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Introducing Computer-Aided Design for High-Value Risk Transfer

Introducing Computer-Aided Design for High-Value Risk Transfer

Introducing Computer-Aided Design for High-Value Risk Transfer

A Brief Introduction to the High-Value Risk Space

High-value risk is everywhere.  Some high-value risks are visible and obvious – iconic skyscrapers looming above our cities; wide-body commercial jets flying overhead in their approach patterns; cargo ships docked in port and waiting to be unloaded.  Other high-value risks – cyber risk; product liability; contingent business interruption; directors & officers (D&O) exposure – arise within the context of financial, legal or virtual worlds, and suddenly materialize as disruptors of corporate balance sheets.

If sheer size is the defining attribute of high-value risk, then how can we cleanly delimit this risk class?  Established industry indexes and researchers offer some guidance.  Verisk Analytic’s loss index service, the Property Claim Services® (PCS®), defines a catastrophe, more or less, as an aggregate industry loss of $25 million or more.  Swiss Re’s research arm, Sigma, catalogs global catastrophes utilizing a multi-part definition that includes insured losses ranging from approximately $20 million to $50 million.  These leading indexes may provide a useful starting point, but we also know that smaller losses can be highly consequential to a carrier, depending on its size and financial strength.  For example, many of the Florida-based carriers recently affected by Hurricane Irma have property-cat program retentions that are far less than the $25 million PCS criterion.  

In the absence of a clear-cut industry definition, perhaps the concept of high-value risk can only be expressed in relative terms as any risk that is too large to be retained by a single carrier or corporation, thereby requiring its cession and allocation to other selected risk-bearers through a variety of contractual mechanisms.

How does high-value risk originate?  Sometimes, it starts out that way.  According to Airbus’ published list prices, if you want to buy a brand new Airbus A380, you should plan to pay $432.6 million, less your negotiated discounts.  According to maritime transport statistics provided by the United Nations Conference on Trade and Development (UNCTAD), if you need a new VLCC, that is, a very large oil tanker, it will cost you roughly $120 million.  Stand-alone high-value risks are not limited to property lines – it was reported in a May 2017 Intelligent Insurer article that a $600 million cyber program was placed to protect a single large corporation.

Alternatively, high-value risk can accumulate gradually, as smaller individual risks are rapidly written and rolled up into risk portfolios.  Individual homeowner policies worth $250,000 apiece can rapidly aggregate into multi-billion dollar portfolios for a particular geographic region exposed to hurricanes, earthquakes, wildfires or other natural catastrophes.

If we attempt to gauge the size of the high-value risk space, it would seem evident that the high-value risk market is expansive, but counting the associated premium dollars poses a challenge since this space is obscured within the B2B portion of the global P&C insurance industry, and it is fragmented and tiered into numerous sub-markets spanning diverse business classes, product structures, and geographic regions.  Diverse sectors  – spanning large commercial insurance; wholesale & specialty markets such as aviation & marine; reinsurance; ILS products – can all be lumped into the high-value risk market, and premium aggregation across these segments suggests that the high-value market exceeds $350 billion of premium throughput each year – or almost one-quarter of the $1.44 trillion global P&C market.

This enormous market is hardly static, and will likely grow rapidly, powered by a number of global trends.  Better coordination between governments & insurers will ultimately close the so-called ‘protection gap’ of uninsured catastrophe exposures, and greatly expand the use of property-cat coverages for new perils and regions.  Growth in emerging market demand for re|insurance products will strain global risk transfer capacity.  Credit risk and other financial risks are increasingly being carved out from securitization systems, and shifted from banks to insurers.  High-tech firms building autonomous vehicles, smart buildings, drones, robots or commercial space vehicles, will require placement of risk towers that will test the capacity of product liability markets.

 

Placement of High-Value Risk is Complex & Messy

High-value risk is also complex risk.  Since a high-value risk cannot be retained by a single carrier, portions of the risk must be ceded off to other carriers – often on a global scale.  At this point, matters get more complicated.

Frequently, a broker must piece together a ‘layered & shared’ coverage structure to allocate the risk among numerous markets with varying risk appetites.  Involvement of multiple markets increases the chance that non-concurrent terms & conditions will leave hidden coverage gaps within the program.  Since the risk can originate in, or be placed into, in different states and countries, brokers must often collaborate with other producing or placing intermediaries, and comply with the mandates of numerous regulatory and tax agencies.  Cross-border business transactions introduce complications relating to cross-currency conversion rates and multi-lingual wordings.

Not surprisingly, given this complexity, placement of high-value risk remains largely a messy, manual process.  Buyers and their brokers process these risks as one-off transactions using an array of timeworn desktop technologies, especially spreadsheets, digital documents and emails.  Emails sent between the transacting parties often arrive with unwieldy attachments that are easy to download, but difficult to analyze and integrate into the decisionmaking process.  In some cases, graphical illustrations of the intended risk structure – sometimes called ‘mud maps’ by brokers – are drawn and delivered to the client at key placement stages.  As client and carrier demands are slowly reconciled, a placement may drag on for weeks, or even months, before the deal is sealed and ready for back-office processing.

