Real-time, Dynamic Pricing in Insurance

Today, insurers use algorithms to generate price quotes for their customers within minutes based on more parameters than ever before. Furthermore, companies are testing real-time, dynamic pricing of insurance where the price of your policy changes hour-to-hour, day-to-day, or month-to-month. In this article, we’ll look at this growing dynamic pricing trend, its pros and cons, and the implications for the insurance industry as a whole.

What is dynamic pricing?

Generally, dynamic pricing is a system whereby the price of a good or service changes based on the timing and context of the sale. In insurance, this means that policies are cheaper for lower risk customers and more expensive for higher risk customers, based on a wide variety of potential factors.

When we consider making dynamic pricing happen in real time, we mean updating the price of a policy regularly based on customer behaviour. For example, a car driving on the motorway is a higher insurance risk than a car safely parked in a garage. The driver of the first car should pay a higher premium for the time on the road. Moreover, the infrequent driver should pay a lower premium because they drive less.

This type of real-time dynamic pricing has only recently become possible thanks to technological advancements in big data and artificial intelligence. Pricing algorithms can now consume vast amounts of data about customers and make intuitive pricing calculations that insurers can reliably trust. These models not only accurately forecast the true risk, but they can also take into account the competitive landscape for similar policies and the policyholder’s propensity to hold, buy, make a claim, or need customer support.

Overall, the dynamic pricing algorithm works in two directions. First, inside-out pricing asks at what price can the insurer offer the policy and still make a profit, considering risk, operating costs, and margins. Second, outside-in pricing looks at the marketplace for what competitors are offering that the product would need to match or exceed to be successful. 

Dynamic pricing’s potential impact

Dynamic pricing is poised to have an enormous impact on the insurance industry because speed and agility now become key in an industry not known for those characteristics. The strongest competitors in the market today can generate a quote for a policy in 60 seconds, update policy terms or pricing via email or an app, and respond to claims online within a matter of hours.

A key part of this shift is software and infrastructure. Well designed data pipelines and algorithms can consume data instantly. In the best cases, the algorithms can autonomously make updates to pricing, or they immediately notify a decision maker at the insurance company who can review the changes before they go live. This quick response to changing risk profiles allows modern insurers to be flexible and stay profitable in the face of fast-changing scenarios.

Moreover, price is becoming increasingly important in insurance industry. Attracting new customers depends on the rates you offer and level of service you can provide. Both of those are affected positively by dynamic pricing and modern infrastructure in your organization.

Benefits & potential drawbacks

Interestingly, dynamic pricing is common in some industries and absent in others. For instance, when we dine in a restaurant we expect to pay the same price as anyone else ordering the same courses. However, if we book a plane ticket we accept that ticket prices change with the hour. The person next to us probably paid a different fare than we did.

As it stands now, insurance pricing has long been veiled in secrecy. As a consumer, you’re offered a rate without much explanation where that rate comes from. Additionally, for the most part, that rate is the same as other customers like you. Dynamic pricing could make this process more transparent. Insurers could specify what aspects of your profile contributed to the quote you were offered.

Moreover, your rate can be more closely tailored to your actual risk level. For example, on most insurance providers, safe drivers subsidize reckless ones. Indeed, for almost any type of policy, half of the policyholders are paying too much while the other half are paying too little. As we use algorithms to personalize rates, we can even those scales, offering better rates to lower risk individuals.

However, for insurers a potential pitfall is competing on price. Dynamic pricing plays into commoditization of insurance. If implemented poorly, it seems insurers are solely competing on price instead of emphasizing coverage levels and customer service.

Ethical considerations

Another major drawback of dynamic pricing is its reputation as a vehicle for price discrimination. Indeed, pricing algorithms that are based on information about individuals can show biases against certain groups. Insurers will need to take great care to make sure their dynamic pricing schemes don’t fall into those biases.

The key thing to consider is how your machine learning algorithm is constructed and what data it is trained on. Limit the parameters you use in your pricing model to non-demographic data – opting only for operational data that’s anonymous. Furthermore, you’ll need a rigorous analysis of your training dataset and where it comes from in order to identify potential biases. Finally, consider a manual review of all policy and pricing changes resulting from the algorithm’s calculations. These algorithms can and do make mistakes.

On this front, regulatory scrutiny also plays a role in checking bias. Ultimately, regulatory bodies are expected to work in the public’s best interest. Good regulation on price discrimination and model biases in insurance is needed. However, regulation also impedes the speed with which insurers can change pricing strategies or dynamic rating schemes when they require regulatory approval.

At its worst, dynamic pricing could result in insurers maximizing profits at the expense of consumers. Using algorithms to make as much money as possible from each transaction. This shouldn’t be the goal of dynamic pricing. Instead, think of it as a way to provide a more fairly-priced, economical, and easier to use insurance experience for customers. Maximizing profits in the short term leads to a whole host of lost opportunities for customer loyalty in the long term.

Companies using dynamic pricing

There are a number of companies experimenting with dynamic pricing. Perhaps farthest along are the insurers in the automobile insurance industry. There, startups are leading the charge toward real-time pricing. British company ByMiles offers a dynamic, pay-by-mile solution where the less you drive, the less you pay. In the States, Root has a similar model, except they also use data on your driving behaviour to see if you’re a risky or safe driver over time.

In the domain of home and renter’s insurance, startups like Lemonade and Hippo are known for their ability to offer you a quote in 60 seconds. Their claims processes are also fully online and integrated. Lemonade and Hippo both use machine learning to power their pricing strategies. In addition, Hippo is exploring ways to use IoT sensors in the home to reduce rates for homes with lower risks.

Other types of insurance are also exploring dynamic pricing. Perhaps the largest explosion comes in the on-demand insurance field. Companies like Trov and Bought By Many allow you to insure your items, pets, trips, and more alongside your peers using on-the-spot pricing algorithms. These types of specialized insurance offerings simply weren’t an option from large insurers in the past, but now they’re a possibility thanks to machine learning powered pricing.

Dynamic pricing of insurance in real time

Dynamic pricing will have a major impact on the insurance industry as a whole. While there are ethical concerns that must be addressed, the trend toward dynamic pricing could be a win-win for insurers and customers. On one hand, customers get policies tailored to their needs. On the other hand, insurers can more efficiently evaluate risk and generate profitable pricing strategies.

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