The influence of AI on insurance pricing models

The insurance industry has been resistant to change for centuries, but that’s about to change. Like all other sectors, the insurance industry is undergoing a digital transformation, with most, if not all, insurance companies making significant progress in AI implementation. As insurance companies leverage AI capabilities like machine learning, data modeling, and predictive analysis throughout their value chain, AI is poised to become one of the biggest game-changers for the insurance industry over the next decade.

With many players in the insurance industry enjoying various applications of AI like risk management, fraud detection, and personalized offerings, we’re bound to see a significant shift in pricing and service offerings in the near future.

This article will explore the impact of AI on insurance pricing and the industry as a whole. It will also dive into the intricacies of implementing AI-based insurance pricing models and the future of AI in insurance pricing.

What is artificial intelligence?

AI can simply be defined as the concept that a computer can learn, think, and behave like a human. From a business perspective, it can be used to conduct operations faster and more accurately. By automating labor-intensive processes, AI can lower production costs and save time. It can also be used to understand customers better, which comes in handy in insurance as companies need to predict customer behavior, optimize price and product offerings and understand customer preferences.

AI is comprised of several related technologies, which include:

• Machine learning: Machine learning typically involves identifying patterns in a dataset, then predicting an outcome. Insurance companies can use machine learning capabilities to develop quantitative trading strategies.
• Neural networks: These are algorithms designed to mimic the human mind and recognize patterns in data. Neural networks can identify, classify, analyze, and find patterns in complex datasets.
• Deep learning: This is a machine learning application whereby models can analyze and draw meaningful conclusions from data. They can also solve problems without being trained or given explicit frameworks or instructions. Deep learning models typically learn by themselves.
• Natural language processing: These technologies help computers understand, interpret, and respond to queries or commands through text or speech. Insurance companies leverage natural language processing in chatbots to streamline customer service.

Artificial Intelligence in the insurance industry

Although it may sound like a futuristic concept, the uses of AI are already ingrained in our everyday lives. For instance, whenever you’re on your favorite streaming service, you’ll notice that the recommendations you get are based on your personal preferences – that’s artificial intelligence at work. AI is also used in other industries, including financial services, health, education, etc.

Over the past few years, several players in the insurance industry have started to implement AI and AI-related technologies into their business processes. However, insurance companies have been pretty slow to implement AI capabilities.

In fact, most insurance companies started to expand their AI capabilities after the onset of Covid-19, which led to an increase in call volumes, medical emergencies, business interactions, and death claims. These disruptions proved that AI might be instrumental in improving customer engagement, supporting distribution, settling claims faster, and detecting fraud.

What role does AI play in insurance pricing models?

A recent forecast from a report by PwC predicts that the initial impact of AI in the insurance industry will be primarily related to automating underwriting and claims processes as well as improving general business efficiency [1].

As a multi-faceted sector, the insurance industry is bound to experience more far-reaching impacts over time, especially in identifying, assessing, and underwriting emerging risks and new revenue sources – which may lead to more product offerings.

Before we get to the future of AI on insurance pricing strategy and its impact on the industry, let’s first take a look at the traditional method of insurance premium pricing.

Traditional methods of insurance premium pricing

Not so long ago, insurance premiums were set using a cost-plus model. The cost-plus model is an actuarial assessment of the risk premium with an added percentage to cover direct and indirect insurance costs, including a profit margin.

This cost-based insurance pricing model is still common in casualty and property insurance, particularly in the auto and home insurance sectors. However, numerous insurance companies have been alienated from this model, primarily due to its drawbacks, especially regarding future-readiness, cost, and customer satisfaction.

Some of the most common challenges associated with the traditional insurance premium pricing model include:

• Consumer demand for personalized services: 66% of customers around the US say that coming across content that isn’t personalized would likely make them stop making a purchase [2]. This sentiment also applies to insurance premiums, where customers are generally more responsive to tailor-made products [3]. One of the most significant challenges presented by traditional insurance pricing models is that they were built for groups, not individuals. Transitioning into more personalized services will require not only a change in the process but also the breaking down of data silos and the implementation of advanced technologies like artificial intelligence and machine learning.
• Price and feature comparison websites: One of the biggest threats to traditional insurance pricing models is websites that aid customers in comparing insurance policies by price, value, and benefits. With all this information at hand, it’s no surprise that customers choose the lowest offer with the highest benefits. This means that any insurance company still relying on traditional insurance pricing models could lose a lot of business.
• New insurance entrants: New insurance startups have no legacy issues to deal with. They are also offering products powered by advanced technology, which enables them to provide dynamic insurance pricing strategy and tailor-made services. These digitally powered companies have already gotten the attention of generation Z and many millennials. And since generation Z will soon make the largest percentage of insurance customers, there has never been a better time to grab their attention.

Dynamic pricing strategy powered by AI is creating cheaper policies for low-risk customers. And although high-risk policyholders have different premium insurance pricing models, they are still better off since their premiums are calculated based on various factors and user behaviors.

