# Introduction to Metrics

# Overview

An analysis result is an estimate of the financial risk posed by an exposure. During risk modeling, Intelligent Risk Platform™ leverages exposure data and peril models to calculate that risk of financial loss posed by a particular exposure. The analysis result is a projection of the losses to an exposure for all financials and perils based on a peril model.

Depending on the peril model used to generate the risk analysis, Intelligent Risk Platform may generate many different event loss data. Risk Modeler supports ALM, DLM, and HD peril models. To learn more about RMS peril models, see Peril Models.

Intelligent Risk Platform stores the results of risk analysis to the Results Data Module (RDM), a cloud-based data structure for storing projected loss event data. RDM stores loss estimates and other output including event losses across financial perspectives and perils. Risk Modeler Metrics APIs services enables you to retrieve analysis results from the RDM.

# Analyses

An analysis result is a collection of projected losses Intelligent Risk Platform has generated for a modeled exposure. The analysis result is identified by its `id`

, `name`

, `runDate`

, `exposureType`

, `exposureId`

, and other attributes. The Metrics API provides services that enable you filter and sort analysis results based on analysis attributes.

# EP metrics

The Exceedance Probability (EP) or stochastic analysis option runs a full probabilistic analysis on the exposure at risk, producing curves that are cumulative distributions showing the probability that losses will exceed a certain amount, from either single or multiple occurrences. These losses are expressed in the occurrence exceedance probability (OEP) and the aggregate exceedance probability (AEP) curves. AEP and OEP curves are two different curves that have two distinct uses and offer different information. Both curves show the probability that losses will exceed a given threshold.

# Event loss tables (ELT)

The event loss table (ELT) is an output table that contains information about the loss-causing events in an analysis, including the the mean loss standard deviation (split into an independent and a correlated piece), exposure value, and event rate. ELT is the basis of losses for all financial perspectives at all exposure levels and is used in computing output statistics.

# Period loss tables (PLT)

Loss results generated by an HD model are stored in a period loss table (PLT). Every row in the PLT

represents an event that causes damage, and therefore loss, to the exposure being analyzed. The event is

stamped with the date that the event occurred and with a loss date that the policy payout occurred.

By simulating event losses over the course of a time period, PLTs provide more flexibility to evaluate loss metrics than the analytical calculations based on event loss tables (ELTs). By simulating events through time, a model computes total loss as well as maximum event occurrence loss for each simulation period in the table, and generates loss statistics based on the distribution of losses across the large number of simulated periods. This methodology can calculate the impact of all contract terms, including terms with time-based features, such as contracts that are shorter (or longer) than a single year.

# Simulate losses

The Simulate losses service generates a new PLT-based HD analysis from an existing ELT-based DLM analysis using the data in specified simulation set to derive the period loss table. The simulated PLT analysis returns the event ID of every event in each simulated period, and the calculated loss value of each of these events.

# Location AAL metrics

The Location AAL service retrieves average annual loss (AAL), coefficient of variation (CV), and standard deviation statistics for modelled location exposures. Using these statistics, underwriters can identify the location exposures in their portfolios that are at the greatest risk.

Updated 3 months ago