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While insurtech has progressed in leaps and bounds over the past years, 2020 has perhaps been the catalyst to help the insurance sector fully embrace digital transformation. Machine learning is one technology which is destined to change insurance forever: with machine learning, insurers can reduce their workload, improve accuracy, and add value to customers in an increasingly competitive market.
But before adopting machine learning for insurance, it's important to understand what it is, and how it's best used.
What is machine learning?
Machine learning is an application of artificial intelligence (AI). AI can be applied in insurance so that computers can analyse large swathes of data - known as big data - to automate parts of underwriting and claims processes and free up insurers' time for more complex tasks.
Today's AI is now capable of machine learning, which describes how AI software and programs can learn and improve over time. This is because much of today's AI is programmed with algorithms which build mathematical models from 'training data', which can allow the system to make predictions using new data.
To illustrate this, let's take an example of AI software which is analysing customer data for the purpose of assessing risk.
The software may start out looking for a few simple data points: previous claims, age, etc. But as time goes on and the software analyses more data, machine learning means it can begin to identify patterns and trends which predict risks that were previously missed, and flag these up too.
In this way, machine learning means that not only can you trust your AI software to complete tasks for you, but you can also expect the software to become more accurate over time, spotting risk factors that human underwriters aren't even aware of.
How is machine learning applied in insurance?
Machine learning can be applied to insurance in countless ways. We've written before about how machine learning is transforming the insurance industry, but let's take a look now at some of these applications.
Machine learning and AI are both responsible for insurers now being able to make use of all of their data. We've been capable of gathering and storing large amounts of data for years, but as humans, we just can't make sense of it ourselves. Machine learning means that all of these numbers can be analysed quickly and precisely, informing both risk assessment and premium pricing strategies.
Claims can also now be processed at least partly by automated AI software. During all points of the process, claims must constantly be checked to ensure they meet policy standards and fulfil various criteria. This isn't difficult work, but it is time-consuming, and by outsourcing it to machine learning software, insurers can spend more time on trickier aspects of claims.
Machine learning can also be applied in customer service, through the use of live chatbots. AI chatbots can be employed to review claims, check policy details, detect fraud, and of course, answer customer queries.
Chatbots respond to customer inquiries quickly and efficiently, without the need to wait in long phone queues, easing the workload for insurers and improving the customer's experience at the same time.
Even the marketing department can make use of machine learning, which can be used to assess the most effective marketing tactics for any given audience. Using machine learning to predict customer preference, new products can be developed to meet popular demand and the right products can be marketed to the right people.
These broad examples are really just the tip of the iceberg. You can read in more detail about how machine learning can be used in underwriting, as well more about the relationship between machine learning and big data.
The future of machine learning for insurance
Machine learning is going to be a crucial aspect of future insurance practices. Insurance, on the whole, is gradually becoming more individualised, with younger customers expecting insurance policies to be tailored to their own individual risk levels.
Evidence that the tide is changing can be found in public opinion. In a recent survey, 64% of people said they would rather everyone pay for insurance which accurately reflects their own level of risk, compared to the current system where applicants are allocated a risk level approximately and many customers will be paying over or under the odds compared to their exact risk levels.
Telematics and IoT devices are a great example of this, and something that we'll be hearing more about in the insurance industry over the next few years. IoT, short for internet of things, includes all kinds of ordinary household appliances and devices which aren't traditionally connected to the internet, such as smart speakers and smart fitness trackers.
Telematics devices are installed in cars, and they collect data which is relevant to motor insurers: how often the car is being driven, when it's being driven, and how. This data can replace outdated metrics like age and gender, which were once used to make sweeping generalisations about an applicant's level of risk.
Adopting machine learning for insurance isn't just about making your brokerage more efficient, boosting the accuracy of risk assessments or improving customer service, although it can do all of those things. Instead, it's better to think of machine learning as one of many new technologies which will soon become a requirement for insurance providers wishing to offer the kinds of contemporary insurance packages which younger customers both desire and expect.
If you’d like to know more but don’t know where to start, download our new whitepaper, AI buyer’s guide: How to choose an AI product for your insurance business.