Litigation Risk Identification via Statistical Agent-Harm Analysis
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Solution Overview
Problem
Insurance companies face challenges in identifying and prioritizing potential mass-litigation risks associated with litagion agents, as existing methods only allow tracking of emerged risks with significant exposures, lacking an early warning system for both known and obscure risks.
Innovation Solution
A computer-implemented method that accesses an electronic document database, inputs user-specified criteria for agents and harms, generates agent-harm coincidences, assesses statistical significance, and compiles risk data to identify and prioritize potential litagion agents, utilizing academic research trends as an early warning mechanism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing risk tracking methods are used, then emerged risks with significant exposures can be monitored, but early warning capability for potential risks is lost
Solution Approach 1:
The system performs preliminary actions by proactively scanning academic literature, news articles, and other information sources to identify potential litigion agents before they cause mass litigation. The methodology establishes early warning signals by monitoring emerging research trends and public concerns, allowing insurance companies to identify risks before claims are made, thus resolving the contradiction between reliable risk identification and early warning capability
Solution Approach 2:
The system introduces an intermediary information processing layer that bridges academic research and insurance risk assessment. By using natural language processing and text mining techniques on intermediate sources like academic papers and news articles, the system translates emerging scientific findings into actionable risk intelligence, enabling early detection of potential litigion agents without waiting for actual litigation to emerge
2Reliability
If comprehensive document database searching is performed, then more potential risks can be identified, but processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential and relevant information from vast document databases using targeted search queries and natural language processing. Instead of analyzing entire documents, the methodology extracts key phrases, sentences, and concepts related to potential litigion agents, harms, and causal relationships. This extraction approach maintains comprehensive risk detection while significantly reducing processing time and computational resource requirements
Solution Approach 2:
The system applies partial action by focusing computational resources on the most critical analysis tasks. It performs exhaustive text mining and statistical analysis only on documents that contain relevant keywords and patterns indicating potential risks. By selectively applying comprehensive analysis only where needed rather than uniformly across all documents, the system achieves high reliability in risk detection while maintaining processing efficiency
Data Source
AI summary
A computer-implemented method, by a computer having a computer processor, of identifying emerging risks of agents causing harms to a particular system comprises accessing, via a computer network, an electronic document database comprising document data; inputting a set of criteria, which includes a selected set of agents and a selected set of harms to a particular system, specified by a user; extracting a subset of the document data that satisfies the set of criteria; generating, with the processor, an array containing agent-harm coincidences from the extracted subset of the document data; assessing, with the processor, statistical significance of each agent-harm coincidence relative to other agent-harm coincidences; compiling, with the processor, risk data, based on the statistical significance, of agents of the selected set of agents causing harms of the selected set of harms to the particular system; and outputting the risk data.