Tag Archives: Risk Data Open Standard

How to Contribute to the Risk Data Open Standard (RDOS)

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.

How to Get Started with RDOS

It is simple to access the RDOS, register to create an account with GitHub if you do not already have one, and use this link to navigate to the RDOS: https://github.com/RMS-open-standards/RDOS

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, complete and sign the required Contributor License Agreement, 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 RDOS Contributor License Agreement is based on the Apache Software Foundation Contributor License Agreement, and it is necessary for providing reliable and long-lived open source projects through collaborative open source software development. In all cases, contributors retain full rights to use their original contributions for any other purpose outside of RDOS.

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.

Industry Collaboration for a Better Data Standard

Data – the buzzword of the decade. The world understands its value, but the insurance industry has not only lagged behind in exploiting data, it has also created huge inefficiencies in how it is handled and exchanged. This situation needs urgent attention, but no single company is going to solve this problem – it will take collaboration. 

The data that drives risk analytics has proven particularly tricky to handle and leverage. Right now, the standards the industry uses are decades-old property cat schemas – venerable workhorses that took the industry from an almost total lack of exposure data to a relatively high degree of understanding. They transformed how property cat risk is transacted, priced, and managed. But these old formats have run their course and if we want to gain meaningful efficiency, improve profitability, and pursue new opportunities beyond property cat. The industry needs an improved standard. 

Fortunately, an intriguing possibility exists. Over the past several years, RMS has researched and built a comprehensive and flexible new data container called a Risk Data Object. This data specification can handle any type of model, any line of business, and any financial terms and conditions.

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Creating an Open Standard for Risk Data With Risk Data Open Standard (RDOS)

The Risk Data Open Standard (RDOS) Steering Committee that guides and oversees this standard was busy in 2019 working to shape the RDOS, to enable the risk industry to simplify risk management and risk data portability. The RDOS has traveled a long journey and is now proudly an open standard.

Much has been published already about what the RDOS is. So, for this post I won’t focus on the mechanics of the RDOS, instead I’ll focus on the “why and how” the RDOS is open, and how it enables anyone in the industry to use and contribute to the standard.

Just in case you are not familiar with this new standard, I will quickly introduce the RDOS, but the majority of the post will be on diving into the “open” part of the RDOS, and to unpack how we are reusing learnings from existing “open source and open standards” that have successfully created international and industry wide collaborations.

So, let’s get started.

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Data Engineering for Risk Analytics with Risk Data Open Standard

This article was originally published by DZone

What Is Risk Analytics?

The picture below on the left shows the extensive flooding at industrial parks north of Bangkok, Thailand. Western Digital had 60 percent of its total hard drive production coming from the country – floods disrupted production facilities at multiple sites to dramatically affect a major, global supply chain. And the picture on the right – showing flooding on the New York Subway from Hurricane Sandy, caused widespread disruption and nearly US$70 billion of losses across the northeastern U.S.

In both examples, the analysis of risk should not only help with physical protection measures such as stronger buildings through improved building codes or better defenses, but also the protection available through financial recovery. Providing financial protection is the job of the financial services and insurance industries. Improving our understanding of and practices in risk analytics as a field is one of the most interesting problems in big data these days, given the increasing set of risks we have to watch for.

Flooding at industrial parks north of Bangkok, Thailand in 2011 (left) and flooded subway stations in New York after Hurricane Sandy in 2012 (right) Image credit: Wikimedia/Flickr

How Does Risk Analytics Work?

Obviously, the risk landscape is vast. It stretches from “natural” events – such as severe hurricanes and typhoons, to earthquakes to “human-generated” disasters, such as cyberattacks, terrorism and so on.

The initial steps of risk analytics start with understanding the exposure – this is the risks a given asset, individual etc. are exposed to. Understanding exposure means detailing events that lead to damage and the related losses that could result from those events. Formulas get more complicated from here. There is a busy highway of data surrounding this field. Data engineers, data scientists, and others involved in risk analytics work to predict, model, select, and price risk to calculate how to provide effective protection.

Data Engineering for Risk Analytics

Let’s look at property-focused risks. In this instance, risk analytics starts with an understanding of how a property – such as a commercial or a residential building, is exposed to risk. The kind of events that could pose a risk and the associated losses that could result from those events depends on many variables.

The problem is that in today’s enterprise, if you want to work with exposure data, you have to work with multiple siloed systems that have their own data formats and representations. These systems do not speak the same language. For a user to get a complete picture, they need to go across these systems and constantly translate and transform data between them. As a data engineer, how do you provide a unified view of data across all systems? For instance, how can you enable a risk analyst to understand all kinds of perils – from a hurricane, a hailstorm to storm surge, and then roll this all up so you can guarantee the coverage on these losses?

