Distributed Catastrophe Simulation for Linked Multi-Peril Loss Forecasting
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Solution Overview
Problem
Traditional catastrophe modeling for multi-peril events is challenging due to their unpredictable nature and lack of repeated data, leading to difficulties in accurately forecasting losses and providing comprehensive understanding of linked perils.
Innovation Solution
A system and method using a distributed simulation engine that integrates multidimensional time series data servers, directed computational graph services, and automated planning services to create datasets and analyze links among perils, enabling accurate forecasting and early warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional catastrophe modeling methods are used for multi-peril events, then the modeling process is simpler, but the accuracy of loss forecasting deteriorates due to unpredictable nature and lack of repeated data
Solution Approach 1:
The system performs preliminary actions by synthesizing data from multiple peril sources and conducting simulations before actual multi-peril events occur. The distributed simulation engine pre-calculates potential loss scenarios by integrating data from various peril models (earthquake, hurricane, flood, etc.) and stores these pre-computed results for rapid retrieval and analysis when needed, thereby improving forecasting accuracy without requiring repeated real-world events.
Solution Approach 2:
The patent introduces an intermediary distributed simulation engine that mediates between raw multi-source data and final loss forecasts. This intermediary system integrates data from multiple peril models, performs graph analysis to identify links among perils, and generates synthesized datasets that bridge the gap between individual peril assessments and comprehensive multi-peril loss evaluation, thereby improving overall modeling accuracy.
2Loss of information
If comprehensive data collection from multiple perils is performed, then the understanding of linked perils improves, but the system complexity increases
Solution Approach 1:
The system segments the complex multi-peril modeling task into distinct modular components: individual peril models (earthquake, hurricane, flood, etc.), a data synthesis layer, a graph analysis module for identifying peril links, and a loss calculation engine. Each peril is modeled separately with its own data requirements and assumptions, then integrated through the distributed simulation framework. This segmentation allows comprehensive data collection while managing system complexity through modular architecture.
Solution Approach 2:
The distributed simulation engine serves multiple functions simultaneously: it acts as a data repository, a processing platform for graph analysis, a synthesis engine for creating integrated datasets, and a simulation environment for testing various scenarios. This multi-functionality reduces the need for separate specialized systems for each peril type or analysis stage, thereby comprehensively capturing linked peril relationships while controlling overall system complexity.
3Quantity of substance
If synthetic data generation is used to enrich datasets, then the data availability for analysis improves, but the computational resources required increase
Solution Approach 1:
The system applies partial action by generating synthetic data selectively rather than comprehensively for all possible scenarios. The distributed simulation engine identifies specific data gaps in the multi-peril dataset and generates synthetic data only for those particular peril combinations or geographic regions where real data is insufficient. This approach enriches data availability where needed while avoiding the excessive computational cost of generating all possible synthetic scenarios.
Data Source
AI summary
Modeling multi-peril catastrophe using a multidimensional timeseries data server that creates a first dataset by retrieving previously gathered and analyzed data based on a plurality of perils, and create a second dataset by retrieving from memory synthetically generated data based at least on the plurality of perils; and a directed computational graph service configured to retrieve the first dataset and second dataset from the multidimensional time series data server, and perform graph analysis on the first dataset and second dataset to find links amongst the plurality of perils.