Adaptive Fraud Detection Feature Acquisition by Risk Level
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Solution Overview
Problem
Existing fraud detection systems require a large number of feature amounts for all users, leading to prolonged detection times and increased processing load.
Innovation Solution
A fraud detection system that adjusts the acquisition method for feature amounts based on user fraud levels, acquiring feature amounts more quickly for users with lower fraud levels and performing more thorough checks for those with higher levels, allowing for parallel processing of different feature amounts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of feature amounts are acquired for all users to accurately detect fraud, then fraud detection accuracy is improved, but the detection time becomes longer
Solution Approach 1:
The patent applies local quality by differentiating the feature amount acquisition strategy based on user fraud levels. Users are divided into different groups (first users with lower fraud levels and second users with higher fraud levels), and different acquisition methods are applied to each group. This allows the system to optimize detection time for low-risk users while maintaining thorough detection for high-risk users, thereby resolving the contradiction between detection accuracy and detection time.
Solution Approach 2:
The patent implements dynamics by making the feature amount acquisition method adaptive and changeable based on user fraud levels. The system dynamically adjusts the acquisition strategy - using first acquisition methods for users with lower fraud levels and second acquisition methods for users with higher fraud levels. This dynamic adaptation allows the system to balance accuracy and time efficiency based on real-time risk assessment.
2Device complexity
If common settings are used for feature amount acquisition for all users, then system simplicity is maintained, but fraud detection accuracy deteriorates
Solution Approach 1:
The patent resolves this contradiction by applying local quality through user-specific acquisition settings. Instead of using a single common setting for all users, the system creates different acquisition methods tailored to different user fraud levels. This allows the system to maintain simplicity for the majority of low-risk users while applying more sophisticated acquisition methods only where necessary for high-risk users.
Solution Approach 2:
The patent applies segmentation by dividing the user base into distinct groups based on fraud levels. The first acquisition method is applied to first users (lower fraud levels) while the second acquisition method is applied to second users (higher fraud levels). This segmentation allows the system to optimize performance for different user categories without requiring complete complexity for all users.
3Reliability
If thorough feature amount acquisition is performed for all users, then fraud detection reliability is improved, but processing load increases
Solution Approach 1:
The patent applies local quality by implementing reliability optimization at the user level rather than uniformly for all users. Users with lower fraud levels undergo simpler acquisition processes, while users with higher fraud levels undergo more thorough acquisition. This localized approach maintains high reliability for risk detection while significantly reducing the overall processing load across the system.
Solution Approach 2:
The patent implements partial action by applying thorough feature amount acquisition only when necessary (for users with higher fraud levels) rather than for all users. For users with lower fraud levels, a reduced acquisition process is sufficient. This partial application of thorough detection maintains reliability for critical cases while improving overall processing efficiency.
Data Source
AI summary
A fraud detection system includes at least one processor configured to acquire, based on an action performed by each of a plurality of users, a score relating to a fraud level of the user, determine, based on the score of each of the plurality of users, an acquisition method for a feature amount of the user such that an acquisition time of the feature amount becomes shorter as the fraud level becomes lower, acquire the feature amount of each of the plurality of users based on the acquisition method determined for the user, and detect fraud made by each of the plurality of users based on the feature amount of the user.