Autonomous Cyber Risk Platform for Near-Real-Time Policy Updates
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Solution Overview
Problem
The insurance industry faces challenges in automating the assessment and quantification of rapidly evolving cyber-related risks, leading to potential coverage gaps and increased risk for insurers and insured parties due to the lag between risk evolution and policy updates.
Innovation Solution
A system utilizing machine learning and predictive analytics to continuously gather and analyze near real-time data, providing immediate risk assessments, recommendations, and updating insurance policies to mitigate cyber risks, including system availability, data breaches, and malicious attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional underwriting methods are used to assess cyber risks, then policy updates can be managed with established procedures, but the policies lag substantially behind evolving cyber risks creating coverage gaps
Solution Approach 1:
The patent implements dynamic risk assessment by continuously monitoring cyber threats, vulnerabilities, and attack patterns in real-time. The system automatically updates policy recommendations and premium pricing based on evolving risk conditions, transforming the static traditional underwriting process into a dynamic adaptive system that keeps pace with rapidly changing cyber risks
Solution Approach 2:
The system establishes continuous feedback loops where risk assessment data, claim information, and threat intelligence are constantly fed back into the underwriting engine. This enables automatic adjustment of policy terms, coverage limits, and pricing to reflect current risk levels, ensuring policies remain aligned with actual cyber threat landscapes without manual intervention delays
2Measurement precision
If vast amounts of data are analyzed to determine suitable underwriting offers, then comprehensive risk assessment is achieved, but the process becomes time-consuming
Solution Approach 1:
The patent replaces manual mechanical underwriting processes with automated computational systems that use machine learning algorithms, predictive analytics, and real-time data processing. This substitution enables the system to analyze vast datasets including threat intelligence, vulnerability scans, and historical claims data instantaneously, achieving comprehensive risk assessment without the time delays inherent in human analysis
Solution Approach 2:
The system dynamically adjusts assessment parameters and data weighting based on the specific risk context, threat type, and organizational characteristics. By automatically modifying which parameters are most relevant for each underwriting case, the system efficiently focuses computational resources on the most critical risk factors rather than uniformly processing all available data
3Reliability
If manual processes are used for claim processing, then detailed human review can be performed, but the wait time for claim processing increases
Solution Approach 1:
The patent implements self-service claim processing where the automated system independently verifies claim validity by cross-referencing incident data with policy terms, risk assessments, and historical patterns. The system automatically determines claim approval, calculates payouts, and notifies stakeholders without requiring manual human review for standard claims, dramatically reducing processing time while maintaining accuracy through algorithmic verification
Data Source
AI summary
A system for autonomous risk assessment and quantification for insurance policies for computer and information technology related risks, including but not limited to losses due to system availability, cloud computing failures, current and past data breaches, and data integrity issues. The system will use a variety of current risk information to assess the likelihood of operational interruption or loss due to both accidental issues and malicious activity. Based on these assessments, the system will be able to autonomously issue policies, adjust premium pricing, process claims, and seek re-insurance opportunities with a minimum of human input.