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Improving analytics for your property & casualty distribution channels

By Justin Milam, FCAS, MAAA | August 23, 2022

While analytics are becoming common across an insurer’s various functions, simple heuristics like loss ratios remain the focus of property & casualty (P&C) distribution channels.
Casualty|Insurance Consulting and Technology|Property Risk and Insurance Solutions
Insurer Solutions|InsurTech

When an insurance company is looking to improve its use of analytics, especially in its property & casualty (P&C) business, pricing is always near the top, along with underwriting, claims and marketing. One area that is often overlooked is analytics at the agency or distribution channel level.

In an informal poll at a recent presentation on this topic with clients, less than 10% of companies are using anything more complex than a traditional loss ratio analysis in their P&C sales channels. The lack of advanced analytics in distribution is common. Use of calendar-year loss ratio as part of an agent’s sales goals and bonus structures are ideal to use to rank agencies and offer a good starting point for other agent initiatives such as a review of agent business practices.

So how do we get from simple heuristics to a system that is actually predictive of whether an agent will succeed in the future?

Asking the right question

A company could have many objectives when reviewing agency data. When actuaries and data scientists work with underwriters and field executives, the most important point is to ensure the model answers the right question. The best predictive modeler at your company is going to struggle to succeed if the business ask is not clearly understood.

Below are a few examples of requests for agency data that would require models that have subtle but material differences in how they are constructed.

  • New agent appointments are not having the success of previous appointments. Which agents need additional training?
    • A model that will typically include agent years of experience may not make sense if you only want to focus on new agents.
  • Underwriting has capacity to audit the book of business for 5% of the agency field force. Which agents should be audited?
    • Capacity to underwrite 5% of the agents or 5% of the unit counts? This could make a difference in how the final output is put together.
  • What characteristics determine whether an agent is going to be successful or not?
    • How is success defined? Low loss ratio? High conversion rate? Lots of business written? Likelihood of an agent staying with the company long term? If you ask four areas of your company to define characteristics for success, you’ll likely get six or seven answers.

Collaborating with business areas

Once you have determined what question you are trying to answer, it is important to continue to collaborate with the business areas that will eventually use your model. Their business insights are invaluable in finding the right variables and interactions to include.

Working closely with them will also build rapport and help to gain buy-in of your final model. This will also allow you to gauge the appetite of using a model that may not be overly interpretable. If they are trusting of the process you may be able to hand over the output only, but typically the “why” will still be important.


Once you start modeling, there are many challenges with reviewing agencies that are quite unique and could require adjustments to your models.

  • Differences by region, company or line of business that could bias the model
    • If an underwriting rule is one of the variables in your model but states have different underwriting guidelines the differences by agent may not be the correct thing to focus on.
  • Adjusting for state rate level indications
    • If a state has a high-rate level indication this may be something that you want to adjust for before modeling. Alternatively, if there is some likelihood that the high loss ratio is driven by a poor performing agent, it may be important to keep it in the model.
  • Variables that are purposely mispriced
    • If there is a target market your company is trying to grow, it’s possible that the high loss ratios are more prevalent because of mispricing than anything negative the agent is doing.
  • Lifetime client value
    • Recently hired agents will have a greater percentage of their book of business newly written with the company. For many lines of business, new business has a higher loss ratio than renewal business. Making an appropriate adjustment for this can have a large impact on the value of your model.

Modeling considerations

Balancing buy-in and usability from the business side with making the model as sophisticated as possible is key. When starting out, some simple one-way or two-way interactions may be the most appropriate model to build until there is comfort with the methodology.

Once you’ve established comfort, using more sophisticated models such as a Tweedie generalized linear model (GLM) with your target variable being loss ratio or nonparameterized models such as a gradient boosting machine (GBM) or neural net could be even more effective, but with loss of interpretability.

A potential way to get the best of both worlds is with a layered GBM. While a traditional GBM can give you the factor importance from each variable, a layered GBM allows you to understand the gain for each interaction as well. This is appealing if you are using the GBM as the final model or to assist with discovering interactions as part of a GLM model.

Final thoughts

Even for the best models, there may be business considerations that trump use of the model as the data scientist intends. For example, if a model is built to reduce binding authority for agents with high projected loss ratios, it may be difficult to get buy-in from field executives and general agents if the producer has had a low loss ratio historically. While this may frustrate the modeler, they must understand their role is to add as much value as possible in a typically overlooked area for analytics, which can be rewarding in and of itself.

Ultimately, the best results will come from a combination of using advanced modeling techniques and collaborating with other areas of the company to ensure the right business insights are included in the final product.


Associate Director - Insurance Consulting and Technology
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