Assembling an effective commercial Property insurance program is similar to solving a Rubik's Cube, the popular 3-D puzzle whose solution involves manipulating the six different colors of the cube so that each face has a uniform color.
Initially, solving the 3-D puzzle was more art than science. Years later, experts — known as "speedcubers" — emerged on the scene, applying mathematical principles, such as group theory and permutation puzzles, to their solutions.
In structuring a global Property insurance program, a broker strategically decides where to position and place different insurance markets within a "panel" or portion of the overall risk.
It is a malleable process where different factors, including risk appetite, capacity and insurance company negotiations, can stretch limits horizontally (quota share) and/or vertically in towers (excess). Yet, unlike the Rubik's Cube, the final panel contains a hodgepodge and prism of different colors in a 3-D schematic that brokers typically display on a computer-generated slide or spreadsheet.
Building a global insurance program is a complex process. In some cases, several brokers will negotiate with multiple underwriters from various parts of the world that may or may not agree precisely on desired terms, conditions, definitions of loss and other specifications.
These inconsistencies may lead to coverage gaps, duplication or nonconcurrency of terms and conditions. In addition, some insurance markets may impose restrictions, such as sublimits or deductibles, while other participating insurers may not.
In this context, assembling the best combination of insurance policies to build an effective global Property program involves both art and science.
Today, insurance program structures are based not only on how underwriting capacity is deployed, but how it is modeled. For instance, risk managers often are presented with hypothetical scenarios, such as the potential impact of 100-, 200-, 500-year catastrophic events on their property portfolios, to help set their related insurance limits.
While the analytics can be complex, technology helps bring more sophisticated math and science applications to the insurance decision-making process. Indeed, technology can reduce the time required for extensive analytics, improve placement results, simplify program recommendations and enhance the overall client experience with respect to insurance renewals or new coverage placements.
By leveraging big data platforms and "deterministic" modeling, which is based on an analysis of past incidents, brokers and risk managers can strengthen each element of their insurance placement process, which typically includes stress testing, marketing, analytics, determining retention and the insurance transaction.
Stress testing
For global enterprises, determining appropriate Property insurance limits can be a challenge. Notably, Property insurance portfolios can have data quality issues. For instance, compiling exact location attributes of every asset in a large portfolio is prone to errors that can arise from incorrect or outdated coordinates or building characteristics. A "statement of value" may have up to a 20% error rate on data attributes, including actual or replacement values and COPE (construction, occupancy, protection and exposure).
These issues may be addressed through rigorous stress testing with past events. By running different loss scenarios based on actual past events, such as hurricanes and earthquakes, risk managers can better understand the actual impact at specific locations and assess the adequacy of their insurance coverage. Third-party data that could be crowdsourced can help compute ground-up losses, retained losses and loss per carrier based on any events simulated.
Open analytics and modeling platforms enable brokers and risk managers to run extensive permutations of loss events so they can make informed assessments of their coverage needs.
Marketability
Every year, risk managers and their brokers meet with underwriters to discuss their insurance renewal objectives. This process generally has been conducted the same way for decades. The goal is to maximize the marketability of the risk so underwriters will provide more favorable terms, conditions, pricing and coverage for each aspect of the exposure.
Because insurance programs involving multiple policies are fungible, positions often can be moved, changed or replaced within the panel. This can enhance the insured's ability to obtain the desired protection with the best available terms.
This process includes a standard submission packet with updated statement of values spreadsheets, coverage specifications, engineering reports, loss reports, etc. Today's technology enables this data to be presented by risk managers and brokers in dynamic ways that can differentiate the account and improve the outcome.
Analytics
Empowering brokers and risk managers with open risk analytics and modeling platforms helps level the playing field during negotiations with insurers. Brokers and risk managers now have access to the same tools and technology used by underwriters to evaluate and price risks. Thus, risk managers and their brokers can "pre-underwrite" the risk and negotiate more effectively with insurers. Pre-underwriting also helps insureds obtain the appropriate coverage limits, reducing the likelihood of a claim recovery shortfall in severe loss events.
Retention
A key challenge in predicting how a program structure will perform involves the time involved to run multiple loss scenarios. Historically, this required taking a policy's terms and conditions, pasting them onto a spreadsheet, and running one simulation at a time.
Today's manuscripted policy forms are complex arrangements of coverages with sub limits, deductibles and exclusions. As such, they require permutations of scenarios to assess how any policy might respond with respect to the coverage provided and loss amounts retained by the insured.
Digitizing your program structure so the policy language is embedded in your analytics platform can reduce errors and provides decision-makers with greater insights regarding how much risk to retain versus transfer.
Transactional
By their nature, insurance placements are transactional. They involve several individuals on the buying and selling side. The cycle time for the average placement (new or renewal) is usually about 120 days, including: collecting values; inspecting locations; creating specs; gathering underwriting data, such as COPE; providing loss runs, etc. Generally the process is inefficient with work duplicated by the risk manager, broker and underwriters. Rather than devoting adequate resources for analysis and modeling, the bulk of the process may involve more mundane aspects of preparation.
However, by increasing the use of analytics and modeling, risk managers and their brokers will be better equipped to create optimal program structures. The added time devoted to modeling will enable them to evaluate their data-driven options, including the adequacy of coverage limits, risk retention versus transfer, and other considerations with confidence.
Further, rather than making risk analysis an annual exercise, new technology facilitates real-time and continuous evaluation. Sharing new data, as well as the results of dynamic modeling activities, throughout the year enhances collaboration and helps ensure the insurance program and risk management operate effectively through the duration of the policy term.
Creating an effective insurance program structure involves both art and science. Relationships, market knowledge, technical proficiency and the financial strength of participating insurance companies are among the key ingredients of a resilient insurance program that can withstand both frequent and severe losses. The use of technology and analytics can help reduce the time and many expenses associated with each step of the insurance placement process and result in insurance programs that deliver protection when and where it's most needed. Similar to Rubik's Cube, many can solve the puzzle of creating an effective program structure with art; however, science will enable them to master the process and achieve the best results.
Eduardo Hernandez is co-founder of Ann Arbor, Mich.-based EigenRisk and head of business development. He has 20 years of risk and insurance management experience in underwriting, placement, product development and sales. Previously, he served as senior vice president, Marsh's CS STARS (now operating under a new name), where he led sales and relationship management for Latin America. While at Marsh, he also was a member of the multinational practice executive committee and served as a client executive for institutional clients.
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