Unlocking Pricing Potential: Strategies to Enhance Accuracy Beyond GLM Limitations
When it comes to pricing models in the insurance industry, analysts often gravitate towards Generalised Linear Models (GLMs) due to their foundational role in pricing strategies. However, a sole focus on model mechanics can lead to overlooking critical limitations inherent in these models. In this article, we will delve into the assumptions of GLMs and how actuarial expertise can help overcome these challenges.
Understanding the Limitations of GLMs in Pricing Models
As powerful as GLMs can be, they operate under two primary assumptions that may not hold true in real-world scenarios:
- Data has full credibility.
- Randomness of outcomes is uncorrelated.
1. The Assumption of Full Credibility in Data
GLMs assume that all data points are fully credible, meaning that the estimates derived from the data are regarded as completely reliable, regardless of sample size. This can lead to significant issues, especially when working with limited data samples.
For instance, consider a pricing model for insurance claims in various Mexican cities. A basic GLM might treat major cities like Mexico City, Guadalajara, and Monterrey—which have numerous claims—as equally credible as smaller cities like San Cristóbal de las Casas, which may have only 120 claims. In such cases, a single high-cost claim can skew the average significantly. GLMs fail to adjust for this disparity in data credibility.
To address this limitation, actuaries can employ credibility weighting techniques such as Bühlmann Credibility, which adjusts severity estimates based on the amount of credible data available. The formula is as follows:
Ci = (wi * Si + (1 - wi) * S0)
- Ci: Adjusted severity for city i
- Si: Observed severity for city i
- S0: Overall expected severity
- wi: Credibility factor
By utilizing these techniques, actuaries can create more robust estimates that account for the reliability of the data.
2. The Assumption of Uncorrelated Randomness
Another critical assumption of GLMs is that each record in a dataset is independent, with no inherent relationships among observations. However, this often does not reflect reality.
Take, for example, auto insurance policy renewals. A dataset tracking a customer over several years treats each year as a separate and independent record. However, a driver involved in an accident in the first year is likely to exhibit similar behavior in subsequent years, leading to potential inaccuracies in accident frequency predictions.
Additionally, when modeling insurance claims related to natural disasters, such as a hurricane in Cancun, GLMs fail to account for the correlation between multiple claims arising from the same event. This oversight can lead to significant overestimations of future claims.
To mitigate these issues, actuaries can use techniques such as:
- Generalised Linear Mixed Models (GLMMs): To link related records, such as multiple years of data for the same policyholder.
- Generalised Estimating Equations (GEE): To adjust for correlated data.
- Aggregated Data Analysis: To capture long-term trends instead of treating each record as isolated.
Should Actuaries Abandon GLMs?
Absolutely not. GLMs are still a vital tool in insurance pricing and can yield reliable results when applied correctly. However, actuaries play an essential role in interpreting these models, identifying biases, and ensuring that the outputs reflect actual business dynamics.
Instead of relying solely on GLMs, insurers can enhance their predictive accuracy by integrating expert judgment and advanced statistical techniques. Exploring hybrid approaches that combine actuarial expertise with machine learning can lead to improved pricing strategies and more accurate models.
While GLMs are not outdated, they require careful oversight. Actuaries must remain receptive to innovation while ensuring that their models accurately reflect real-world conditions.
For more insights, read the full blog from Quantee. Stay updated with the latest in FinTech by visiting FinTech Global.
