Are You Leaving Money on the Table by Ignoring Dynamic Pricing in Insurance?

Imagine a model where every insurance policy is personalized in real-time. Data about customer behavior, such as driving habits or frequency of vehicle use, allows insurers to instantly recalculate the policy’s cost. What once took months, now takes minutes. For example, telematics in auto insurance collects data on how carefully you drive, how often you switch lanes, and at what speed you drive. This data is analyzed by AI, which recalculates your insurance premium accordingly.

The integration of IoT and AI has been pivotal in implementing dynamic pricing. Sensors in vehicles, homes, and wearable devices collect extensive data, which AI analyzes to predict risks. For example, AI can assess accident probabilities based on driving habits or evaluate property damage risks using data from smart homes. These technologies enhance data processing speed and accuracy, facilitating more responsive and tailored insurance solutions.

Here’s what dynamic pricing looks like in practice:

• Auto insurance: Data from telematics devices in cars tracks driving habits — speed, frequency of lane changes, and braking patterns. Safer drivers enjoy lower premiums, while riskier drivers pay more.
• Home insurance: Smart sensors in homes predict risks like water leaks or fire hazards, letting insurers adjust policies to reflect real-time conditions.

Dynamic pricing allows insurers to offer personalized policies that evolve with the customer.

Dynamic pricing isn’t just a buzzword; it’s a game-changer. Take GEICO, for example. In Q1 2024, the company’s underwriting profits skyrocketed from $703 million in 2023 to $1.928 billion. This leap wasn’t luck — it was driven by advanced data analytics. Using AI, GEICO fine-tuned its ability to assess risks and optimize pricing in real-time.

Dynamic pricing vs. traditional pricing: A side-by-side look

Dynamic pricing’s key advantage lies in its precision and adaptability, but it also requires robust infrastructure and clean data to succeed.

• Accuracy. Policies are no longer based on average data. Every client receives an offer that reflects their actual risk.
• Speed. Automation allows decisions to be made instantly, saving time and resources.
• Flexibility. A policy's cost can change as the client’s behavior evolves, making insurance more adaptive.
• Risk reduction. Dynamic pricing helps insurers adjust quickly to market changes and reduces the risk of adverse selection.
• Personalization. By analyzing customer behavior, insurers can offer customized terms that meet individual needs.

We are not here just to get the best deal for ourselves, right? 

Today, customers can easily compare prices on insurance websites and choose the best offer. But what if the client doesn’t even know what’s best for them? 

With dynamic pricing insurers are able to offer fairer conditions based on behavior and risk level. For example, drivers who rarely use their cars will pay less, while those who follow all the rules on the road can receive discounts for safe driving.

High-risk customers may see higher prices, which helps insurers avoid losses. But the most important thing is that dynamic pricing encourages clients to change their behavior: drive more carefully, monitor their home with smart sensors, and, in turn, lower their insurance costs.

Who knows, maybe society will send us a thank-you card for that!

• Data collection and standardization. For AI models to work, a significant amount of data is needed, but this data is often disorganized.
• Data quality: Without clean, up-to-date data, AI models won’t be accurate.
• Infrastructure investments. A robust IT infrastructure is required to process and analyze large amounts of data in real-time. 
• Risk of bias: AI models can be biased if trained on incomplete data.
  Choosing the right partners: Reliable tech partners are essential for dynamic pricing systems.

• What assumptions will you make when working with data?
• How will you collect pricing data (team feedback, market research, integration with existing systems)?
• What price optimization strategy will you choose: revenue growth, profit maximization, or market share expansion?

Read more about it in a great piece our Solution Advisor Vlad Popovic wrote a while back.

Even industry disruptors like Tesla are embracing dynamic pricing. Tesla’s insurance uses data from its vehicles to create personalized, behavior-based premiums, proving that tech-driven pricing isn’t just a concept — it’s a competitive edge.

Another example is Amazon’s partnership with Marsh Insurance, which leverages AI to provide affordable policies for small businesses. These examples show how dynamic pricing is carving a path for profitability and innovation.

Dynamic pricing benefits everyone involved, enabling more accurate risk assessments, lowering policy costs, and increasing profitability. This is an entire architecture that can lead to even more improvements, optimizing products and the overall workflow of the insurance company.

The insurance sector, with its deep analytical roots, is more ready for this transformation than any other industry.