An insurance company’s primary responsibility is to measure & control risk with precision.

Non-Catastrophe Risk

The risk of a home burning from an unattended oven is not the same as that from a wildfire.

We isolated pure non-catastrophic risk, and applied extremely selective machine learning algorithms to consider the fine details of each policy.

The purity of our approach, combined with the sophistication of our non-catastrophe models, results in much richer and more precise recognition of the components of risk, meaning we have unprecedented insights into the driving forces that cause claims.

Classic frameworks that use territories combined with classes of risk and discounts, cannot match our capabilities.

We Must Model Every Possible Loss Event.
No simplified, single output models.

To accurately price for a risk, we must be able model the underlying scientific properties of the events that cause claims using full scale stochastic models for all perils.

We do everything the hard way so that we can pick up every detail that might impact the risk:

• We run full catastrophic event catalogues of at least 10,000 years, if not 100,000 years, for every single catastrophic peril which could affect any policy we look to insure. We do this live at the point of sale, in less than 2 seconds. It is the only way to accurately know the entire loss distribution.
• We built the most sophisticated full scale stochastic wildfire model ever for Canada.
  
  The model incorporates a plethora of data sources and algorithms that underlie the very physics of wildfire spread. This creates a complete set of potential loss events, which are modelled with the risk data to determine intensities, vulnerabilities and insured losses.
• We run 4 million years of live simulations at the time of quote for each non-catastrophe peril. Often times, there are inter-relationships between data that happen in dimensions in which the human brain cannot function. Our models employ an extensive neutralnet framework to capture all unseen interactions, inter-relationships, high order factors and volatilities/dispersions.

We Must Control for Concentration Risk, Live at the Point of Sale

As noted, primary insurance companies largely act as though it is not their responsibility to properly price for catastrophe risk within a single home insurance policy, since they are not the ones ultimately bearing that risk.

Here are the issues though:

1. Aggregation to catastrophes can break you in this business.

   If an insurer writes too many policies that are exposed to a single catastrophic event, and that event happens, they don’t want to be counting on luck to ensure they have enough reinsurance coverage.

2. Even if you are passing off the risk, reinsurance still has a cost.

   An insurer shouldn’t wait until the annual reinsurance renewal to find out if all those policies they wrote in the last year have similar exposures, because if they do, their reinsurance rates will go up unexpectedly, deteriorating returns.

Most insurers check their aggregations once a year when they do their reinsurance renewals. But this is a reactive approach. To facilitate a rapidly growing portfolio and control for aggregation risk, insurers must have an immediate and precise understanding of the accumulation of their risks to individual loss events.

As such we:

• model all catastrophe risks live at the point of sale for each policy, and
• calculate the resulting accumulation risk for each and every event to ensure the overall portfolio remains balanced in terms of risk profile and price.

A Quote Involves Billions of Calculations in 2 Seconds

Personal property policies are the most highly commoditized insurance products that a consumer can buy. Convenience cannot be sacrificed for precision.

Legacy rating methods show a price instantaneously due to their simplistic nature. We cannot fail to deliver that same experience.

We needed to pack billions of calculations into 2 seconds to keep the same customer experience.

Furthermore, there are no shortcuts. You can’t pre-run models. Policyholders often update their risk data at the time of quote, and small changes in data can yield large impacts on risk, and therefore price.