Machine Learning in Insurtech

Black Friday: Optimizing Prices for Insurtech

Find out how to optimize prices for your insurtech using machine learning technology and prepare for Black Friday and Cyber Monday.

Do you hear this? The clock’s ticking. It denotes the countdown to Black Friday, the most favorite American tradition that also marks the beginning of the prolonged shopping season.

While in retail and entertainment sectors, Black Friday and Cyber Monday have rooted deeply, more conservative industries such as insurance are more cautious and delay catching Black Friday fever. Hesitations of insurance and insurtech organizations are yet understandable, provided high stakes in the industry. If a car insurance company suddenly gives large discounts, it risks attracting high-risk drivers and suffering great losses at the end of the day.

But this year things can go differently. Implementation of the Machine Learning (ML) model allows you to optimize prices and have promotions for future Black Fridays, without taking risks to run a losing business. With ML-based pricing, an insurance organization can fine-tune its pricing model and offer a client a discounted rate. Let’s say it could be 20% off for the first three months of health insurance on the condition that the client buys a yearly plan. This way, your insurtech will engage more clients along with setting itself apart from more conservative competitors.

So, how can Machine Learning help your insurance company improve the pricing model and be in a more winning position on Black Friday?

Traditional vs machine-based pricing models in insurtech

After a client applies for a policy, an insurtech delivers a customized premium that is based on:

• The client’s risk
• The risk that they will file claims
• The expected cost of these claims during the policy coverage

To estimate this risk, pricing actuaries use predictive models. The key feature of traditional insurance pricing models is their simplicity. In this case, the pricing team uses historical data along with policy attributes and information about the client. The idea is to categorize the risk in a simple matrix to alter the premium based on several chosen variables.

But here is the problem with the conventional approach to pricing:

1. No one guarantees the variables used, as well as the chosen pricing formula, are linked to the actual risk i.e. the level of claim.
2. Insurance companies can lack critical data to assess the risk. Besides, they usually rely on a limited number of criteria such as age, gender, or marital status. Together, this could affect the accuracy of the results.
3. Inaccurate pricing is costly for insurance businesses, leading to uncontrollably high exposure and loss ratio. If you plan to provide discounts for Black Friday, you risk losing money even more.

Now let’s review what happens if you choose machine-based pricing models for prediction. With no exaggeration, Machine Learning is a game-changing technology for price optimization since it solves many challenges that modern insurers face.

First of all, ML algorithms are designed to analyze large amounts of data and consider more variables as compared to traditional pricing models. This means that ML models can make pricing more accurate for insurers, giving clients fairer premiums.

Let’s take a motor insurance example. If a traditional pricing model is used, an insurance company will likely offer higher premiums to younger drivers, regardless of their experience and how safely they drive. It’s simply because of the age factor. In contrast, an insurer using an ML-powered pricing model will pay attention to other important variables such as the driver’s experience and skillfulness. The system will notice the lower risk in this scenario, so the company will be able to give a more generous offer to the younger driver.

Secondly, you can implement an ML solution that is designed to never stop learning from the results. Following this logic, ML algorithms will see patterns in claim info and link it back to the client or policy attributes. And this will make pricing even more accurate, leading to higher customer satisfaction, improved loyalty, and better retention rates (rarely seen in the insurance industry).

Another example we’ll review is car insurance. A critical factor here is the driver’s location as studies reveal that more accidents happen in densely populated areas with much congestion. This leads to more claims in these areas, which means that clients from these locations get unfairly high premiums. Meanwhile, insurtechs and larger insurance companies lose potential clients without reasonable excuse. Again, a tailored ML algorithm could improve the insurer’s risk sensitivity and allow the company to charge skillful drivers more fairly, regardless of where they live.

So what’s the value of Machine Learning for price optimization?

• Number and nature of variables: ML considers different factors categorizing the risk in insurance pricing. Plus, it sees the hidden patterns behind variables.
• Numerous sources and channels: Unlike machines, humans are limited in the number of sources they can analyze when defining premiums. ML can take data from the most unexpected sources such as previous claims or even social networking sites if someone directly programs it to do it. If you engage an experienced ML team as early as at the stage of data collection and preparation, it can help bring the most value from your undertaking.
• High accuracy: As said, ML-powered pricing is highly accurate, which makes it so attractive to use for price optimization.
• Flexibility: With ML, insurance companies can adjust pricing dynamically. This means you can adapt to market competition better, including participation in Black Friday and Cyber Monday activities.

