Forward-Looking Event Generation for Liability Catastrophe Risk
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Solution Overview
Problem
Existing automated systems struggle to accurately predict and measure liability catastrophes and casualty risk accumulation due to their long-tail nature, susceptibility to soft factors, and limited historic data, failing to capture complex interactions and changes in legal, economic, and societal environments.
Innovation Solution
An event generator system that uses a multi-dimensional data structure to capture and generate risk events, incorporating liability catastrophes with LLC, ULC, and ELC scenarios, employing forward-looking modeling (FLM) to predict and quantify impacts, independent of causing units, and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data analysis and predictive modeling approaches are used, then systems can operate with available historic loss data, but they fail to accurately predict liability catastrophes due to long-tail nature and limited historic data
Solution Approach 1:
The system performs preliminary action by using forward-looking modeling to predict liability catastrophes before they occur, rather than relying on historic data analysis. The FLM technique proactively models potential catastrophe scenarios, risk factors, and loss distributions in advance, enabling the system to prepare risk assessments and mitigation strategies before actual losses materialize.
Solution Approach 2:
The system introduces an intermediary mechanism by employing FLM as a bridge between limited historic data and future catastrophe prediction. The FLM technique acts as a mediator that translates available historical information into forward-looking predictions by modeling risk factors, loss scenarios, and their interactions, thereby overcoming the data scarcity problem.
2Reliability
If forward-looking modeling (FLM) techniques are employed to reduce reliance on historic data, then prediction capability improves, but system complexity increases due to structured cause-effect chains and multiple modeling layers
Solution Approach 1:
The system applies segmentation by dividing the FLM process into distinct functional modules: risk factor identification, loss scenario modeling, parameter estimation, and prediction generation. Each module handles a specific aspect of the modeling process, making the complex system more manageable and maintainable while preserving the comprehensive forward-looking prediction capability.
Solution Approach 2:
The system implements dynamics by making the FLM approach adaptable and flexible. The modeling framework can dynamically adjust to different liability types, risk factors, and data availability conditions. The system evolves its predictions based on new information and changing conditions, maintaining reliability while managing complexity through adaptive rather than rigid structures.
3Adaptability or versatility
If multiple risk-transfers and locations are involved in catastrophe scenarios, then comprehensive risk coverage is achieved, but measurement and parametrizing of quantitative legal, societal, and economic impact factors becomes more difficult
Solution Approach 1:
The system achieves universality by creating a unified FLM framework that can handle multiple risk-transfers, locations, and impact types (legal, societal, economic) through a common modeling approach. The same core FLM techniques and risk factor structures are applied across different jurisdictions and transfer mechanisms, enabling comprehensive coverage while maintaining consistent measurement methodologies.
Solution Approach 2:
The system manages measurement difficulty by dynamically adjusting parameters based on the specific catastrophe scenario, location, and risk-transfer type. The FLM technique modifies risk factor weights, loss distribution parameters, and impact quantification methods to match the characteristics of each scenario, making the measurement process adaptable rather than uniformly difficult across all contexts.
4Adaptability or versatility
If new risk events with new characteristics keep emerging, then system adaptability to current risks improves, but reliance on historic loss data becomes even more limited
Solution Approach 1:
The system addresses emerging risks through preliminary action by using FLM to model and predict new risk events before sufficient historic data accumulates. The forward-looking approach allows the system to proactively identify and assess novel risk characteristics, enabling adaptation to emerging threats without waiting for historic patterns to establish themselves.
Data Source
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AI summary
Proposed is an event generator (10) generating risk events (2) for clash-quantifying, multi-risk assessment systems (1) with automated assessment of multi-risk exposures induced by the generated risk events (2), liability catastrophes (21) and casualty accumulations (22). A plurality of affected units (3) are subject to the risk exposure of the occurring risk events (2) caused by one or a plurality of causing liability risk exposed units (4). The event generator performs a scenario selection by means of a scenario selector (104) selecting relevant LLC/ULC/ELC scenarios (1001) from the scenarios (1001) of a data structure (101). Based on the selected scenarios (10001), concrete events (1002) are generated by means of the event generator (10).