Risk splitter and risk quantifying forecast system using a structured forward-looking simulation technique quantifying clashing, long-tail risk events causing casualty loss accumulation and high earning volatility, and method thereof

An automated forecasting system for measuring future occurrence probabilities of risk events using a structured forward-looking model structure quantifying clashing, long-tail risk events, comprising:

• a passage-based risk-parsing device for providing automated risk splitting for a multi-risk forecast system capturing multiple loss aggregation under complex environmental conditions based on occurrence of defined loss events which are triggered by one or more involved causing units and affected units,
• wherein a plurality of affected units are subject to a risk exposure of occurring risk events caused by one or a plurality of causing units,
• wherein the loss events evolve from one or multiple risk accumulating sources,
• wherein risk events related loss measures are scattered over multiple causing units or multiple related clash events, and
• wherein the multi-risk forecast system provides forecasting and exposure-based signaling, steering and/or operating of associated, catastrophic risk-event driven or triggered systems,
• wherein the automated forecasting system comprises measuring stations or sensors in loco and/or by satellite-based for measuring occurring risk-events,
• wherein measured sensory data of the measuring stations or sensors
• are transmitted via a data transmission network to the automated forecasting system, comprising a central control unit controller for processing of the captured electronic data, and
• are assigned to a historic set comprising event parameters for each risk-event, wherein for capturing and measuring the measured sensory data, the central control unit controller comprises a risk-event driven core aggregator with measuring data-driven triggers for triggering, capturing, and monitoring in a data flow pathway of the measuring sensors and/or measuring devices of risk-exposed and affected units, and wherein the forward-looking model structure is based on measured risk events and risk events generated by the automated forecasting system providing a defined structured cause-effect chain,
• wherein the passage-based risk-parsing device being based on a structured forward-looking cause-effect chain with parsed parameter data space, comprises:
• circuitry including a multi-dimensional data structure holding predefined accumulation scenarios for capturing measures of at least three risk accumulation sources triggered by the causing units, wherein the multi-dimensional data structure comprises:
• a first accumulation scenario data structure for capturing first frame parameters of first scenario loss events, the first frame parameters defining risk events with a limited geographic impact range, with a duration time window, with a plurality of affected units affected by an impact of occurring risk events, and with a plurality of causing risk exposed units affected by a loss burden associated with an impact of the occurring risk events,
• a second accumulation scenario data structure for capturing second frame parameters of second scenario loss events, the second frame parameters defining risk events with a global geographic impact range, with a longer duration time than the duration time of unfolding of the events, and with an extended number of causing risk exposed units,
• a third accumulation scenario data structure for capturing third frame parameters defining third scenario risk events impacting first and second scenario risk events with external influences, the third frame parameters capturing external influence measures impacting at least frequency or severity of the impact of the occurring risk events,
• wherein a structured cause-effect chain is provided by a generated plurality of data sets holding risk events evolving from a specific accumulation scenario for each of the predefined accumulation scenarios,
• wherein an activity is realized as a unit that is executed and is part of a scenario in order that the scenario actually happens within the structured cause-effect chain, and
• wherein each causing activity has one or more types of industry assigned executing a given activity together with a probability measure that a causing activity is executed from a specific industry, and
• the circuitry is configured to split the risk based on risk exposed, causing unit characteristics and risk exposed affected units characteristics, the causing unit characteristics comprising for each causing unit assigned activity characteristic parameters and participation characteristic parameters of said causing unit defining for a specific causing unit a specific set of executable activity link members and market share driving participation probabilities, and the causing unit characteristics comprising quota parameters defining affected populations of affected units with incidence and defining refined effect types.