Insurance dates back to prehistory (yes, really) and insurers have had millennia to hone their craft. Lloyd’s of London, for example, was founded in 1686 and ever since then, its insurance experts have been at the forefront of predicting losses and managing data.
However, there is only so much data a human can handle, and in the modern economy, valuable data is everywhere and it's increasing at an exponential rate.
Just as many customers now expect to be able to purchase personal insurance policies via smartphone apps, and even make claims this way, modern insurers must keep up with the digital revolution in order to stay in the race.
Nowhere is this lesson more valuable than on the subject of predictive analytics.
What is predictive analytics in insurance?
Predictive analytics is a branch of analytics concerned with making predictions on the risk and probabilities of future events. It's not surprising then that predictive analytics is a major aspect of insurance work.
Predictive analytics in insurance is about using a wide variety of methods, including data mining, predictive modelling, statistics, machine learning and AI in order to produce reliable reports which accurately identify levels of risk and aid in underwriting and policymaking.
Insurers have been utilising the basic principles of predictive analytics for decades, but today the field is much more complex than ever before.
Whereas in the past insurance brokers might have considered a few variables to alter a premium when pricing a policy, today predictive analytics means that potentially dozens of data points can be mined to inform pricing decisions and provide more personal policies to customers.
Likewise, predictive analytics can be put to use in claims management. When a claim is made, predictive analytics can flag up potential concerns and queries and accurately assess the validity of the claim being made. This makes the claims process simpler and more straightforward for both employees and customers.
Traditional insurance pricing methods
The traditional approach to pricing in insurance involves the use of pricing bands; consumers will be allocated to various bands depending upon a few simple metrics. In some cases, this might simply mean that the price of the product being insured alone is what determines the premium, whereas in others a few small variables such as claims history might be factored in too.
Obviously, traditional pricing is a blunt tool. Let's take the example of car insurance, where a policy's premium might be determined by a few details on the car and the driver and little else.
Two drivers of the same model of car might pay the same premium despite one driver having much safer driving habits than the other. The safer driver is effectively subsidising the other driver's insurance policy.
It's clear that 'one size fits all' pricing methods should be a thing of the past. Machine learning and predictive analytics can offer a more sophisticated alternative, but only if implemented correctly.
Pricing using predictive analytics
To be clear, machine learning is definitely an important aspect of predictive analytics in insurance and not one that most insurers should try to do without.
But trying to incorporate machine learning into existing pricing structures can make pricing less transparent for customers and more confusing for everyone.
Predictive analytics which take more variables under consideration will always result in more accurate risk assessments, but if these assessments can't be explained and justified to customers, it doesn't matter how accurate they are.
And for insurers who already have existing pricing structures, machine learning must take these into consideration. While not all machine learning pricing systems will do this, clever AI pricing can.
Let's take the example of car insurance again.
Insurers using machine learning enhanced AI pricing can offer safer drivers a lower premium despite their age and other factors that traditional models would give too much weight to. The customer is happy and motivated to drive safely on the basis of this premium and retention rates are therefore higher. These types of policies use telematics or ‘black boxes’ that are fitted to the policyholder’s vehicle and record a whole range of data about their driving style.
These enhanced predictive analytics are best implemented when they're adapted to work with an insurer's current pricing structure. This means premiums can be priced within existing pricing bands, with the new pricing engine simply doing the work of assessing risk and choosing the optimum price band for each particular customer.
This is exactly how Artificial's pricing software operates, allowing insurers to reap the benefits of AI without having to overhaul their entire system. We go into more detail in our two-part article: How can I use AI to improve my company’s pricing?
What are the benefits of predictive analytics in insurance?
Predictive analytics can improve efficiency, customer experience and profitability for brokers working in every branch of insurance, from commercial to health insurance and more.
One of the biggest benefits of this pricing system from a customer's viewpoint is that their premium can more accurately reflect their risk level. Clear guidance to the customer can also help them reduce their own premiums.
For example, health insurance could become cheaper for regular gym goers or car insurance reduced for careful drivers.
This approach builds trust and can help boost customer loyalty and lead to higher retention rates over time.
For insurers, machine learning pricing engines can also improve loss ratios and raise profitability. Over time, engines with in-built machine learning become better and better at assigning risk, and therefore better at pricing premiums accurately.
This leads to a reduced loss ratio, as those policyholders who are genuinely high risk must always pay high premiums to account for this.
Likewise, those customers who are in actual fact low risk will benefit from lower premiums and - you guessed it - retention rates for these customers will go up. It truly is a win-win.
If you're keen to learn more about intelligent AI pricing and the pitfalls of the traditional model, you can read our first blog on this subject: How can I use AI to improve my company’s pricing?
Our intelligent insurance software, artificialOS, uses AI and machine learning to price quotes quickly and accurately.
If you’d like to arrange a call to find out how we can help your business implement AI and machine learning, then get in touch.