Pricing is an unending battle in the insurance world. With the rise of more comparative raters within the P&C or property and casualty market, prospects can compare prices instantly, and, unsurprisingly, most tend to pick the lowest options. However, ballpark pricing can prove costly for insurers; it can improve competitors’ customer base, attract risky consumers, and reduce consumer retention. For this reason, actuaries often spend considerable time fine-tuning their pricing models.
But how do actuaries create insurance premiums? Moreover, is it possible for artificial intelligence to be helpful in this case? When customers apply for policies, insurers provide customized premiums reflecting the risks of claims being filed and, more importantly, the amount they’ll end up costing during the time frame of the policy coverage. Predictive models are used for pricing to analyzing these risks, measuring the amount customers are anticipated to claim depending on the available data at the underwriting time.
Generalized Linear Model
For years, the generalized linear model or GLM approach has been used to build pricing models for their interpretability and flexible structure. Pricing teams generally depended on historical data, covering some policy attributes and many other customer information to create their models. The models are then divided into severity and frequency, making the outcome easier to understand. However, more and more insurance providers are moving away from GLM because of intelligent insurance software solutions powered by artificial intelligence.
Challenges
Insurance companies have many features they can work with, and more often than not, they correlate with one another. However, GLM relies on manual processes for feature selection and interaction, limiting the features incorporated into the model while keeping it from getting complex data interactions. Additionally, it needs a lot of manual analysis and calculations, which can take weeks, if not months, of refinement for the motor product insurance’s pricing model.
Unfortunately, GLM isn’t as accurate as it should be, especially when compared to machine learning and artificial intelligence-based algorithms. And this is mainly why pricing teams are starting to adopt AI and ML technologies.
Artificial intelligence
The artificial intelligence methodology isn’t dissimilar to the conventional approach: creating models that use historical data policies that can anticipate incurred claims. However, there’s a difference: the models it uses aren’t necessarily linear, may not shadow the severity frequency approach or provide rating tables, and can make use of more significant volumes of data and variables. All the while, AI can keep explainability and interpretability.
In other words, artificial intelligence technologies can produce much more accurate results within a shorter period, from raw data to implementation. This not only saves valuable time but also minimizes the risks of incorrect pricing.
Conclusion
Artificial intelligence has opened the door for businesses across many industries to enhance their operations in different ways, and insurers aren’t an exception. Thanks to technology, carriers can create more accurate pricing for their insurance premiums and avoid any inaccuracies that could end up losing their customers or losing their competitive edge.
