What are the challenges that traditional insurance pricing models are facing?

Price and feature comparison websites: There is no bigger threat to existing pricing models than websites that are aiding customers to compare policies by price, value, and benefits. It is no surprise then that consumers choose the lowest offer. In fact, the drill-down benefits offered by these sites make evaluating hundreds of insurance products a piece of cake. This is because these websites are using disruptive technologies like AI to provide answers in a matter of seconds. Consumers demanding personalization: Customers are open to new pricing models based on personalization. The IBM Institute for Business Value (IBV) in their study, revealed that customers are more responsive to tailor-made products. The problem here is that the traditional pricing models were built for groups and not for individuals. Making this change will require not only a change in processes but also the implementation of advanced technologies and the breaking down of data silos. New insurance entrants: These digitally powered insurance start-ups have no legacy issues to deal with. They come offering products that are built with advanced technology. Their dynamic pricing is an inherent part of their offering and is grabbing the attention of Gen Z and many millennials as well. Gen Z will one day be the biggest slice in the customer pie. The right time to grab their attention is now.

What are the steps towards implementing AI in insurance pricing?

Investment in a data infrastructure that has the ability to integrate internal data and external data sources. The effectiveness of AI is dependent on it being able to harness all relevant data. Data sources could be from automation platforms, CRMs, content management platforms, financial data and more. All data will need to be cleansed and only then will predictions based on these large data sets be accurate. There must also be a year on year evaluation of new data sources to continuously add on new features to pricing models. Once the data infrastructure is in place, the next phase is investing in self-learning algorithms. Finding the right AI-based pricing models depends on preset objectives. Models can range from simple matrix models to complex simulation-based models. Every quote will generate important data points, even if the quote does not conclude positively. These data points must be fed back into the algorithms so that self-learning models increase accuracy over time.

Dynamic Pricing in Insurance Using AI and Predictive Analytics

Turn on a football game and there are at least 5 insurance companies trying to get their products to be ‘top of the mind’ for the same customer. For instance, Progressive spent $1.9 billion on advertising in 2020, Geico spent an estimated $2.26 billion. Post pandemic, insurance carriers are reassessing their marketing budgets and how they communicate with customers. Priorities have shifted towards accelerated underwriting and faster processing of claims in making the product distinction. It is also more important to be able to predict customer behavior and their intent and personalize marketing outreach.

Budgets are being evaluated differently to embrace a future that will see smart marketing as well as dynamic pricing that meets market realities, both supported by implementing a powerful AI strategy.

Let’s keep the focus here on artificial intelligence-powered dynamic pricing and the advantages it brings. Benefits are many such as cutting short from months to mere weeks, the introduction of new policies to market. There is more.

Also read: 5 Myths of Insurance Technology Modernization That Could Trip You Up

The traditional method of premium pricing

Traditionally, insurance premiums are set using a cost-plus model. Every insurer knows what it involves but let's still briefly touch upon it. The cost-plus model is an actuarial assessment of the risk premium and adding on a percentage to cover direct and indirect costs and including a small profit margin.

In property and casualty insurance, particularly in the auto and home insurance sector, this cost-based pricing model still is common. However, times are changing and traditional pricing has a few drawbacks that make it a hurdle towards future-readiness. The challenges that traditional pricing models are facing are:

Price and feature comparison websites: There is no bigger threat to existing pricing models than websites that are aiding customers to compare policies by price, value, and benefits. It is no surprise then that consumers choose the lowest offer. In fact, the drill-down benefits offered by these sites make evaluating hundreds of insurance products a piece of cake. This is because these websites are using disruptive technologies like AI to provide answers in a matter of seconds.
Consumers demanding personalization: Customers are open to new pricing models based on personalization. The IBM Institute for Business Value (IBV) in their study, revealed that customers are more responsive to tailor-made products. The problem here is that the traditional pricing models were built for groups and not for individuals. Making this change will require not only a change in processes but also the implementation of advanced technologies and the breaking down of data silos.
New insurance entrants: These digitally powered insurance start-ups have no legacy issues to deal with. They come offering products that are built with advanced technology. Their dynamic pricing is an inherent part of their offering and is grabbing the attention of Gen Z and many millennials as well. Gen Z will one day be the biggest slice in the customer pie. The right time to grab their attention is now.

Dynamic pricing in insurance is creating policies that are cheaper for low-risk customers. High-risk policyholders have a different premium model that is again divided based on various factors and user behaviors. For instance, infrequent drivers will pay lower auto insurance, while those driving more frequently on highways will pay a higher premium. Within the latter group, premiums can again differ based on their driving behavior(e.g. how many times do drivers shift lanes) and speed limits adhered to. These are only a few potential factors, there are actually hundreds of signals that can go into dynamic pricing.

If insurance carriers continue to use a limited set of risk differentiators, they will find that the majority of their clients will fall into the riskier and hence less profitable group. The younger group of digitally advanced clientele would have moved to carriers who dovetail their policy plans with smart pricing that is designed for rapid deployment.

Pricing Automation in insurance

Also read: 6 Ways Machine Learning and AI are Transforming the Insurance Industry

The 3 steps towards implementing AI in insurance pricing

AI has been shown to bring the offering price closer to a client’s willingness to pay, generally leading to premium increases of 2 percent to 3 percent. Source: IBM

There are many insurers experimenting with AI models for defining premiums. Auto insurers are leading this transformation and are using IoT technology to come up with innovative plans. Root in the United States is collecting data on user behavior to lower or increase premiums over time. The British insurer, ByMiles, offers its policyholders a premium based on miles used, where infrequent drivers pay a lower premium. Back again to the United States, Lemonade and Hippo in the home insurance space, use machine learning to provide policy rates online in just 60 seconds. Their claims process is also fully online.

There are 3 steps that insurers need to factor in when considering implementing an automated pricing model.

Investment in a data infrastructure that has the ability to integrate internal data and external data sources. The effectiveness of AI is dependant on it being able to harness all relevant data. Data sources could be from automation platforms, CRMs, content management platforms, financial data and more. All data will need to be cleansed and only then will predictions based on these large data sets be accurate. There must also be a year on year evaluation of new data sources to continuously add on new features to pricing models.
Once the data infrastructure is in place, the next phase is investing in self-learning algorithms. Finding the right AI-based pricing models depends on preset objectives. Models can range from simple matrix models to complex simulation-based models.
Every quote will generate important data points, even if the quote does not conclude positively. These data points must be fed back into the algorithms so that self-learning models increase accuracy over time.

Finally, AI implementation will require evaluating experienced technology partners. Big data, the Internet of Things, and predictive analytical tools are providing the capability of usage-based pricing. Tesla might be the first big tech company venturing into insurance, it certainly won’t be the last. Amazon has entered into a partnership with Marsh Insurance to provide small businesses with more affordable product liability coverage. To keep ahead, insurers must increase usage of predictive algorithms to optimize products, find new markets, and finally showcase the right insurance products to the relevant customers.

Topics: A.I. in Insurance