Closed-loop Risk Analytics Platform for Real-time Sensor Data
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Solution Overview
Problem
Existing methods for predictive risk analytics in enterprises are time-consuming and error-prone, especially when dealing with complex logic and numerous factors.
Innovation Solution
A closed-loop system incorporating a risk analytics platform that receives sensor data in real-time, analyzes it using risk analytics algorithms to detect abnormal patterns, and automatically transmits results to a risk operations platform for active risk mitigation adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual rules and logic are used for predictive risk analytics, then flexibility in defining prediction criteria is improved, but time consumption and error rate increase
Solution Approach 1:
The system enables self-service through automated risk analytics algorithms that independently process sensor data and generate predictions without requiring manual rule definition. The closed-loop system automatically receives data, analyzes it using predefined algorithms, and transmits results, allowing the enterprise to benefit from continuous automated analysis while maintaining the ability to update algorithms as needed.
Solution Approach 2:
The patent replaces the mechanical process of manual rule definition and analysis with an automated computational system. The risk analytics algorithms automatically process sensor data using computational methods, substituting human manual analysis with machine-based automated analysis that is both faster and more consistent.
2Ease of operation
If manual rules and logic are used for predictive risk analytics, then ease of understanding the analysis process is improved, but error rate increases
Solution Approach 1:
The closed-loop system incorporates feedback mechanisms where analysis results are transmitted back to the enterprise, allowing for validation and refinement of the risk analytics algorithms. This feedback loop enables continuous improvement of the system's reliability while maintaining transparency in the analysis process through documented algorithmic procedures.
Solution Approach 2:
The system uses standardized risk analytics algorithms that can be replicated and applied consistently across different datasets and time periods. This copying of proven analytical methods ensures consistency and reduces errors that might arise from manual analysis variations, while the algorithms themselves remain transparent and explainable.
3Productivity
If automated risk analytics algorithms are implemented, then productivity and speed of analysis are improved, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: sensor data collection, risk analytics algorithm processing, and result transmission. This segmentation allows each component to be optimized independently for speed while managing overall system complexity through clear separation of concerns and standardized interfaces between modules.
Solution Approach 2:
The risk analytics algorithms are designed as universal, multi-functional components that can process various types of sensor data and apply multiple risk assessment methodologies through a single integrated system. This universality reduces the need for multiple separate systems, thereby managing complexity while maintaining high productivity.
4Speed
If real-time sensor data analysis is performed, then responsiveness to risk events is improved, but computational resource requirements increase
Solution Approach 1:
The system performs partial analysis by focusing computational resources on detecting abnormal patterns and high-risk conditions rather than analyzing all data points in equal detail. This approach maintains real-time responsiveness for critical risk events while reducing overall computational resource consumption by applying different levels of analysis intensity based on data characteristics.
Data Source
AI summary
According to some embodiments, a risk monitoring data store may contain a set of electronic data records, with each electronic data record being associated with a stream of sensor data received via a communication network from a remote set of sensor systems located at a risk monitoring site. A risk analytics platform computer may receive information associated with the sensor data in substantially real-time and analyze the received sensor data, using at least one risk analytics algorithm, to detect an abnormal pattern associated with a predicted elevated level of risk at the risk monitoring site. The risk analytics platform computer may also automatically transmit a result of the analysis to a risk operations platform. The risk operations platform may then implement an active risk mitigation adjustment at the risk monitoring site responsive to the result of the analysis.