This blog post by Jay Kamdar is the runner’s up entry in Quantiphi’s February’21 QuriousWriter Blog Contest.

In addition to COVID-19, devastation from extreme calamities and catastrophes has spiked sharply in recent years. With close to 23 states being affected by the spring floods in the USA and Hurricane Isaias hitting the East Coast, the destruction in terms of properties and human lives has been incalculably high. The insurance industry paid out more than $100 billion globally in the past two years for tropical cyclones and other wind-related claims.

The cataclysm has led insurance companies to emphasize the importance of efficient catastrophe modeling for better loss predictions. Catastrophe modeling, introduced in the 1980s, is the process of using computer-assisted calculations to estimate the losses that could be sustained due to a catastrophic event such as a hurricane or earthquake. It has led to a paradigm shift in the underwriting process and has increased the risk landscape by leaps and bounds.

While existing models account for certain levels of risk, the emerging risks due to drastic climate changes raise concerns for significant improvements in loss reserve planning and risk exposure assessment for auto and home insurers. Similarly, life and health insurers need to account for catastrophes like pandemics while revamping their underwriting guidelines. Thus, insurers are compelled to manage the reserves better using data-driven decision making while mitigating and reducing losses resulting from these calamities.

Data types for decision making

According to a 2017 article in the Financial Times, “Before catastrophe modeling came into common use, insurers would frequently go bankrupt after a single catastrophic event.”

Amalgamating science, technology, engineering knowledge, and statistical data to simulate the impacts of natural perils in terms of damage and loss can prove to be a boon for the financial services and insurance sector. They can clearly define distinct modules and the type of data used to estimate the financial impact of catastrophic events.

Event Module: Generates thousands of possible catastrophic event scenarios based on a database of historical parameter data. Each kind of catastrophe has a method for calculating potential damages taking into account data related to history, geography, and geology.
Intensity/Hazard Module: Determines the level of physical hazard specific to geographical locations using location-specific risk characteristics for each simulated event.
Vulnerability Module: Quantifies expected damage to properties and structures from an event, taking into account the exposure characteristics and event intensity. Data related to the occupancy, construction, number of stories, year built and square footage of that property or structure is taken into account.
Financial Module: Measures monetary loss from the damage estimates.

The data analyzed in catastrophic modeling can be divided into three key metrics:

Exceedance Probability (EP): EP is the probability that a loss will exceed a certain amount in a year.
Return Period Loss: It describes the number of years that may pass between two periods where the threshold amount is exceeded.
Annual Average Loss (AAL): It is the average loss of all modeled events, weighted by their probability of yearly occurrence.

The model inferences often prove to have a significant impact on the underwriter’s decision in pricing the contacts. These models dictate the risk appetite of the organization and determine reinsurance management. In addition, catastrophe modeling enables businesses to monitor exposure growth, geographic spread, evaluate portfolio expansion/contraction, set capital adequacy, adhere, and respond to evolving regulatory requirements, and stress test portfolios.

With the drastic changes in climate resulting in widespread disasters, catastrophe models must be deeply embedded in operations to improve risk management. Companies should leverage these models to improve their competency in facing these catastrophes and minimize the adverse effects cost-effectively.

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