The upper echelons of the high-value risk space are populated with spreadsheet wizards who have effectively engineered their own, individualized IT systems using the traditional desktop toolset.  This outcome is understandable – spreadsheets were invented in the 1980s, but new features and macros are constantly added to keep professionals busy and intellectually engaged.  Once a placement is done, documents can be tucked away in folders stored on laptops, desktops or company servers – ready for retrieval when the next annual renewal cycle rolls around.

Not surprisingly, the both the administrative costs and profit margins associated with these large, one-off transactions are high, and industry leaders have started to complain openly about the fiscal burden of distribution costs that can consume almost one-half of each premium dollar paid by insurance buyers.  The enormous value at stake in these transactions can trigger other hidden costs.  For example, errors or misunderstandings in structuring complex coverages can spark costly coverage disputes and injure long-standing trading relationships.

 

Toward an Interactive, Graphical Approach to Risk Design

Since an earlier market shift from paper to desktop computing and the internet in the 1990s, the placement process has essentially relied on the same set of software tools for the last two decades.  These long-standing business practices have their staunch defenders, but they can hardly be expected to survive intact in the brave, new world of insurtech innovation.

Deployment of cloud-based risk structuring platforms that draw upon CAD-style, graphical, interactive toolkit can provide re|insurance buyers and their brokers with a dynamic risk design alternative to the spreadsheets and other manual methods currently used to manage these risks.  Using a new class of software tools that provide an intuitive, user-friendly ‘visual framework’ for these complex risks, risk advisors and specialists can follow the path successfully taken by architects, product designers, graphic designers, microchip engineers, and other professionals who have transitioned from labor-intensive, error-prone manual processes to sophisticated CAD systems in recent years.  According to an August 2017 study published by Grandview Research, the global 3D CAD software market has expanded to more than $8 billion in 2016, driven by this professional shift to greater design automation.

The OnRisk software platform, and similar products, platform utilizing a CAD-based, visual framework can potentially speed the placement process; cut placement costs, and improve buyer outcomes by offering a number of advantages to buyers & their brokers.

 

  • Common Risk Transfer Framework.  A software platform providing a common risk transfer framework that can be understood & shared by all transacting parties, thereby mitigating the risk of complex corporate litigation arising from misunderstandings and ambiguities relating to placement structures.
  • Concentration of Risk Transfer Data.  A visual framework concentrates and updates risk transfer data to significant business effect.  This data concentration supports real-time risk transfer since the framework it provides both a concise, intuitive snapshot of a company’s net risk position and a rich, graphical environment for the design of on-demand re|insurance transactions.
  • Rapid Assembly of Complex Structures.  A visual framework enables the rapid assembly of complex multi-coverage structures, and convenient comparison of alternate structured solutions.  By tracking & updating key placement metrics in real time, a visual framework enables a user to shift & pivot a deal strategy more rapidly.
  • Enhanced Control of Deal Distribution.   A visual framework enables user to construct & track complex distribution networks (e.g., corporate subsidiaries & producing brokers for multinational insurance program).  Distribution control enables a user to track expense loading across various distribution elements.
  • Integrated Rating/Modeling.  Using plug & play APIs, rating and modeling programs can be integrated directly into the risk structuring process to ensure that complex structures remain closely tied to critical rating factors.

 

This novel approach to risk design reflects the simple proposition that each and every high-value risk will be structured and traded globally on computer screens.

About Frank Sweeney

Frank Sweeney is the CEO & Co-Founder of the insurtech startup, OnRisk, which is developing a cloud-based risk structuring platform for high-value P&C risk. Frank was formerly a Co-Founder, COO and Director of CATEX International, a reinsurance services company with offices in Princeton and London. Prior to his tenure with CATEX, Frank worked as a counsel to the Governor of New Jersey; legislative counsel to the Energy and Commerce Committee of the U.S. House of Representatives; and policy analyst and project manager with Science Applications International Corporation (SAIC). He is an attorney, and he holds a J.D. from the Stanford Law School and an M.S. from the Stanford Engineering School. He is a graduate of Princeton University (A.B.; Honors; Phi Beta Kappa).

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Frank Sweeney is the CEO & Co-Founder of the insurtech startup, OnRisk, which is developing a cloud-based risk structuring platform for high-value P&C risk. Frank was formerly a Co-Founder, COO and Director of CATEX International, a reinsurance services company with offices in Princeton and London.

Prior to his tenure with CATEX, Frank worked as a counsel to the Governor of New Jersey; legislative counsel to the Energy and Commerce Committee of the U.S. House of Representatives; and policy analyst and project manager with Science Applications International Corporation (SAIC). He is an attorney, and he holds a J.D. from the Stanford Law School and an M.S. from the Stanford Engineering School. He is a graduate of Princeton University (A.B.; Honors; Phi Beta Kappa).

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