How are AI and machine learning used in insurance?

To offer competitive rates, the insurance industry needs predictability. That’s why insurers are increasingly turning to AI and machine learning to more readily predict the future and the risks that the future may hold for their customers.

For example, AI can help insurance companies to gain a sufficient insight into changing weather patterns to keep premiums low, by analysing historical weather data and future climate predictions.

Another example is behavioural policy pricing. Rather than tailoring a policy based on the information provided by a customer on application, policies can be tailored using personalised data from devices such as vehicles and fitness trackers. This leads to personalised pricing, meaning that safer drivers and those who lead healthier lifestyles will be offered reduced premiums for car insurance and health or life insurance, respectively.

Essentially, AI allows insurance companies to rely on predictions based on real events in near-real time using large datasets, as opposed to statistical sampling of past performance.

It can also help financial firms to stay compliant with the ever-changing regulations to which they are subject. Machine learning algorithms can quickly read and learn from regulatory documents to detect correlation between certain actions and compliance, which in turn enables the detection of anomalies. Machine learning algorithms can be used in the prevention of fraudulent claims, too, by identifying characteristics that set them apart from legitimate claims.

Here are examples of applications by an InsurTech, banks and financial institutions, and an insurance broker partnering with a data analytics company.

Getsafe: the InsurTech start-up using AI

Getsafe used algorithms to identify 27 indicators that drive fraud and has implemented automated checks into its system to monitor those indicators.

Getsafe is also using AI to settle claims automatically through its in-app chatbot named Carla, which reports and processes claims in real-time. As well as using self-learning algorithms to prevent fraud, the algorithms provide other functions such as assessing risk and ensuring that their products are fair to customers using dynamic price adjustments.

From credit score to risk score

AI and machine learning is increasingly used to determine someone’s creditworthiness. In addition to the traditional credit score check, AI allows banks and other financial institutions to consider large volumes of consumer data, such as data from social profiles, telecommunications companies, utilities, and rent payments.

This generates an accurate risk score which, if below a certain threshold set by the lender, means that the customer will be automatically approved and offered the most competitive rate available to that borrower. It also significantly speeds up the loan applications process.

Willis Towers Watson and Polecat Intelligence

Insurance broker, Willis Towers Watson, has partnered with social business intelligence and data analytics company Polecat Intelligence – using Polecat’s AI to extract insights into emerging risks from unstructured data (like online reviews, social media comments, and customer service call logs).

This type of data represents more than 80% of all data, but because it is difficult to store and analyse, it is largely being wasted by organisations. This is now changing, thanks to the development of AI tools able to analyse unstructured data by extracting useful information, such as patterns and trends, at a speed and scale which is staggering.

Willis Tower Watson can gain insights into emerging risks and develop appropriate risk mitigation mechanisms.

Unlocking unstructured data offers complete market analysis for organisations. They can, for example, find patterns in customer behaviour. This allows organisations to develop products that are tailored towards customers’ specific needs, and improve their service, sales, marketing, and purchase experience.

In the case of Willis Towers Watson, having such an insight means they can develop insurance policies to help clients manage risks in a range of areas, including life sciences, reputational, product recall risk, and directors’ and officers’ liability. These insurance solutions will enable organisations to respond to the rapidly changing, increasingly complex, and interconnected challenges they now face.

Want to find out more?

In short, AI and machine learning are revolutionising the insurance industry by allowing insurers to overcome limitations that they currently face, such as inaccessibility of unstructured data and manpower resources. If you need legal assistance with the applications of AI and machine learning to your business, our team has had extensive experience with such arrangements and is well placed to assist.