Dynamic Threshold Recalibration for Fraud Detection Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning models for fraud detection lack flexibility and fail to adapt to evolving fraud patterns, leading to inefficient resource allocation, overreactions, and insufficient actions due to static thresholds that do not account for unique computing infrastructure risk profiles and subtle fraud patterns like card testing.
Innovation Solution
A system utilizing multiple prediction models and a function-generation machine learning model to dynamically adjust thresholds based on real-time data, monitoring performance metrics, and identifying subtle patterns to differentiate between legitimate and malicious activity, allowing for tailored responses to varying risk levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static thresholds are used in machine learning models for fraud detection, then the system is simple and easy to operate, but the system lacks flexibility and cannot adapt to evolving fraud patterns
Solution Approach 1:
The patent implements dynamic threshold adjustment where the machine learning model continuously learns from new data and automatically updates detection thresholds. This allows the system to adapt to evolving fraud patterns while maintaining ease of operation, as the adjustment occurs automatically without manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms where performance metrics and fraud detection results are fed back into the machine learning model to continuously improve and recalibrate thresholds. This feedback loop enables the system to adapt to changing fraud tactics while maintaining operational simplicity through automated learning.
2Device complexity
If static thresholds are used, then the system is simple to implement, but it leads to false positives and inefficient resource allocation
Solution Approach 1:
The patent dynamically changes threshold parameters based on learned patterns and performance data. Instead of using fixed static thresholds, the system continuously adjusts detection parameters to optimize accuracy and reduce false positives, while the overall system remains relatively simple through automated parameter adaptation.
Solution Approach 2:
The machine learning model performs self-service by automatically monitoring its own performance and adjusting thresholds without external intervention. This self-adjustment capability reduces false positives and improves reliability while maintaining system simplicity, as the model manages its own optimization.
3Use of energy by moving object
If static thresholds are used, then the system requires less computational resources, but it cannot provide tailored responses to varying risk levels
Solution Approach 1:
The system uses dynamic threshold adjustment that adapts to different risk levels and computing infrastructure characteristics. The machine learning model adjusts detection sensitivity and response thresholds dynamically, enabling tailored responses without requiring excessive computational resources for manual configuration.
Solution Approach 2:
The patent applies local quality by customizing detection thresholds and responses for different computing infrastructures and risk scenarios. The machine learning model identifies unique characteristics of each infrastructure and adjusts parameters locally, providing tailored responses while maintaining efficient resource usage through targeted computation.
4Ease of operation
If conventional machine learning models are used with fixed workflows, then the system is easy to operate, but it cannot dynamically select relevant data sources or adapt to specific characteristics
Solution Approach 1:
The patent implements dynamic workflow adjustment where the machine learning model automatically selects and weights relevant data sources based on current fraud patterns and infrastructure characteristics. This dynamic selection occurs automatically without manual intervention, maintaining ease of operation while enhancing adaptability.
Solution Approach 2:
The machine learning model serves multiple functions by simultaneously performing fraud detection, threshold adjustment, data source selection, and response customization. This multi-functionality is achieved through a single unified model that handles various tasks adaptively, maintaining operational simplicity while providing versatile capabilities.
Data Source
AI summary
Discussed herein are methods and systems for dynamically recalibrating machine learning model parameters. In one method, a server executes one or more prediction models to process network operations from various data feeds, in order to identify the likelihood of these operations being fraudulent or malicious. The server monitors performance data, such as the operation and execution metrics of network operations, and evaluates whether the performance values, like recall values, meet defined thresholds. If the performance data fails to meet these thresholds, the server employs a function-generation machine learning model to predict a threshold modification function. This modification function is then applied to adjust the relevant thresholds. Utilizing the modification function, the server dynamically revises one or more parameters of the prediction models to enhance their accuracy and efficacy.