Model Degradation Alerting via Distribution Analysis
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Solution Overview
Problem
Existing analytical models used for event detection, such as fraud detection, often degrade over time and fail to detect evolving threats, necessitating a method to determine when these models should be updated or replaced.
Innovation Solution
A computer-implemented method and system that utilizes a distribution analysis module to monitor model metrics, set alert thresholds based on risk tolerance ratings, and alert for model degradation by comparing current metrics to a normal distribution, incorporating survival analysis and ensemble approaches to assess model health.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If analytical models are used for extended periods to maintain productivity, then operational continuity is improved, but model degradation occurs leading to reduced detection accuracy
Solution Approach 1:
The system performs preliminary actions by continuously monitoring model metrics and detecting degradation trends before the model completely fails. The health assessment module evaluates model health scores and predicts future degradation, allowing proactive model updates before detection accuracy deteriorates below acceptable thresholds.
Solution Approach 2:
The system implements feedback mechanisms where model performance metrics are continuously monitored and fed back to the health assessment module. This feedback loop enables the system to detect degradation patterns, adjust health scores, and trigger model updates when degradation thresholds are reached, maintaining detection accuracy over extended operational periods.
2Reliability
If model updates are performed frequently to maintain detection accuracy, then reliability is improved, but computational resources and time are consumed
Solution Approach 1:
The system dynamically adjusts model update timing based on actual degradation rates observed in production. The health assessment module continuously evaluates model health scores and compares them against threshold values, triggering updates only when degradation exceeds acceptable levels. This dynamic approach optimizes the balance between maintaining detection accuracy and minimizing unnecessary update overhead.
3Reliability
If comprehensive model monitoring is implemented to detect degradation early, then reliability is improved, but system complexity increases
Solution Approach 1:
The system extracts and monitors only the most critical model metrics that indicate degradation, rather than comprehensively tracking all possible model parameters. The health assessment module focuses on key performance indicators and degradation patterns, simplifying the monitoring system while maintaining effective degradation detection capability.
Data Source
AI summary
A detection modeling system performs a distribution analysis to alert to model degradation. The detection modeling system may have a distribution analysis module configured to perform an alerting process in conjunction with a processing device. The distribution analysis module may receive a risk tolerance rating for alerting to degradation of an analytical model, and determine a threshold value for a model metric based on the risk tolerance rating. The model metric may be a measure of a parameter associated with the analytical model. The distribution analysis module may also monitor the analytical model for degradation using the threshold value for the model metric and model metric values and alert to model degradation of the analytical model based on the monitoring of the analytical model.