The aim of machine learning in insurance industry is to obtain greater profitability through decisions backed by the analysis of millions of data and the prediction of future behaviour with a system that learns and improves every time it comes into operation.
Machine learning for insurance has become an essential ally in the industry’s digital transformation process. This discipline of Artificial Intelligence has revolutionised the processes of insurance companies because, thanks to an algorithm, it is much easier to improve productivity and adopt proactive strategies in customer acquisition and loyalty.
What is machine learning?
Machine learning is a discipline of Artificial Intelligence that makes it possible to create an algorithm capable of learning automatically. In this sense, learning means recognising complex patterns among millions of data to make predictions about future behaviour. Moreover, when we say it is automatic, we mean that these systems can improve their predictions over time and without the need for human intervention.
Applications in everyday life
Machine learning in insurance draws on other examples from everyday life. This discipline is, for example, responsible for smart assistants such as Alexa or Siri becoming more and more accurate. It also enables the existence of personalised lists of suggestions based on previous consumption, such as those offered by apps like Netflix or Spotify. Even newspapers like the New York Times use machine learning to understand their readers’ behaviour better.
Machine learning is also used to improve search engines, robotics, medical diagnostics and credit card fraud detection, making it a strategic ally for many companies. Machine learning and insurance were, therefore, destined to meet.
Applications of machine learning in insurance industry
A large part of the business of insurance companies consists of comparing data and making estimates in order to obtain greater profitability from the technical note. Machine learning for insurance enables efficient management of the large amount of information handled about customers, the use of insured objects and processes. It is not surprising that in its unstoppable commitment to innovation, the insurance industry is adopting machine learning to improve claims management and customer treatment through automatic learning based on millions of past experiences.
Machine learning in underwriting
Machine learning insurance can play an essential role in the underwriting process for new customers thanks to the ability to analyse thousands of data from past processes and be able to predict what services, products and coverages a customer values. We can even find out what time they are most willing to see us, as well as the most effective sales strategy to use with them.
Similarly, the system will be able to provide a personalised offer for each type of customer thanks to information on previous subscriptions of similar profiles. Personalised policies are a first step in moving towards the as-a-service model, an on-demand insurance model made possible by cloud technology.
In this sense, machine learning in insurance also means a considerable saving of resources in the underwriting process. Insurance agents and call centres have very large lists of potential customers. Tackling this database from start to finish takes a lot of time and staff dedicated exclusively to the task. However, thanks to machine learning for insurance, it is easy to optimise the lead acquisition process by focusing the acquisition effort on the subscribers with the greatest potential.
Machine learning against insurance fraud
Another application of machine learning in insurance is the ability to detect patterns of behaviour, which is extremely useful in determining the likelihood that a customer is attempting to commit fraud. This means considerable savings for insurers, whose revenues depend on the payouts and claims they face on a daily basis.
For that reason, many insurers are already using Artificial Intelligence to categorise potential customers depending on the risk of fraud. By knowing data from similar experiences, information about specific people and assets to be insured, companies can anticipate and reject dubious customers.
The analysis of millions of data also makes it possible to calculate the probability of fraud at the time of claim declaration or prior to settlement. This is an essential tool that complements the work of the loss adjuster in charge of assessing incidents. Machine learning in insurance industry can even be used to support potential lawsuits.
Machine learning for insurance claims
Efficiency and speed in handling incidents is one of the main reasons for policyholders to subscribe and remain insured. In this sense, machine learning for insurance, as a system of continuous learning and improvement of past experiences, can carry out an exhaustive analysis of the most successful claims and resolutions to offer the most appropriate solution for the claimant’s needs.
But in addition to a successful solution, this technology is capable of referring each case to the appropriate department by analysing and processing the information in less time, which favours a faster response.
As some insurers point out, machine learning, in addition to more efficient customer acquisition, can also improve customer loyalty. Algorithms can detect patterns of behaviour and even predict which customers might drop out in the near future. So instead of trying to convince them to stay after they have made that decision, machine learning in insurance industry allows you to adopt a proactive loyalty strategy.