Catastrophic Risk Model Blending via Pre-Simulated Data Extraction
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Solution Overview
Problem
Current methods for simulating catastrophic event loss require significant time and resources for data preparation and analysis, leading to storage challenges due to pre-aggregated and pre-simulated data, which limits real-time risk assessment capabilities.
Innovation Solution
The proposed solution involves a system and method that reduce storage requirements by pre-simulating catastrophic model event loss data into loss data sets for rapid risk calculations, allowing for customizable simulations and blending data from multiple models to generate custom model versions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-aggregated and pre-simulated data is stored to eliminate heavy lifting in data preparation, then real-time risk assessment capability is improved, but storage requirements increase to several terabytes
Solution Approach 1:
The patent extracts only the essential pre-simulated data elements needed for rapid risk calculations rather than storing complete pre-aggregated datasets. By selectively extracting and retaining only critical loss data records, the system achieves real-time assessment capability while dramatically reducing storage requirements from several terabytes to a manageable size.
Solution Approach 2:
The patent segments the catastrophic model data into distinct loss data records that can be independently stored and rapidly accessed. Each record contains specific pre-simulated information for particular catastrophe scenarios, allowing the system to retrieve only relevant segments for specific risk assessments rather than processing or storing entire datasets.
2Loss of time
If full pre-aggregation of catastrophic model data is performed, then storage of pre-simulated information is available for rapid calculations, but storage requirements increase by about 80%
Solution Approach 1:
The patent applies partial pre-aggregation by pre-simulating and storing only the specific loss data records needed for common risk calculations, rather than performing complete pre-aggregation of all possible catastrophe scenarios. This partial action approach reduces storage requirements by approximately 80% while maintaining rapid calculation capability for the majority of practical applications.
3Measurement precision
If statistical analysis is performed on large-scale catastrophic loss data across wide swath of loss segments, then comprehensive risk analysis is improved, but processing time and resources increase significantly
Solution Approach 1:
The patent performs preliminary statistical analysis and pre-simulation of catastrophic loss data during an initial processing phase, storing the results as pre-simulated loss data records. When comprehensive risk analysis is subsequently requested, the system retrieves and combines these pre-analyzed records rather than performing new statistical analysis on raw data, dramatically reducing processing time while maintaining analysis comprehensiveness.
Data Source
AI summary
In an illustrative embodiment, systems and methods for producing a customized catastrophic risk model involve receiving a blend definition identifying two or more catastrophic risk models and at least one peril per model, calculating trial count(s) using available trials for each model, and sampling loss records from each model according to the trial count(s). The models may contain pre-aggregated and/or pre-simulated data. The models may have been created by pre-simulating each constituent event loss data set of an original catastrophic model into a year-loss data set, aggregating the year loss data to produce a set of sample year losses at level(s) relevant to a set of risk calculations, using the sample year losses to calculate gross loss characteristics, and comparing the gross loss characteristics to corresponding anticipated gross loss characteristics to confirm closeness in results.