If comprehensive network activity data is collected and analyzed, then detection capability improves, but system complexity and computational resources increase
Solution Approach 1:
The system segments both users and network activities to enable targeted analysis. By dividing the comprehensive data set into manageable segments based on user categories, activity types, and risk levels, the system can analyze data more efficiently using distributed computing and specialized algorithms for each segment, reducing overall system complexity while maintaining comprehensive detection capability.
Solution Approach 2:
The system introduces intermediary components such as data preprocessing layers, feature extraction modules, and segmentation filters that organize and structure comprehensive network activity data before analysis. These intermediaries simplify the complexity by transforming raw data into structured segments that are easier to process and analyze, maintaining detection capability while reducing system complexity.
4Speed
If real-time analysis of network activity is performed, then response time to fraudulent activities improves, but computational energy consumption increases
Solution Approach 1:
The system implements periodic analysis cycles where network activity data is collected, segmented, and analyzed at optimized intervals rather than continuously. By using periodic batch processing combined with real-time segmentation and rule-based filtering, the system achieves fast response times for critical fraudulent activities while reducing computational energy consumption through efficient scheduling and resource allocation.
Data Source
AI summary
Systems and methods to detect network activity deviations are described. The network activity data can include a set of network activities of a user and can be used to categorize the user into a segment. Deviations in the network activity can be detected based on one or more patterns of previous network activities of the user. For example, a value associated with the network activity can be determined to deviate from an expected value by a threshold value, determined based on the segment. Remediation to prevent the network activity from impacting future activities of the user can be determined based on scores of the network activity's participants, which includes the user. In an example, the network activity can be removed from an account of the user based on the determined remediation.