Overview

Risk analysis is the process of analyzing exposure data (collections of location, hazard, and financial data) using computer-based peril models to estimate financial risk. In Risk Modeler, an analysis processes exposure data through a peril model to estimate financial risk. Every analysis requires a model profile, output profile, and event rate scheme.

Once a location exposure has been geohazarded -- enriched with location data from RMS peril models, you can run a risk analysis job on that exposure using a probabilistic model. Risk Modeler supports models for analyzing risk at different levels of detail.

• The Detailed Loss Module (DLM) is a probabilistic model designed for underwriting situations. DLM peril models simulate natural catastrophe events and generate hazard analyses and estimates of potential loss.
• ALM peril models provide a faster alternative to the Detailed Loss Module (DLM) for streamlined analysis results. Rather than using detailed modeling capabilities, ALM is designed to use data with no per-risk details, such as precise locations or building characteristics. Values are entered as exposed limit per line of business.
• HD peril models leverage the benefits of distributed cloud computing to realistically represent the exposure and losses from catastrophic events. High Definition (HD) models simulate both the severity and frequency catastrophic events and consequently more realistically represent exposure and losses from these events.

Analyze account risk

The Analyze account resource enables you to "process" —analyze— an account exposure using the specified analysis engine as identified by its modelProfileId.

In this procedure, we will analyze a previously geohazarded account location using the DLM model. The DLM simulates a range of catastrophe events and returns a probability distribution of estimated financial losses.

The account ID must be specified in the path. The specified account must be geocoded and hazarded prior to analysis. All other parameters are defined in the request body.

curl --request POST \
     --url https://{host}/riskmodeler/v2/accounts/534/process \
     --header 'Accept: application/json' \
     --header 'Authorization: XXXXXXXXXX' \
     --header 'Content-Type: application/json' \

We are using the v2 version of this resource. The v2 version has been deprecated.

DLM analysis generally requires that you provide "detailed" information about the exposures analyzed. Address details (postal code or better) enable the model to precisely locate the exposure on the globe, while primary building characteristics (construction, occupancy, year built, number of stories) enable the model to more accurately estimate the vulnerability of the exposure.

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Best Practice

RMS recommends that you create and manage model profiles using tools in the Risk Modeler UI. To learn more, see the Risk Modeler User Guide.

The request body specifies details about account exposures. In the DLM analysis request, you must specify a model profile (modelProfileId), output profile (outputProfile), and event rate scheme (eventRateSchemeId).

{
     "currency": {
         "code": "USD",
         "scheme": "RMS",
         "vintage": "RL18",
         "asOfDate": "2020-03-01"
     },
     "treaties": [
          0
     ],
     "globalAnalysisSettings": {
          "franchiseDeductible": true,
          "minLossThreshold": 0,
          "treatConstructionOccupancyAsUnknown": true,
          "numMaxLossEvent": 0
     },
     "exposureType": "ACCOUNT",
     "edm": "my_edm",
     "modelProfileId": 14172,
     "eventRateSchemeId": 168,
     "jobName": "string",
     "id": 14,
     "treaties": [],
     "outputProfileId": 0
     "outputSetting": {
        "metricRequests": [
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "STATS",
                "perspective": "GU"
            },
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "EP",
                "perspective": "GU"
            },
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "LOSS_TABLES",
                "perspective": "GU"
            },
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "STATS",
                "perspective": "GR"
            },
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "EP",
                "perspective": "GR"
            },
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "LOSS_TABLES",
                "perspective": "GR"
            },
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "STATS",
                "perspective": "RL"
            },
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "EP",
                "perspective": "RL"
            },
            {
                "granularity": [
                    "Portfolio"
                ],
                "metricType": "LOSS_TABLES",
                "perspective": "RL"
            }
        ]
    }
}

The currency object and edm attribute are required.

• The id attribute Identifies the ID number of the account analyzed.
• The edm attribute specifies the name of the data source.
• The exposureType attribute specifies an enum that identifies exposure type analyzed.
• The currency field specifies a Currency object.
• The modelProfileId attribute identifies the ID number of the model profile used to analyze the specified account exposures. The model profile defines settings that define a particular type of analysis job. Model profiles are exposure-independent: the same DLM profile may be used for different exposure types. In this example, we have specified a DLM profile: 14172.
• The eventRateSchemeId attribute identifies an event rate set. For information about alternate event rates, see the model methodology documents, available on the Peril Model Documentation page on RMS Owl.
• The outputProfile attribute specifies a predefined or custom output file that defines the output to be generated by EP, scenario, historical, or footprint analyses.

If successful, returns a 202 Accepted HTTP response and initiates a ANALYTICS_JOB job on the workflow engine. You can use the Get workflow or operation resource to track the progress of the workflow job.