The village of Eyam in Derbyshire, central England, was unlucky to discover that the pandemic, then raging 150 miles (226 kilometers) to the south in London, had arrived on its doorstep.
The pandemic was the plague – the year was 1665. The disease had reached Eyam through the delivery of flea-ridden cloth from London to the local tailor, who would then made clothes for the villagers. The fleas carried the plague bacterium and the recipient of the cloth was the first to die.
Within three months another 41 villagers had perished. By spring 1666 a newly appointed rector proposed that, for the sake of other plague-free towns in the Peak District region, the village should self-isolate. A local Earl offered to guarantee food for the town (supplied on a rock at the edge of the village, paid with coins immersed in vinegar – see location below). In June 1666 the villagers reluctantly agreed. Over the summer the plague returned with a vengeance and there were five or six deaths each day. Eventually one third of the population died. But the nearby towns stayed plague free.
Since 2017, in modeling the threat from wildfire on communities in California, the significant new RMS innovation has been in capturing the process of “spotting” (i.e. identifying new outbreaks of fire far from the fire-front). Strong dry winds bring swarms of glowing embers from a raging wildland fire, which can travel long distances. Should these embers settle on shingle roofs, wooden patios or a leaf-filled plastic gutter, a fire will start. Unchecked, the fire will consume a house.
In high-density housing suburbs, wind-driven fire can spread from building to building and consume a whole neighborhood – as happened in the city of Santa Rosa in 2017. And the only way to stop an outbreak is to intervene: to extinguish each ember-ignited fire before it can spread.
Modeling ember ignitions requires sampling the speed
and direction of the wind and also anticipating what proportion of fire-starts get
extinguished before they can spread. Still it only takes one unchecked fire to
burn down the town.
This same process, in modeling “spotting”, is key to anticipating the spread of the new coronavirus into western Europe and North America.
It is an exciting time for the insurance and wider risk management industry. As of January 31, a new asset for the industry – the Risk Data Open Standard (RDOS) has been made publicly available on GitHub, the largest host of source code in the world, with over 100 million repositories and over 37 million users. It is available royalty-free to the public. The RDOS represents a step change in how risk management data is managed and is designed to overcome and improve interoperability issues and ultimately expand business opportunities.
Released as an open source and an
open standard project, this “open” approach adopted for the RDOS represents a
fundamentally new and pioneering initiative for a risk industry accustomed to
proprietary data standards. The industry recognizes the need for openness and
collaboration to tackle data interoperability problems and increase adoption
and innovation, and the “open” approach has been proven to be the best way to
unlock new collaboration among industry participants. The open source method has
worked successfully in many fields, especially when the problem domain is
engineering a standard data exchange (like HTML or JSON) or data processing and
analytics (like Hadoop and Spark).
RDOS is a “superset” of existing standards such as EDM, RDM, OED, and CEDE, that modernizes risk data into an extensible, risk model agnostic, vendor agnostic, single (unitary), and complete containment with all exposures, losses, contracts, structures and domain data in one package.
One of the major drawbacks with current data schemas is they are predominately focused on property risk – existing data models are rigid and difficult to extend to new classes of risk and associated coverage. The RDOS, in contrast, supports the capability to add new lines of business beyond property catastrophe, in a modular way that does not interfere with existing lines of business.
The extensibility of the RDOS schema also empowers open modeling and the ability to customize a personal view of risk. For instance, using the RiskItem entity (see figure below), which already includes the subtype RealProperty, this can be extended to new subtypes that represent new lines of business, like aviation, or offshore energy. Existing attributes, like RealProperty attributes, can be extended to define new attributes that may support advanced analytics or other internal system operations.
With the RDOS, you are not restricted to a particular database
technology, it is a logical data model,
meaning that it provides schema specifications and business rules to
standardize the data model independent of a specific database technology. You
are free to choose the database solution that best meets your organization’s
needs, depending on your access patterns, performance requirements, and scaling
needs. Users can select from several technology options, including Object-Relational,
Relational or a simpler Interchange format such as JSON.
By accessing the RDOS Repository on GitHub, you agree to an Apache Licence 2.0, which is a straightforward, widely used open source licence that grants access, usage and modification rights for private and commercial use.
To contribute to the RDOS, participants simply need to access RDOS via GitHub at this link: https://github.com/RMS-open-standards/RDOS and add their contribution – whether it is creating a new branch or a data subtype, implementing a new feature or fixing feature(s) as desired, and then provide comments so that others can understand your feature. The ongoing development of the RDOS is being guided by the RDOS Steering Committee, which is composed of industry experts from leading companies. The committee meets regularly to review and approve/disapprove RDOS contributions that may be added to the standard.
If you are a developer, a risk modeler, a data analyst – or if you are from outside of the risk industry and want to explore the RDOS, there is a wealth of documentation available and a microsite which explain the RDOS in more detail. If you are already familiar with working on open standards, please explore via GitHub, and come join the party – it is time to create something amazing together.
Natural disasters and other large catastrophes can trigger huge economic losses potentially among multiple insurance lines. RMS, in collaboration with research partner Cambridge Centre for Risk Studies at University of Cambridge, have developed eight template scenarios that model liability clash triggered by natural or man-made catastrophic events.
This research was primarily focused on property–liability clash modeling and was the continuation of the two-year Global Exposure Accumulation and Clash (GEAC) project. GEAC laid the foundations for a risk data schema for the various insurance lines, as well as an approach to modeling insurance clash among life and non-life insurance. Please see here for more information.
The project resulted in liability loss assessments for various scenarios including natural catastrophes, cyber and terrorism (see Figure 1 below). Although these scenarios are extreme, they are well within the realm of possibility and are aimed at stressing the various types of liability insurance.