Survival Analysis for Analytical Model Degradation Alerts
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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 adapt to changing conditions, leading to reduced accuracy and effectiveness, necessitating a method to determine when these models should be updated or replaced.
Innovation Solution
A computer-implemented method and system that performs survival analysis on analytical models by determining their health values, training periods, and failure periods, allowing for the identification of a survival time period and enabling alerts for model degradation through distribution analysis and ensemble approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If analytical models are deployed for long-term use, then operational efficiency is improved, but model accuracy degrades over time due to changing conditions
Solution Approach 1:
The system performs preliminary actions by continuously monitoring model performance metrics and detecting degradation trends before the model becomes completely ineffective. Survival analysis is used to predict the remaining useful life of the model, allowing proactive replacement or retraining before accuracy falls below acceptable thresholds, thus maintaining both operational efficiency and reliability.
Solution Approach 2:
The system implements feedback mechanisms by continuously collecting model performance data, comparing it against baseline metrics, and using survival analysis to generate predictions about model degradation. This feedback loop enables dynamic adjustment of model replacement timing, ensuring models are updated at optimal moments to maintain accuracy while maximizing operational efficiency.
2Reliability
If analytical models are frequently updated, then model accuracy is maintained, but operational interruptions increase
Solution Approach 1:
The system performs preliminary assessment of model degradation using survival analysis to predict when a model will fall below performance thresholds. This allows scheduling model updates at optimal times when operational impact is minimized, rather than performing frequent preventive updates or waiting until performance degrades significantly.
Solution Approach 2:
The system changes the parameter of update timing from fixed schedules or reactive triggers to dynamically optimized timing based on survival analysis predictions. By adjusting the replacement timing parameter based on actual model degradation trajectories, the system maintains accuracy while minimizing operational interruptions.
3Measurement precision
If multiple model metrics are monitored, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system merges multiple model performance metrics into a unified survival analysis framework. Instead of analyzing each metric separately, the system combines them into a comprehensive model degradation assessment, reducing analytical complexity while maintaining or improving detection accuracy through the integrated survival analysis approach.
Data Source
AI summary
A detection modeling system has a processing device and a memory coupled to the processing device. The detection modeling system is configured to obtain health value data associated with an analytical model, determine a time period at which the model was trained based on the obtained health value data, and identify a survival time period of the model based on the determined time period at which the model was trained and a failure time period of the model. The detection modeling system is further configured to repeat these steps to determine a survival time period for a plurality of analytical models, and perform a survival analysis based on the survival time period for the plurality of analytical models.