For instance, less-frequent drivers will pay lower auto insurance than those who drive more frequently. Additionally, auto insurance premiums for frequent drivers can again differ based on factors like driving behavior and adherence to speed limits, among others.

The future of AI in the insurance industry

The World Economic Forum predicts that upwards of 42 billion IoT-connected devices like smartphones, cars, home assistants, smartwatches, and fitness trackers will be in use by 2025 globally [4]. The growing volume of consumer data from IoT-connected devices can help AI assess customers’ risk profiles by combining the data with lab testing, claims data, biometric testing, and health data.

While deep learning models can help insurance companies evaluate their customers’ risk profiles and offer personalized services at optimal prices. Eventually, riskier customers will have to pay more, and less risky customers will enjoy discounted pricing. This will ultimately result in higher profitability and might even expand the market.

How can AI help insurers increase their profitability?

The current market is barred with numerous risks and exceedingly low-interest rates, prompting insurance companies to focus on technical results to improve profitability. As such, there is a greater need for a better risk evaluation and insurance pricing strategy process.

Insurance companies can achieve these goals through the vast amount of structured and unstructured data that is now available to them. When this data is fed into machine learning algorithms and processed through powerful cloud computing, underwriting and pricing strategy will reach a whole new level and ultimately improve profitability while reducing risk.

Factors to consider when implementing an automated pricing model

Numerous insurers are already experimenting with AI models for defining premiums. At the forefront of this trend are auto insurers using IoT technology to innovate their product and service offerings. That said, there are a few crucial steps insurers need to factor in when implementing an automated pricing model. They include:

Investing in a versatile data infrastructure

The effectiveness of AI is entirely dependent on its ability to harness all relevant data. Therefore, insurance companies need to invest in a data infrastructure that can integrate internal and external data sources.

Data sources could be in the form of CRMs, automation platforms, financial data, content management platforms, and more. In addition to collecting and integrating data, it must also be cleaned in order to make accurate predictions based on these large datasets.

Investing in self-learning algorithms

Once you have a data infrastructure in place, the next step is investing in self-learning algorithms. Unfortunately, there is no one-size-fits-all approach to finding the right AI-based pricing model. Instead, insurers have to choose pricing models based on preset objectives. These models can range from simple matrix models to complex simulation-based models.

Increasing the accuracy of the self-learning models

Every quote generates important data points, even if it concludes negatively. In order to improve the accuracy of the self-learning models, insurers have to create an infrastructure that enables the data to be fed back to the self-learning models.

Other applications of AI in the insurance industry

Fraud detection

Insurance companies lose up to 40 billion a year to fraudulent claims [5]. Additionally, 30% of customers have admitted to lying to their car insurer at least once to gain coverage [6]. AI-powered text analysis and predictive analytics tools might detect fraudulent claims based on data captured from the claimant’s story in accordance with business rules. Insurers can also benefit from voice analytics to determine whether a customer is lying when submitting a claim.

Personalized services

According to a recent study by Accenture, 80% of insurance customers want a more personalized experience and are even willing to disclose their personal data to get it [7]. AI can enable insurance companies to understand their customers better and offer personalized and customized products that will allow their customers to only pay for the coverage they need.

Application processing and insurance underwriting

Application processing typically involves extracting information from a huge pile of documents. This process is often tedious, time-consuming, and prone to errors when done manually. However, with AI-powered document capture technologies, insurance companies can automatically extract relevant data from application documents and expedite the application process. The result is a faster application and underwriting process, which ultimately leads to improved customer satisfaction.

Appeals processing

By 2025, up to 50% of insurance claims will be automated, primarily thanks to AI and machine learning technologies [8]. After processing, some claims may result in appeals, which can be automated through AI technologies such as RPA and OCR [9].

The future of AI in the insurance industry: Insurance pricing strategy

As mentioned earlier, artificial intelligence has the potential to transform the insurance industry. For starters, customer experience will be transformed from a frustrating and bureaucratic nature to a faster, more affordable, and on-demand service.

Ultimately, the tailor-made products and reduced prices will attract more customers. In time, as insurers apply AI-driven technology to the vast amounts of data at their disposal, we will start to see more flexible insurance services such as premiums that automatically adjust in response to customer health and accidents as well as on-demand pay-as-you-go insurance.

Soon, insurance will become more personalized since insurers using AI-based technology can better understand their customers’ needs. Additionally, insurance companies will realize cost-saving benefits due to faster workflows and a reduced dependency on human resources. They will also realize new revenue streams as data-driven analytics open up new business and cross-selling opportunities.

AI solutions will also make it easier for customers to interact with insurance companies, which might increase the likelihood of more people purchasing insurance.

Final thoughts on the future of AI in the insurance industry

As the world becomes more data-driven, all industries have adopted different forms of automation in an effort to improve their processes, improve profitability, and enhance customer satisfaction and retention.

As one of the oldest industries in the world, it’s no wonder the insurance industry has jumped onto the AI bandwagon, particularly around pricing strategy and service offering. As more insurance companies adopt automated services, the insurance sector will see a major shift to more affordable and personalized services.

If you want to benefit from AI in your pricing models or software, start by discovering our AI consulting services.