There are also a number of standards used by the insurance industry to integrate, transfer, and exchange this type of information. The most popular of these formats is the Exposure Data Model (EDM). However, EDMs and some of their less popular counterparts (Catastrophe Exposure Database Exchange – CEDE, and Open Exposure Data – OED) have not aged well and have not kept up with the industry needs:

  • These older standards are property centric; risk analytics requires an accommodation and understanding of new risks, such as cyberattacks, liability risks, and supply chain risk.
  • These older standards are propriety-designed for single systems that do not take into account the needs of various systems, for example, they can’t support new predictive risk models.
  • These standards don’t come with the right containment to represent high fidelity data portability – the exposure data formats do not usually represent losses, reference data, and settings used to produce the loss information that can allow for data integrity.
  • These standards are not extensible. Versioning and dependencies on specific product formats (such as database formats specific to version X of SQL Server etc) constantly make data portability harder.

This creates a huge data engineering challenge. If you can’t exchange information with high fidelity, forget getting reliable insights. As anyone dealing with data will say: garbage in, garbage out!

For any data engineer dealing with risk analytics, there is great news. There is a new open standard that is designed to remove shortcomings of the EDM and other similar formats. This new standard has been in the works for several years. It is the Risk Data Open Standard. The Risk Data Open Standard (RDOS) is designed to simplify data engineering. It is designed to simplify integrating data between systems that deal with exposure and loss data. It isn’t just RMS working to invent and validate this standard in isolation. A steering committee of thought leaders from influential companies is working on validating the Risk Data OS.

The Risk Data OS will allow us to work on risk analytics much more effectively. This is the way we can better understand the type of protection we need to create to help mitigate against climate change and other natural or human-made disasters. You can find details on the Risk Data OS here. If you are interested in the Risk Data OS, have feedback, or would like to help us define this standard, you can email the Risk Data OS steering committee by clicking here .

Reactions North America Awards: We’re in Good Company

On Thursday, September 26, a contingent from RMS attended the Reactions’ 2019 North America (Re)Insurance Conference and Awards in New York. It was such an enjoyable event and it was a great honor to receive the North America Risk Modeler of the Year award. To win a Reactions award, you not only needed to impress the panel of independent judges, you also need market approval. Thank you to the judges and to everyone who took the time to support us.

Karen White, chief executive officer for RMS, receives the Reactions North America Risk Modeler of the Year award

Alongside us were many of our clients and partners who were either award nominees or winners on that night, including North America Insurer, Specialty Reinsurer, ILS Investment Manager, and Consultancy of the Year. Congratulations to our clients gaining recognition for great work. We are proud to be in such good company!

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EXPOSURE Magazine: Looking Ahead to the Next Ten Years

The year 2020 is just months away, and in the latest edition of EXPOSURE — the RMS magazine for risk management professionals, we consider some of the changes that the (re)insurance industry will have undergone in ten years’ time. Mohsen Rahnama, Cihan Biyikoglu and Moe Khosravy from RMS tackle the issues, examining the evolution of risk management, the drivers of technological change, and how all roads lead to a common, collaborative industry platform.

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Industry Collaboration for a Better Data Standard

Data – the buzzword of the decade. The world understands its value, but the insurance industry has not only lagged behind in exploiting data, it has also created huge inefficiencies in how it is handled and exchanged. At RMS, we believe that no single company is going to solve this problem – it will take collaboration.

The data that drives risk analytics has proven particularly tricky to handle and leverage. Right now, the only standards the industry uses are decades-old property cat schemas – venerable work horses that took the industry from an almost total lack of exposure data to a relatively high degree of understanding. They transformed how property cat risk is transacted, priced, and managed. But these old formats have run their course and if we want to gain meaningful efficiency, improve profitability, and pursue new opportunities beyond property cat, we need a new standard.

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Exceedance: The Times They Are a Changin’

The opening keynote at Exceedance clearly set the agenda for this year’s conference – the future of risk. Karen White, chief executive officer for RMS, in her opening address, summarized the state of the risk management industry with one of her favorite songs – it just had to be David Bowie and “Changes”. But Karen asked what’s driving these changes, how do our clients see change, and how are they responding? Karen outlined how she had travelled the globe, (and clocked up hundreds of thousands of United MileagePlus points), talking to clients to get a clear-eyed view of what has changed and what to do about it.

Karen discovered that the catalysts for change had come from a wide range of sources, from how bad surprises are becoming, how new opportunities are motivating change, and how technology is changing approaches to risk. And it is a poignant time for RMS to look to the future of risk, as we celebrate and reflect on thirty years in business this year – and the birth of the nat cat modeling industry in 1989. Change has been constant in thirty years, but is now accelerating ahead, as Karen remarked that the next five years will define the future of risk.

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