In today’s ever-changing world, property insurance companies face a daunting challenge of managing the escalating risks posed by catastrophic (CAT) weather events. From the relentless fury of hurricanes to the devastating force of wildfires, the severity of these incidents is on the rise, leaving real estate companies vulnerable to substantial losses. CAT modeling, combined with business intelligence (BI) analytics, plays a vital role in determining reasonable limits for CAT exposures.
CAT modeling is a process that assesses the potential losses and risks associated with catastrophic events such as earthquakes, hurricanes, floods, and wildfires. By utilizing historical data, scientific models, and advanced analytics, CAT modeling provides insights into the potential impact of these events on a company’s property portfolio. It also allows us to determine reasonable limits for CAT exposures and can help guide placement decisions and educate real estate companies of their risks and how to mitigate.
A significant advantage of CAT modeling and BI analytics is the ability to challenge lender requirements. Lenders often impose contractual insurance requirements on real estate companies’ property programs. However, armed with analytics that demonstrate the reasonableness of CAT limits, insurance brokers can negotiate with lenders to lower the required limits. This negotiation can potentially save real estate companies substantial amounts of money.
To illustrate the practical application of CAT modeling and BI analytics, let’s consider a case study involving a business unit of a large real estate client. During the program development process, CAT modeling was employed to reassess the earthquake (EQ) limit initially agreed upon by the client and the lender.
Based on the modeling results, it was determined that the original EQ limit of $3 million was excessive for the client’s specific risk profile. Armed with this data-driven insight, the insurance professionals approached the lender and successfully negotiated a lower EQ limit of $1 million. This adjustment resulted in a significant cost reduction, saving the client $275,000 in property premiums.
By harnessing the power of data and analytics, companies can make strategic decisions that align with their risk appetite and financial goals.
