Artificial intelligence (AI) is the technology used by machines to mimic the human mind and how it processes information. It effectively programmes machines to think and behave like humans when analysing data and completing tasks. AI is constantly being adapted in order to help solve the most complex of human problems.

Advances in technology and the disruption caused by COVID-19, has led to an increase in the use of AI across many different sectors. An online study by McKinsey and Company asked 2,395 participants from a variety of industries about their usage of AI. It was found that 50% of respondents state that their company has incorporated AI into at least one business function. It has been embraced by an increasing number of organisations, including those within the insurance industry.

With a wealth of data available in modern times, the insurance industry has benefited hugely from this transformational technology. Using both structured and unstructured data, AI can facilitate customer service interactions and streamline claims processing, as well as improve marketing messages and communications to target the right services to the right audience. Ultimately it has allowed insurance companies to become increasingly competitive and more successful within the industry.

Machine learning in the insurance industry

Machine learning (ML) uses AI to enable computers and machines to adopt human behavioural patterns so that they can learn from and analyse data without the need for human intervention. This can be done through different ML algorithms which replicate the human brain and study and learn from various patterns of data. Once the user has selected and fine-tuned an appropriate model to use, ML is able to use the available data to problem-solve and come up with predictions. The more data the machine is fed, the more it learns and the more intelligent it becomes – just like the human brain!

Managing risk:
Insurers use machine learning models to improve efficiency through evaluating certain risks and predicting their potential financial costs at an early stage. This saves both the underwriter and customer’s time.

Claims management:
Many insurers use machine learning models to improve efficiency across claims processing. These processes may be automated, decreasing costs as well as reducing the claims settlement time.

Prevention of fraud:
Identifying fraud in the insurance industry can prove challenging for insurers. The algorithms used by machine learning can identify and flag fraudulent claims more easily as it can tap into data quicker than the human workforce.

Chatbot

A chatbot is an example of AI software that can simulate human conversations and interact with customers using text or voice commands. It is often used to answer basic queries and process requests. Unlike the human workforce, chatbots are available 24/7 and are available through insurance websites. According to a study by Cognizant, 69% consumers prefer chatbots to the human workforce for instant responses and service-related queries. A report by the same company predicts that the chatbot market is expected to reach $1.25 billion by 2025.

Challenges

Whilst AI offers huge potential to insurance organisations, it can pose challenges too. With access to an unlimited amount of data, there are inevitable security risks. Therefore, one of the most important considerations, is to make sure an AI system is safe and secure. The quality of data provided in Machine Learning is also important – ML models are only as good as the data they are fed. It is central to make sure that the quality of data is not compromised. Algorithms used require a lot of power and can be expensive to run, making cost a significant issue.

Nonetheless AI is key to many industry success today. Ultimately ML can never completely replace a human, yet its ability to learn and replicate human behaviour has proved advantageous for many companies across a variety of industries.