Wildfire season started early and ferociously. According to CAL Fire’s website, as of October 4, over 4 million acres have burned. That is more than 20 times higher than the acres burned in 2019 for the same time period. Five of the top twenty largest wildfires in California have occurred in 2020 and wildfire season is not yet over. Although I’m very lucky to be safe from the fires, there are reminders of the devastation with the dark skies and the falling ash. Checking both the air quality index and pandemic metrics has become my daily habit.

Much like with auto insurers in the early 20th Century, it’s only been recently that the insurance industry has realized how significant—in both size and importance—cyber exposures are. Further, this significance will likely continue to increase for the foreseeable future, as businesses continue to become more technology-driven and hostile actors continue to find new ways to commit cybercrimes that steal from and disrupt businesses.  

Depending on the number and complexity of models that exist for an insurer, model monitoring runs the risk of becoming overwhelming very quickly. A first step to building a solid model monitoring program is to catalog models in use, including any state-specific versions and assessing the relative importance of each. That could be assessed in a variety of ways including by the number of policies that a model potentially impacts, or the premium volume that a model has influence on. An insurer may also want to assess the complexity of the model and its potential stability. Combining all of the different characteristics helps lead to determining which models are most important to monitor first (relative priority).

We were very pleased to be chosen to participate in the Pinnacle U program for 2020. We decided to investigate the ever-evolving role of simulation in insurance, with a focus on the practicality and implementation of the Monte Carlo Simulation (MCS). Our presentation followed a funnel approach starting with the general procedure for any type of simulation. We then narrowed in on theoretical and practical methods and uses of simulations for the insurance industry. Finally, we focused specifically on MCS and a few applications of that particular simulation technique.

Continuing our previous deeper dive into certain aspects of the Modeling Lifecycle concept, this installment is the last entry in the lifecycle—model monitoring. While monitoring is the last step in the process, it is arguably one of the more important steps since it can send a modeler back to nearly every other earlier step in the lifecycle. However, model monitoring often receives the least attention.