We are excited to have released version 2.0 of Oortfolio. This significant release of Oortfolio brings to the market substantially more company and industry profiles, enhances our liability catastrophe model to reflect how insureds and insurers react to emerging science under the occurrence and integrated occurrence (Bermuda) policy forms and introduces a robust set of features that continues to improve upon the quantitative underwriting tools and metrics for managing liability insurance portfolios already present in Oortfolio.
This release leverages Praedicat’s expertise in data science to incorporate approximately 3,000 new algorithmically generated company profiles in the company database. Algorithmically profiled companies represent an automated, data science-based process that combines data from multiple sources to suggest likely links between a company, an industrial/commercial activity, and use of a Litagion® agent in that activity by the company. Oortfolio now represents not only the Fortune 1000 analyst profiled companies with US liability exposure but also a large fraction of the large, medium and small capitalized companies on the NYSE and the NASDAQ stock exchanges and the largest private companies in a broad range of industries.
Also, Oortfolio expands the industry database by incorporating SIC and ISO GL industry classifications in addition to NAICS (6 digit) classification to facilitate the representation of small and midmarket companies in your portfolios.
Oortfolio v2.0 is powered by an enhanced Nekomodel™, Praedicat’s breakthrough forward-looking liability catastrophe model. The enhanced Nekomodel now supports 46 Litagion agents and improves the way the latest science and exposure information is used to quantify and manage liability catastrophe risk. This release will also smooth sampling errors in the tails of the Nekomodel simulation.
In addition, with this release, you can:
- Upload your own portfolios directly into Oortfolio from your computer
- Export portfolio, policy, scenario and model data directly to your computer
- Evaluate a portfolio against an updated library of realistic and extreme disaster scenarios using an enhanced scenario display and more intuitive workflow
- For portfolios with multiple policies for the same industry or company, you can now assess the results, such as marginal impact to the portfolio, rolled up at the industry or company level to quickly identify drivers of risk
- Present a range of outcomes for policies written on the occurrence policy form
Note that your existing portfolios stored in Oortfolio are now updated with the latest Nekomodel information. Please reach out to your client success manager if you’d like a supplementary release document describing in detail the Nekomodel methodology enhancements and the impact to your portfolios.
Expansion of company profiles with the introduction of algorithmically profiled companies
A new company filter on the Companies page called Filter by Company Profile allows you to filter the company list for algorithmically profiled companies or analyst profiled companies. Algorithmically profiled companies are represented by a placeholder dartboard visualization in black-and-white, whereas analyst profiled companies are represented by a company-specific dartboard summarizing the company’s risk of litigation attributable to Litagion agents they manufacture or incorporate into their products. Algorithmically profiled companies function identically to analyst profiled companies in Oortfolio—they have ground up economic losses, underwrite mode, and can be incorporated into portfolios.
Expansion of industry profiles with the introduction of SIC and ISO GL industry classifications
If your portfolio includes small and midmarket companies, you can now represent those companies via an industry identified by its SIC or ISO GL code in addition to the NAICS (6 digit) currently available. A new industry filter on the Industries page called “Industry Name” allows you to display NAICS, SIC, or ISO GL industry classifications. SIC and ISO GL industries will function identically to current NAICS industries in Oortfolio—they have ground up economic losses, underwrite mode, and can be incorporated into portfolios.
Also, to more comprehensively represent all industries within a classification, Oortfolio now includes industries without any assigned Litagion agents. This will allow all policies within a portfolio to be represented in Oortfolio, as long as industry codes are provided for the policies. Industries without any assigned Litagion agents will be represented by a placeholder Sawtooth visualization in black-and-white.
Data submission template upload
You can now upload a portfolio directly from your computer to create your own portfolio in Oortfolio via a simple 3-step process:
- Complete the data submission template (downloadable from Oortfolio);
- Upload the data and allow the Oortfolio validation algorithm to check for errors; and
- Process the validated data into a portfolio.
You can also keep track of all your uploaded portfolios and track status of the validation or portfolio creation via an Upload History dashboard.
