Audience
Catastrophe Model platform for everyone wanting to avoid potential magnitude and probability of economic loss
About Global Models
RMS has over 200 peril models in nearly 100 countries enabling insurers, reinsurers and other organizations to quantify the potential magnitude and probability of economic loss from catastrophe events. Manage risk in growing and evolving agricultural markets in Asia-Pacific across all key coverages and schemes with the RMS India and China Agricultural Models. Capture the changing value and vulnerability of the builders risk class of property insurance across different phases of construction projects. Improve your near and long-term climate change risk outlook with new catastrophe models. Assess tropical cyclone risk to inform sound underwriting, portfolio management, and risk transfer decisions. Access broad-scale, well-validated views of flood risk to gain necessary insights into the range of commercial opportunities associated with various flood markets.
Integrations
No integrations listed.
Company Information
Risk Management Solutions
Founded: 1988
United States
www.rms.com/models
Videos and Screen Captures
Active Risk Manager (ARM)
Active Risk Manager, world-leading Project Risk and Enterprise Risk Management software, powering risk management for medium and large organizations in core industries of Aerospace & Defense, Mining, Energy, Rail, Construction, and Government. Highly configurable and flexible to provide a single view of risk.
Popular features include:
- BowTie;
- Quantitative Risk Assessments & Analysis;
- Risk Reports & Dashboards,
- Risk Normalization & Aggregation,
- Incident Management.
Popular Benefits Include:
- Security Model
- Highly Configurable and Scalable
- Accelerated Time to Value
- Ease of Implementation
- Comprehensive Security Control
- Full Data History
- 24/7 Global Support
- On- Premise or Cloud Based Solution
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The XDI Climate Risk Hub
The XDI Climate Risk Hub provides world-leading physical climate risk analysis on-demand. Features: Instantly view asset and portfolio risk ratings, 5 risk rating bands based on 10 hazards, Key metrics include Maximum Value at Risk and Productivity Loss. RCPs and NGFS scenarios, Annual analysis from 1990-2100, Spatial resolution of up to 5mx5m. Highly customisable.
XDI uses a wide range of engineering, hazard, context, weather, climate and financial data to provide detailed physical risk insights. Asset engineering features vulnerable to damage or failure from hazards are based on 140 standard residential, commercial and industrial archetypes which can be amended to test adaptation responses. Hazard probabilities are calculated using historical weather data (1990 baseline) and local environmental context such as topography, flood mapping, soil composition and others, and the change in hazard severity and frequency as a result of climate model simulations (CMIP5 and CMIP6).
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Jupiter
Jupiter is the market leader for climate risk analytics in key industries, including insurance, banking, asset management, real estate, energy, utilities and power, oil and gas, manufacturing, chemicals, mining, retail, agriculture, the public sector, and NGOs. Our best-in-science solutions deliver climate risk analysis of multiple perils, in spatial resolutions from portfolio to asset level, globally and across flexible time horizons and climate scenarios. Jupiter’s physical climate risk analytics have been adopted by at least one of the world’s five largest firms in asset management, banking, insurance, oil and gas, mining, power, and construction, as well as the government of the United States.
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Terrafuse AI
Extreme wildfire conditions have increased by 50% in recent decades due to climate change, causing $31B in property loss. As a result, insurance companies have refused to insure homes and businesses in California, drastically reducing their revenue. Terrafuse’s wildfire AI risk scores profile the actual likelihood of any property to burn one year ahead. Our wildfire risk product allows insurance companies to not only underwrite more businesses and reduce non-renewals to only properties with the highest burn probabilities but also price business commensurate with actual risk. We train our models to learn the interdependencies between 50+ environmental conditions (e.g. building density, dead fuel moisture, vegetation) and severity of loss over time. We also embed the physics of the atmosphere into our models using advanced machine learning techniques.
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Product Details
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SaaS
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