Counter-fraud Engine Adjusts Thresholds via User Feedback
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Solution Overview
Problem
Counter-fraud management systems lack the ability to dynamically adjust thresholds based on user feedback, leading to a high rate of false-positive identifications, which increases the workload and resource consumption without effectively identifying fraudulent activities.
Innovation Solution
Implementing a system that captures user feedback during triage and performs semantic analysis using graphs to translate it into quantifiable scores, which are then used to adjust parameters and reduce false-positive alerts through a machine-learning technique, thereby enhancing the counter-fraud operation management engine's performance over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If counter-fraud management systems use data to flag various activities, then fraud identification capability is improved, but the rate of false-positive identifications increases
Solution Approach 1:
The patent implements a feedback mechanism where user responses to flagged activities are captured and used to adjust system parameters. The system monitors user interactions with alerts and uses this feedback to dynamically modify threshold values and parameter settings, thereby reducing false positives while maintaining fraud detection effectiveness.
Solution Approach 2:
The system dynamically adjusts parameters such as alert thresholds, sensitivity levels, and decision boundaries based on accumulated user feedback and performance metrics. By changing these parameters over time, the system optimizes the balance between detecting actual fraud and minimizing false alarms.
2Reliability
If the amount of data related to fraud identification increases, then detection coverage is improved, but resource consumption increases
Solution Approach 1:
The system applies partial analysis by focusing computational resources on high-risk transactions and patterns identified through feedback, rather than uniformly analyzing all data. This selective approach maintains comprehensive detection coverage while reducing overall resource consumption by concentrating processing power where it is most needed.
3Measurement precision
If user feedback is captured and used to adjust parameters, then false-positive rate is reduced, but system complexity increases
Solution Approach 1:
The system performs self-adjustment by automatically modifying its own parameters based on captured user feedback. The feedback processing engine autonomously analyzes user responses and adjusts threshold values without requiring manual intervention from system administrators, thereby managing complexity internally while delivering simplified operation to users.
Data Source
AI summary
Disclosed aspects relate to counter-fraud operation management. A counter-fraud operation may be executed using an initial set of parameter values for a set of parameters of the counter-fraud operation. A set of user counter-fraud activities of a user may be monitored corresponding to a user interface. A set of user feedback data may be captured to determine a feedback-driven set of parameter values for a set of parameters of the counter-fraud operation. The feedback-driven set of parameter values may be determined for the set of parameters of the counter-fraud operation. The counter-fraud operation using the feedback-driven set of parameter values may be executed.