Multi-Model Risk Prediction Through Near-Real-Time 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 assess and manage financial risk effectively.
Innovation Solution
A system utilizing machine learning models to aggregate, normalize, and filter data, generating predictive risk outcomes by identifying linkages and correlations, enabling proactive risk management through near-real-time risk event predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional static risk indicators are used, then risk management is simpler and more manageable, but the risk assessment is reactive and lagging, failing to predict future risks proactively
Solution Approach 1:
The system performs preliminary actions by continuously analyzing current data and historical patterns to generate risk predictions before actual risk events occur. The machine learning models process data in near-real-time to forecast potential risks, enabling proactive risk management rather than reactive response to static indicators.
Solution Approach 2:
Machine learning models serve as intermediaries between raw data and risk predictions. These models aggregate and normalize data from multiple sources, identify correlations and patterns, and translate complex data relationships into actionable risk predictions, bridging the gap between data complexity and actionable insights.
2Loss of information
If large amounts of risk data are collected and analyzed, then more comprehensive risk assessments are achieved, but the manual review process becomes too complex and time-consuming to process effectively
Solution Approach 1:
The system replaces manual review processes with automated machine learning models that can process large volumes of risk data in near-real-time. The ML models automatically aggregate, normalize, and analyze data from multiple sources, identifying correlations and generating risk predictions without human intervention, thus eliminating the time and complexity constraints of manual analysis.
Solution Approach 2:
The system changes parameters by transforming raw risk data into normalized and aggregated formats that machine learning models can process efficiently. Data is converted from diverse formats and timeframes into standardized parameters that enable rapid analysis and prediction generation, maintaining comprehensive data utilization while reducing processing time.
3Speed
If static risk indicators are used, then the risk management process is simpler to implement, but it cannot provide near-real-time risk predictions or identify emerging risks
Solution Approach 1:
The system performs preliminary data processing by continuously aggregating and normalizing risk data in near-real-time before actual risk events occur. Machine learning models are pre-trained on historical data to quickly identify patterns and generate predictions, enabling rapid risk assessment without complex manual analysis when risks materialize.
Solution Approach 2:
Machine learning models act as intermediaries that simplify complex data processing by automatically identifying correlations and patterns in near-real-time data streams. These models translate complex multi-source data into actionable risk predictions, maintaining high processing speed while managing data complexity through automated pattern recognition.
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.