Pros and Cons of Catastrophe Modeling

Catastrophe modeling used to be considered very complex and difficult to use. Just a few decades back, most insurance companies were either unwilling to or unable to use catastrophe models. However, over the past couple of decades, the field of catastrophe modeling has seen rapid change.

The increase in computing power has led to catastrophe models becoming more and more accurate. As a result, it has now become a very important part of the reinsurance industry. There are advantages as well as disadvantages that accrue as a result of using catastrophe modeling.

In this article, we will have a closer look at both the advantages and disadvantages.

Benefits of Catastrophe Modeling

There are several distinct advantages of using catastrophe modeling. Some of these advantages have been listed below:

  • Better Risk Management: Catastrophe modeling can help reinsurance companies predict the amount of risk they have on their balance sheet. Catastrophe modeling allows reinsurance companies to evaluate which risks they can undertake and which risks will make their portfolio too exposed to a loss scenario.

    Reinsurance companies have built automated risk management models which directly take input from such catastrophe models.

  • Preventive Development: Catastrophe modeling is also good for other entities such as governments and mortgage companies. Such models help governments and mortgage companies identify which areas are likely to be prone to natural disasters. In the past, excessive development has taken place in cities which are prone to natural disasters.

    Catastrophe modeling ensures that insurance companies do not cover such areas or take a significant risk premium to do so. The end result is that development is reduced in such areas and the loss of human lives, as well as property, is avoided.

  • Underwriting and Pricing: Catastrophe models are a very important factor in the pricing and underwriting process followed at reinsurance companies. This is because catastrophe models allow reinsurers to identify specific risks which arise in specific geographic locations and then charge premiums accordingly.

    In the absence of catastrophe modeling, reinsurance companies have been distributing the risks among all participants. This leads to overcharging some customers while undercharging others. Catastrophe modeling ensures a fair distribution of premiums based on each risk covered.

  • Portfolio Risk Analysis: Not all reinsurance companies can afford to take the same risks. This is because the new risk being underwritten eventually becomes part of an overall portfolio.

    Now, the important thing to understand is that catastrophe modeling helps reinsurance companies what their current portfolio is, what the risk involved in the current portfolio is and what impact will including a specific risk have on the overall portfolio of the reinsurance firm. This careful selection of additional risks helps companies avoid situations where they face a cash crunch.

  • Stress Testing: Lastly, catastrophe modeling allows reinsurance companies to simulate different kinds of natural catastrophes which may occur and also what their financial impact will be. This helps the reinsurance companies better understand the value at risk of their portfolio as well as the kind of financial losses they will suffer in an adverse event. Hence, catastrophe modeling helps in better cash flow planning for the reinsurance company.

Drawbacks of Catastrophe Modeling

Even though catastrophe modeling is considered to be a huge advancement in the field of reinsurance, it still has several drawbacks. Some of the most important ones have been listed below:

  • Lack of Input Data: Firstly, it is important to note that catastrophe models are like all other data models. This means that as the quantum of data given to the model increases, the accuracy of the model also begins to increase. However, when it comes to catastrophe modeling, the amount of data is limited. This is because very few governments started recording catastrophe-related data a few decades back. Also, most of the records which are available are not digitized. This means that a lack of data reduces the accuracy of catastrophe modeling.

  • Climate Change: Catastrophe models assume that the pattern of disasters that happened in the past will be similar to the pattern of natural disasters which occur in the future. However, it is important to realize that climate change is drastically altering the environment. This means that some of the disasters which are taking place today cannot be predicted by any model since they don’t have any correlation with past events.

  • Cannot be Used in Isolation: Another significant drawback of catastrophe models is the fact that the data provided by catastrophe models cannot be used in isolation. It needs to be considered while keeping other scenarios in mind as well. For example, the data provided by catastrophe models need to be double-checked with weather reports to determine their accuracy.

  • Different Results in Different Models: Also, since catastrophe models give out predictions based on the data which has been used in their input, the predictions provided by different catastrophe models can vary significantly. It is possible for two different catastrophe models to give out two very different predictions. As a result, relying on the accuracy of a single model is difficult. Reinsurers often use a combination of in-house as well as external models in order to come to avoid the bias which may be inbuilt into a single model.

The fact of the matter is that catastrophe modeling has several pros as well as cons. However, with the passage of time, the drawbacks are being neutralized and the advantages are increasing. This is the reason that it can be considered to be an emerging technology in the reinsurance domain.

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