Probability and statistics for AI and financial model risk

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Probability and statistics for AI and financial model risk: Catastrophe model pipeline

Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, average annual loss, exceedance curves, PML and TVaR.

1. 01 Hazard

   1. 01Event catalogueDataset · Model developerThe hazard module holds a large catalogue of simulated events that represents a wide range of plausible scenarios.
   2. 03Hazard moduleModel · Model developerFor each event the hazard module gives the local hazard intensity that the vulnerability module reads.

2. 02 Exposure and damage

   1. 02ڕووبەڕووبوونDataset · MRM committeeThe exposure module houses the portfolio data, such as location-specific information.
   2. 04Vulnerability moduleModel · Model developerThe vulnerability module quantifies the expected damage to the exposures from an event.

3. 03 Financial

   1. 05Financial moduleModel · MRM committeeThe financial module calculates losses from all event scenarios. Ground-up, gross and net losses differ as shown below.
   2. 06Event loss tableDataset · MRM committeeAn event loss table lists each event with its annual occurrence probability and its loss for the portfolio.
   3. 07Year loss tableDataset · MRM committeeSome models also sample simulated years from event losses. This outline does not describe their format.

4. 04 Metrics

   1. 08Exceedance probability curvesModel · MRM committeeThe occurrence curve gives the chance that the largest loss in a year exceeds an amount. The aggregate curve does so for the sum of losses.
   2. 09AAL, PML and TVaRModel · MRM committeeAverage annual loss sums event probability times loss. PML is one point on the occurrence curve.

5. 05 Use of results

   1. 10Use of resultsDecision · MRM committeeThe tail metrics are used when considering a portfolio’s exposure and the level of catastrophe reinsurance an insurer requires.

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