Predictive Risk Modeling With Multi-Stage Data Filtering
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Solution Overview
Problem
Traditional risk management systems are reactive and rely on static, lagging indicators, underutilizing vast amounts of available data due to complexity, weakly understood inter-relationships, and manual review, making it difficult to proactively identify and mitigate potential risks.
Innovation Solution
A system utilizing machine learning models to aggregate, normalize, and filter data, generating predictive risk event predictions by identifying connections and trends, enabling proactive risk management through real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional static and lagging indicators are used for risk management, then the system is simpler to operate, but the risk identification is delayed and reactive
Solution Approach 1:
The system performs preliminary actions by continuously analyzing current data and identifying early warning signs of potential risks before they materialize. Machine learning models predict future risk events by detecting patterns in real-time data, enabling proactive risk management rather than reactive response to lagging indicators.
Solution Approach 2:
The patent replaces manual, mechanical risk assessment processes with automated machine learning systems. The ML models automatically process large volumes of data, identify correlations, and generate risk predictions without manual intervention, eliminating the time loss associated with manual review while managing complexity through automation.
2Productivity
If large amounts of risk data are collected and analyzed manually, then comprehensive risk assessment is achieved, but the process is time-consuming and inefficient
Solution Approach 1:
The system replaces manual data analysis with automated machine learning models that process large volumes of risk data in real-time. The ML algorithms automatically identify patterns, correlations, and risk signals without manual intervention, dramatically increasing productivity while eliminating the time loss associated with manual review processes.
Solution Approach 2:
The machine learning models perform self-service by autonomously analyzing data, generating risk predictions, and updating assessments without human intervention. The system continuously learns from new data and automatically adjusts its analysis, enabling comprehensive risk assessment at machine speed rather than human speed.
3Measurement precision
If multiple machine learning models are used to process data, then predictive accuracy is improved, but the system complexity increases
Solution Approach 1:
The system segments the risk analysis process into multiple specialized machine learning models, each handling specific aspects of risk prediction. This segmentation allows each model to focus on particular data patterns or risk types, improving overall prediction accuracy while organizing complexity into manageable, modular components that can be independently trained and maintained.
Data Source
AI summary
Disclosed embodiments may include a method for generating predictive risk outcomes by receiving data and generating, using a first machine learning model (MLM), associated data. Then generating from the associated data, using a second MLM, correlated and uncorrelated data, which is then filtered to a reduced data set. The reduced data set is then used to generate, using a third MLM, risk event predictions that are output to an interactive graphical user interface (GUI) in a ranked, dynamic index. The system can be adjusted and run in near-real time from the GUI.