Export Oortfolio results
This release introduces the ability to directly download portfolio, policy, scenario and modeling data to your computer with a single click. Simply click the export button on a portfolio summary page, and the following information will be downloaded in Excel format:
- Portfolio Loss Summary Statistics
- Portfolio Exceedance Probability Curve Data Points
- Portfolio Loss Latency Curve Data Points
- Portfolio Litagion Marginal Contribution
- Portfolio Company Marginal Contribution
- Portfolio Industry Marginal Contribution
- Policy Level Loss Summary Statistics
- Policy Level Exceedance Probability Curve Data Points
- Policy Loss Latency Curve Data Points
- Policy Litagion Marginal Contribution
- Scenario Description and Statistics
You can also keep track of your download requests and track their status via a Reports dashboard.
Enhanced scenarios
You can now analyze the losses that a portfolio could experience with approximately 50 new realistic and extreme disaster scenarios.
- Realistic Disaster Scenarios (RDS) describe catastrophic mass litigation events that Praedicat considers unlikely but minimally probable and consistent with current science and law.
- Extreme Disaster Scenarios (XDS) describe hypothetical liability catastrophes that lie outside of Praedicat’s estimated probabilistic model. These scenarios explore the magnitude and distribution of losses in a range of conceivable disasters that would require extreme shifts in science or law.
We have also enhanced the scenarios format to make them easier to read and understand. In addition, we have implemented a more intuitive scenario workflow to access and interact with scenarios and provided the ability to directly download detailed scenario data to your computer.
Easily identify the Litagion agents, companies and industries driving portfolio losses
With this release, it is now possible to quickly assess the holistic impact of a company or industry in the portfolio summary. The results are now displayed as rolled up, meaning that if your portfolio has many policies for the same company and/or industry, the summary results will display the marginal effect on the portfolio of removing all policies associated with that company or industry. This will allow you to quickly identify which companies and industries are driving the losses in the portfolio.
In the figure below, the SIC industry “Industrial Organic Chemicals, NEC” represents the marginal impact to the portfolio PML(5) for all policies within the Industrial Organic Chemicals industry. Similarly, the marginal PML(5) impact of all polices for a single company will be displayed under one company name in the companies section.
Enhanced underwriting capabilities for the occurrence policy form
This release introduces a range of possible outcomes for policies written on the occurrence policy form. Within the underwrite mode for a company or an industry, there are now two occurrence policy trigger types – Occurrence (High) and Occurrence (Low) to generate insured losses. Ground-up losses are also available for both Occurrence (High) and Occurrence (Low) policy forms.
Due to the many ways occurrence losses can be allocated in court, the Occurrence (High) and Occurrence (Low) trigger types are provided to represent two possible scenarios of allocation, as described in further detail in the following section.
Alternative implementations of the integrated occurrence and occurrence policy forms
Modeling losses stemming from the Bermuda form requires a model of the insured’s decision to declare an integrated occurrence encompassing a set of related claims. In this release of Oortfolio, this decision (informally referred to as “batching”) is now modeled as a function of “liability risk,” the likelihood that a plaintiff will prevail in court. Once liability risk exceeds a certain level for a set of related claims, the model assumes affected insureds will choose to declare an integrated occurrence. The model further assumes that all policies written in years subsequent to the batching decision will exclude that risk from coverage, regardless of the policy form.
Modeling losses stemming from the occurrence form requires an assumption of how losses attributable to the same underlying event (or “occurrence”) will be allocated across available policy years. In this release, users now have available a range of estimated losses from the occurrence form, representing the range of common loss allocation methods used in various jurisdictions. This range is described by two alternative estimates of losses, called Occurrence (High) and Occurrence (Low). Occurrence (High) assumes that losses are allocated evenly across policy years between the current policy year (2017) and the year in which the risk is excluded from future coverage (the “batching” year). Occurrence (Low) assumes that losses are allocated evenly across policy years between the year of first exposure (which is typically many years prior to the current policy year) and the batching year.
Please reach out to your client success manager to receive a more detailed methodology description.
