Big data has quickly become one of the most powerful buzzwords in the insurance industry. It's an invaluable tool for analysing trends and patterns that insurance brokers can use to inform policy, assess risk and identify fraud.
By now most insurance providers are using big data to some degree, but the applications of big data are still growing and changing every year. Understanding what big data is and how it can be utilised to offer efficient, smart insurance products to discerning consumers should be the first step in any insurer's playbook.
What is big data?
Broadly speaking, big data refers to the analysis and management of high volumes of data for use in recording, tracking and predicting patterns and trends. Almost all businesses in all sectors are inundated with vast swathes of data every day, both structured and unstructured.
What matters is how they apply this data and turn it into something usable.
Big data is a relatively recent development both in insurance and other sectors because the size of the data sets previously made it impossible to analyse with traditional methods.
But with advancements in artificial intelligence and machine learning, big data can be stored efficiently and analysed computationally, making it all the more valuable for companies who are keen to understand consumer trends and patterns.
The recent surge in the popularity of big data in insurance can, in part, be attributed to the rise of the Internet of Things (IoT). The IoT refers to ordinary devices all around us that can be used to send and receive data via the internet.
Telematics or ‘black boxes’ for car insurance policies are a great example of how IoT devices have become a normal aspect of daily life for many people, and these smart devices can also provide valuable and accurate data which companies have never had access to before.
Big data in insurance
By now most insurance companies understand that big data should be at the crux of much of their work, but only a few truly understand how to process it and put it to good use within their business.
In fact, big data can be applied to almost all aspects of the insurance process, from underwriting to managing claims and customer service.
A report by the European Insurance and Occupational Pensions Authority (EIOPA) found that the most significant role of big data in insurance today is in pricing and underwriting. A great example of this is in motor insurance, where brokers can compare individual driving behaviour with a big data set to accurately predict risk and tailor policies to each motorist.
In claims management, insurers can use big data to assess loss or damage in order to segment or in some cases help automate claims. This makes it much simpler for providers to make big decisions on claims, including whether or not a claim is paid out.
Perhaps one of the most interesting uses of big data is when it is used as a tool to predict and even change customer behaviour. This is tied into the IoT; insurers who can correctly analyse customer behaviours using data from a wide range of devices may be able to step in before a claim is even made to remind policyholders to adjust high-risk behaviours, such as driving too fast or forgetting to set a burglar alarm.
Finally, big data plays an important role in fraud detection. 1,300 insurance scams are detected every day, and big data can be used to scour data for anomalies, analyse social network information and model fraud risk.
How can AI and machine learning help with big data?
Big data is a vital component of most insurtech innovations, and artificial intelligence (AI) and machine learning are crucial in discovering the full potential of big data in insurance. Big data and AI complement each other because both can be used to inform and improve the other.
Let's take, for example, the role that both AI and big data play in online chatbots. Online chatbots can be used by insurers to handle customer queries quickly and effectively, freeing up staff for other important tasks. To effectively train a chatbot, insurers must use both machine learning technology and big data to feed policy and claims data into the bot, which can then offer fast, smart responses to customer questions.
Machine learning has also been used to great effect in claims management, particularly in sectors such as motor insurance which have vast amounts of data to draw upon. Machine learning algorithms can be programmed to scan big data for specific queries which can aid in decision-making over claims.
Big data in commercial insurance
Big data can be used to great benefit in the commercial insurance industry to inform policy and optimise business practices at a high level, while at the same time improving value for business customers.
Commercial insurers keen to take advantage of big data must first recognise just how widely big data can be applied to their business strategies.
Some of the core features of commercial insurance products are public liability and employers' liability.
Big data is particularly relevant in products offering public and employers' liability cover, as it can be used to assess risk against a wide variety of behaviours and precautions. It can be used to identify the most effective health and safety measures and provide an incentive to commercial customers to improve health and safety across all sites.
The future of big data
Unlike some other hot topics, big data isn't a passing trend. As more and more IoT devices come online and consumer behaviours change, the opportunities afforded by big data in insurance will grow with it, as will the capacity of the cloud to store such quantities of data. IDC forecasts report that the global datasphere is expected to reach 175 zettabytes by 2025.
With the global capacity to collect and store data growing and with the advancements in AI and machine learning technology, insurers need to seriously evaluate their technology stacks to ensure they can remain competitive and respond to growing customer demand.
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