Sensor Data Drift Detection for Insurance Underwriting
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Solution Overview
Problem
Existing security and access control systems in commercial and residential premises lack a comprehensive method to analyze sensor data for predictive insights, leading to limited utilization of valuable operational and service records, which could enhance risk assessment and insurance underwriting processes.
Innovation Solution
A computer program product that collects and analyzes sensor information using unsupervised learning models to detect drift state sequences, predicting potential events and adjusting insurance rates based on real-time evaluations against unique underwriting guidelines, thereby integrating sensor data into continuous insurance underwriting and risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data is collected and stored in product memory or electronic logs, then data availability is improved, but data utilization remains limited
Solution Approach 1:
The patent introduces an intermediary data mining system that acts as a mediator between the sensor data storage and the insurance underwriting process. This intermediary system processes and analyzes the stored sensor data to extract meaningful insights, thereby improving data utilization without requiring the original sensor systems to become more complex.
Solution Approach 2:
The system enables self-service by automatically analyzing sensor data and generating risk assessments without requiring manual intervention. The data mining process autonomously transforms raw sensor information into actionable insurance underwriting insights, eliminating the need for manual data processing while maximizing data utilization.
2Productivity
If traditional underwriting methods using paper or online forms are used, then process simplicity is maintained, but continuous verification capability is lost
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing sensor data before insurance events occur. This proactive approach enables continuous verification of insured premises conditions, allowing the system to assess risks in real-time rather than relying on periodic manual assessments, thus enabling continuous underwriting capability.
Solution Approach 2:
The patent replaces the mechanical process of manual underwriting review with an automated data mining and analysis system. This substitution transforms the underwriting process from a manual, periodic activity to an automated, continuous process that leverages sensor data and machine learning algorithms to continuously verify and assess risk.
3Reliability
If sensor data is analyzed using traditional methods, then analysis speed is maintained, but predictive insight capability is limited
Solution Approach 1:
The system implements continuous analysis of sensor data streams using data mining techniques. Rather than performing periodic or batch analysis, the system continuously processes incoming sensor information to detect patterns, trends, and potential risks in real-time, thereby maintaining both high reliability in risk assessment and timely detection of emerging issues.
Solution Approach 2:
The system applies partial analysis by focusing data mining efforts on specific critical parameters and patterns most relevant to insurance risk assessment. Rather than analyzing all sensor data equally, the system selectively concentrates computational resources on the most informative aspects of the data, achieving high reliability without excessive analysis time.
Data Source
AI summary
Techniques for detecting physical conditions at a physical premises from collection of sensor information from plural sensors execute one or more unsupervised learning models to continually analyze the collected sensor information to produce operational states of sensor information, produce sequences of state transitions, detect during the continual analysis of sensor data that one or more of the sequences of state transitions is a drift sequence, correlate determined drift state sequence to a stored determined condition at the premises, and generate an alert based on the determined condition. Various uses are described for these techniques.