Claims Transformation

How machine learning can drive revenue for insurers

Claims Transformation | Georgina | 22 February 2021

There are multiple benefits to adopting machine learning within a business model. These include the ability to interrogate data and make fast decisions from the results. Additionally, machine learning provides insight to your customer base far quicker than manual processes could.

For insurers, the opportunities from machine learning can be huge.

Margins from aggregators can be minimal, so direct selling multiple products to a customer is far more beneficial. Not only for short term costs, but for longer term relationships with these customers. Upselling products to customers can be difficult without solid intelligence behind what to sell them. This is where machine learning comes in; and with it, recommendation engines.

What is a recommendation engine?

A recommendation engine uses machine learning patterns and algorithms to promote a relevant product or item to a potential customer. Not only does this act as a boost for sales and revenue, it also aims to provide the customer with a more tailored and personal user experience.

Consumers encounter recommendation engines all the time. Even in a manual sense – the snacks at the supermarket checkout are a physical example of recommendation engines. Amazon’s recommended products have increased its sales – and Netflix’s film and TV recommendations have ensured a tailor-made customer experience for its users.

The data collected from recommendation engines provides the business with valuable marketing intelligence. It can dictate what products or items are recommended the most, as well as identify gaps in the consumer market; providing space for improvement and innovation in the services/solutions provided.

How can machine learning improve customer experience for insurers?

Customer experience in the insurance world is becoming more important and competitive than ever. Machine learning can provide insurers with the upper hand in cross selling to their customer base while retaining loyalty. With so many insurance products on the market for an increasing number of purposes (e.g. niche add-ons, various types of excess, specific requirements for a policy such as home or contents insurance) – both the customer and insurer can benefit from relevant products bubbling up to the surface upon purchasing or renewing.

Upon renewal, a customer may find particular add-ons or changes they can make to their policy at the checkout. Not only does this empower the customer to make choices about what cover suits them best, it enables the insurer to be front and centre of its’ customers wants and needs.

How else does machine learning drive further revenue for insurers?

Many insurers use aggregators and comparison sites to sell policies. The margins from these services are incredibly small, so where possible, insurers aim to directly sell to the customer as much as possible. A bonus on top of this direct selling is to cross sell additional policies and products. The chances of cross-selling being successful is increased if the insurer has suitable intelligence via recommendation engines using machine learning.

Up-selling over the phone to customers does not work as well as it once did. Customers can feel pressured and are more likely to ditch the transaction altogether and go elsewhere. However, using machine learning to upsell will provide the customer with the empowerment to make their own choices. A more comfortable customer is arguably a more loyal one.

Machine learning algorithms should be adaptable and agile for a superior customer experience. Depending on the age and other demographics of the audience, the products recommended to them must change over time; otherwise the product will very soon become less innovative and more stagnated from a customer experience point of view. In order for machine learning to work efficiently within insurance, the insurer in question must have solid IT architecture in place to accommodate upgrades. This includes security measures to protect customer behaviour data as well as a roadmap of how machine learning will change within the business model over time.