The Risks of Hazard

The Risks of Hazard blog is devoted to the exploration of natural catastrophe risk, and how to better understand it for insurance. An important aspect of the topic is how the analytics can be delivered. The software that delivers the analytics is as important as the analytics themselves.

Cloud computing is driving a software revolution as astonishing as any other computing revolution of the past 40 years. With the processors and memory moved to the cloud, along with the advance in both of those aspects of computing, it is now possible to build software solutions that would have been impossible 5 years ago. Platform-as-a-Service (PaaS) and Software-as-a-Service (Sass) are the only ways in which innovative software products are being built now. And they are being built to work on the next generation of IT infrastructure being installed by forward-thinking companies everywhere, like Allianz.

Next week I will be heading to Asia to try and find out how insurers, brokers, and reinsurers are handling their risk assessment, particularly for flood risk. Asia is an exciting market for property insurers, with economies growing quickly and property values becoming valuable enough to stimulate thriving markets for insurance — especially for natural catastrophe coverages. It is also a breeding ground for innovation right now, as everyone is working hard to ensure they have the necessary tools to understand risk from all natural catastrophes (and Asia gets them all!). This article by James Nash (of Guy Carpenter) summarizes the situation in Asia and their response to it very well.

The use of risk models and maps to predict the likelihood and intensity of flooding has an inherent, although not immediately apparent, flaw: dependability. Flood maps and models are relatively static, updated every few years (maybe), while weather and climate are extremely dynamic. So how can static models predict dynamic events dependably? A recent study suggests they really can’t.

One of the things that make natural catastrophes inherently unpredictable is the ever-changing nature of the natural world. This is a trivial observation, but it is fundamental to understanding how peril models work and what their limitations are. This post will discuss flood specifically, but the case is the same for all natural catastrophic phenomena.

One of the regular topics of this blog has been the importance of having the best data for the job when trying to understand risk, as well as understanding what can and can’t be done with the data at hand. Earlier this month (March 2015), there was a fantastic case study showcasing this principle in action in the U.K. flood insurance market.

While researching underwriting leakage for an earlier blog post, I discovered another, more prominent type of leakage in insurance: claims leakage. While these two concepts are similar in nature, there is a fundamental difference between them that is worth exploring.

What is the difference between claims leakage and underwriting leakage?

• Underwriting leakage is the gap between optimal and actual underwriting (selection, pricing, and conditions).
• Claims leakage is the gap between optimal and actual settlement of a claim (payment of the claim according to the policy, correctly offsetting the loss).

Put another way: Underwriting leakage is a result of the inability to predict the future, while claims leakage is a result of the inability to accurately assess the past. Leakage is a fundamental concept for insurers because the insurance business is comprised of certain costs and assets balanced with uncertain liabilities. No other non-speculative business is so dependent upon an ability to know the unknowable.