Current data models put exposure and results in separate containers. For example, the RMS EDM contains exposure and contract terms and the RDM contains modeling results. There is limited information about how models were used to produce the results. This incomplete picture of the analysis is confusing to consumers of the information – leading to potential misunderstanding of the analysis and to inefficient processes that need to work around the shortcomings of the information. The RDOS contains exposure and results in the same construct, and adds more complete information about model setting, linkage between exposure and results, and other domain data – providing a clearer picture of how analysis results were derived.

A new generation of higher definition models requires larger input and output data sets and can generate results that are structured differently from the existing algorithms. Models that address new classes of business, such as cyber, have data requirements that are radically different from traditional property cat models. As modeling options multiply, however, modeling efficiency can plummet as users process too many data formats, and contend with ever-increasing interface requirements. As a remedy, the re/insurance industry needs a single container for exposures and results from any model fit into a single exposure or results format. Rather than attempting to shoehorn unlike data into the old constructs for new models, the RDOS is a universal container with the flexibility to suit the new models and products being created throughout the industry.

The best approach to financial modeling uses a language-based description of contract terms. The RDOS uses Contract Definition Language (CDL) to build contract terms and conditions that can represent virtually any policy, treaty, or insurance-linked security.

Financial modeling is further complicated by the need to refer to business structures. This is particularly the case when describing reinsurance programs, where the subject of a given treaty can be the net of losses that come from combined business units, perils, and inuring reinsurance. Because there is currently no way to represent business hierarchies or complex contract interaction within the modeling framework, building an analysis that combines results and properly applies coverage is often difficult and poorly documented – resulting in wasted time and a greater likelihood of errors. Structure Definition Language (SDL) is a new invention that documents and automates the creation of company-specific financial perspectives – creating significant value and efficiency.

Because the RDOS is a NoSQL construct, it can store language – enabling these new financial modeling solutions. However, CDL and some aspects of SDL can be converted to a tabular format, and the RDOS can also store financial terms and conditions in a tabular form.

Re/insurers increasingly seek to apply enhanced pricing and exposure analytics to lines of business beyond property catastrophe. Existing schemas are very difficult to extend to new classes of risk and associated coverage, but the RDOS is designed to facilitate the addition of new lines of insurance or risk with minimal disruption and effort.

A broad industry work group called the Global Exposure And Clash (GEAC) project created schemas for 14 lines of insurance. Each line has a different set of fields that describe the risk characteristics that are key to analyzing and underwriting that particular type of risk. The RDOS is capable of accommodating these new schemas.