Steps to achieve optimized pricing

Imagine a small insurtech company that wants to increase its sales on the occasion of Black Friday. The company knows about high sensitivity risks to provide discounts in the insurance industry, so it decides to optimize prices, but with the help of a Machine Learning solution.

Let’s see the steps the company should take to implement technology successfully.

1. Gather input data

The research proves that most insurance companies process only 10 to 15% of data they have access to, failing to unlock the value from the info available to them. But data is the key to success if you plan to implement an ML-based pricing model.

So, a starting point for the insurtech from the example above is to make sure it has enough claim and policy data to feed into its pricing engine. Next to the volume, the quality of data also matters — data should be complete, balanced, and as detailed as possible.

For more accurate results, the company should consider using different sources:

• More standard ones like data from traditional claims and policy management systems
• Less trivial ones such as surveys, third-party marketing databases, and social networking sites

When data is captured, the insurer should leave time to convert info into a suitable format for a pricing model to consume it. Moreover, it’s a good idea to seek patterns in data. For instance, a correlation exists between the driver's altitude and more claims in motor insurance. If a driver is used to going skiing for weekends, they’re more likely to claim than the driver with less change in altitude. With the help of the machine, the insurance company can combine this data with weather conditions and see the underlying risk more clearly. But it’s hardly possible to notice this pattern without painstaking research if the analysis is done by people.

2. Define goals and constraints

The next step is to figure out the goals and limits of the insurtech. For sure, price optimization is usually completed for profit maximization, but the insurer may also be interested in boosting customer loyalty or attracting new segments.

Constraints may also differ: they could be legal or relate to the company’s reputation. For instance, many car insurance companies beware of racial biases as the research shows that clients living in Black neighborhoods usually pay 30% more than those who live in white communities.

The insurance company will need these goals and limits when it shifts to defining parameters to shape its pricing model.

3. Modeling and training

In this step, our insurance company should choose the most optimal algorithm for price optimization: will it be Deep Learning methods, Reinforcement Learning approach, or something else?

After that, the individual pricing model is built and fed in with the training data. So it was successful, this ML model goes through lots of iterations, testing assumptions, and fine-tuning the prediction mechanisms.

4. Execute and adjust prices

Last but not least, when our insurtech obtains the price from the model, it has to test it, collect data, and repeat the process as many times as needed. It’s better not to forget to optimize prices regularly to get the most benefit from the ML model.

But wait a minute…

In theory, if an insurtech company adds more variables and assesses the risk thoroughly, it should come up with a more effective pricing model, shouldn’t it? In reality, a Machine Learning project is a big step forward, and switching to an ML-based pricing model may cause lots of difficulties unless done properly.

Here are a couple of problems that insurers can face when adopting ML for price optimization:

• Right data sources: As said, quality of data is as important as quantity in Machine Learning, and many insurance companies struggle to provide relevant and representative data.
• Less transparency: The more parameters the insurtech adds to its ML pricing model, the less transparent this model becomes to the company itself as well as its clients.
• Data security: Collecting a big amount of data is a large responsibility and requires understanding the value of this data. Insurtechs should spend much attention on how this data is stored and how access is managed if these companies want to save themselves from extra security risks.

If the insurance company wants to minimize risks like these, it’s better to think about a partnership with a professional ML team, which will be able to accompany the insurer through its entire Machine Learning journey.

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Final thoughts

Black Friday and Cyber Monday is a wonderful time in the B2C sector that can bring lots of new clients through your doors. The holiday is less popular in insurance though, simply because of the higher risks of reducing prices in the industry.

By optimizing prices through Machine Learning, this great Black Friday opportunity won’t slip through the insurers’ hands. What’s more, insurance companies will be able to reduce the loss ratio as well as provide better pricing for their clients, which will improve their loyalty too.

If you’re interested in implementing an ML-based project successfully, don’t hesitate to contact us. ML experts from Intelliarts AI’s team are ready to help you.