Property Analytics vs. Casualty Analytics: A Tale of Two Cities

June 12, 2017

Let’s go back to school. Luckily you won’t have to read Charles Dickens again, but you will need to relearn a bit of math, logic, and history.

First, here’s a math problem for you. Don’t worry, it’s simple arithmetic. Add up everything you spend on modeling for property catastrophe risk—salary and benefits for all modeling staff, hardware and software infrastructure, and fees paid to modeling vendors. Now do the same thing for casualty catastrophe risk. No need to be exact, rough estimates are fine. Now look at the ratio of what you spend on property analytics to what you spend on casualty analytics. If you are like most (re)insurers, that ratio may be 10 to 1. It may be 100 to 1. Or you spend nothing and have to divide by zero, which according to our high school teacher, is undefined.

Now here’s a logic problem for you. Take a look at where you write business and what business you actually write. If you are like most (re)insurers, you likely write an equivalent amount—or perhaps even more—casualty insurance premiums than property insurance premiums. Logically you need to ask yourself, Why am I investing so unevenly in analytics across both sides of my business?

Now let’s go to history class. According to A.M. Best, net ultimate asbestos losses stand at USD 100 billion.1 By comparison, the costliest natural catastrophe of all time is Hurricane Katrina, which would cost USD 49 billion in today’s dollars. And it gets worse. Katrina claims have essentially all been settled; for asbestos, however, the P&C industry continues to pay out about USD 2.5 billion in claims annually. The reason that insurers are still paying claims on exposure to asbestos that happened decades ago is that many asbestos-related illnesses such as mesothelioma have a latency period of 40 years or more.2 Because of the time it takes for injuries to manifest, courts have applied a “discovery rule” that negates the statute of limitations. In most states injured parties have 12 to 24 months from the time of diagnosis to file a claim, regardless of when the original exposure to asbestos occurred.3 Because many asbestos manufacturers were driven into bankruptcy years ago, enterprising trial lawyers are now suing contractors and other companies in the asbestos-related supply chain.

Okay, your back-to-school nightmare is over, but another is about to begin. You are back to being an adult, doing your job at (re)insurer XYZ, when the industry’s next casualty cat hits. Let’s call it asbestos 2.0. And just like when a hurricane hits, you are going to be bombarded with questions—from your CEO, from your board of directors, from rating agencies, from regulators. Just how much can we lose? Does this represent a threat to solvency? For how long will we lose money? What could we have done to avoid this? Ask yourself honestly, How am I going to answer these questions? And if you can’t provide detailed and credible answers, what are the implications for your career?

There is a perception that casualty risk cannot be modeled, but thanks to modern analytical techniques and the explosion in data, casualty risk can indeed be modeled. Some (re)insurers may still be hesitant about the additional technology infrastructure that casualty analytics might require, along with the need to hire more modeling resources. But these concerns are misplaced. Casualty analytics solutions exist that are SAAS/cloud-based, eliminating the need for incremental IT infrastructure spend. More importantly, the same modeling resources dedicated to property cat modeling have the skill sets to easily pick up casualty analytics, and it is likely advantageous for the same teams to be working on both sides of the business. Ultimately, whether new modeling resources need to be hired will vary by the individual situation. Ultimately, the question is not “Can you afford a casualty analytics solution?” but “Can you afford not to have one?”


Learn how the Arium casualty analytics platform can help you understand your risk


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