Machine Learning in Insurance

Insurers are being forced to explore ways to use predictive modelling and machine learning to maintain their competitive edge, boost business operations and enhance customer satisfaction, according to Beinsure report about Machine Learning in Insurance. They are also examining how they can take advantage of recent advances in artificial intelligence (AI) and machine learning to solve business challenges across the insurance value chain. These include underwriting and loss prevention, product pricing, claims handling, fraud detection, sales and customer experience.

AI and advanced machine learning are among the top 10 strategic technology trends leading organisations are currently using to reinvent their business for a digital age.

The key market forces driving the adoption of AI and advanced machine learning are:

Smart everything – Enterprises are looking to use advanced machine learning to drive smart, automated applications in fields such as healthcare diagnosis, predictive maintenance, customer service, automated data centres, self-driving cars and smart homes.

Open source everywhere – As data becomes omnipresent, open source protocols will emerge to ensure data is shared and used across. Different public and private entities will come together to create ecosystems for sharing data on multiple use cases under a common regulatory and cybersecurity framework.

Harnessing Internet of things data – The volume and velocity of data from IoT will drive the need to automate the generation of actionable insight using advanced machine learning tools. According to Gartner, 20% of enterprises will employ dedicated people to monitor and guide machine learning (such as neural networks). The notion of training rather than programming systems will become increasingly important.

Ability to talk back – Natural-language processing algorithms are continuously advancing. AI is becoming proficient at understanding spoken language and at facial recognition, helping to make it more useful and intuitive. These algorithms are evolving in unexpected ways, as Google found when Google Translate invented its own language to help it translate more effectively (see How AI Technology Can Help Insurers Enhance the Customer Experience?.

Global AI market, by geography 2017-2024 (in $ mn)

Most insurance companies process only 10–1% of the data they have access to—most of which is structured data they house in traditional databases. That means they are not only failing to unlock value from their structured data, but also overlooking the valuable insights hidden in their unstructured data.

Analysing this unstructured data and using it to drive better business decisions requires advanced data science techniques.

Emerging data analytics technologies centred on machine learning bring order and purpose to this unstructured data so that it can be more effectively mined for business insights.

One major benefit of machine learning is that it can be effectively applied across structured, semi-structured or unstructured datasets. It can be used right across the value chain to understand risk, claims and customer behaviour, with higher predictive accuracy.

The potential applications of machine learning in insurance are numerous: from understanding risk appetite and premium leakage, to expense management, subrogation, litigation and fraud identification.

Challengesin implementing machine learning

Most insurers recognise the value of machine learning in driving better decision-making and streamlining business processes. Research for the Accenture Technology Vision 2018 shows that more than 90% of insurers are using, plan to use or considering using machine learning or AI in the claims or underwriting process.

Some of the challenges insurers typically encounter when adopting machine learning are:

Training requirements

AI-powered intellectual systems must be trained in a domain, e.g., claims or billing for an insurer. This requires a separate training system, which insurers find hard to provide for training the AI model. Models need to be trained with huge volumes of documents/transactions to cover all possible scenarios.

Right data source

The quality of data used to train predictive models is equally important as the quantity, in the case of machine learning. The datasets need to be representative and balanced so that they can give a better picture and avoid bias. This is important to train predictive models. Generally, insurers struggle to provide relevant data for training AI models.

Difficulty in predicting returns

It’s not very easy to predict improvements that machine learning can bring to a project. For example, it’s not easy to plan or budget a project using machine learning, as the funding needs may vary during the project, based on the findings. Therefore, it is almost impossible to predict the return on investment. This makes it hard to get everyone on board the concept and invest in it.

Data security

The huge amount of data used for machine learning algorithms has created an additional security risk for insurance companies. With such an increase in collected data and connectivity among applications, there is a risk of data leaks and security breaches. A security incident could lead to personal information falling into the wrong hands. This creates fear in the minds of insurers.