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.

Science, applied: 3 ways AI and ML are advancing the insurance industry.

From maximizing advertisement relevance to customizing user experience, the benefits of applied sciences and advanced data analytics have become more apparent as industries adopt data-driven approaches to create new competitive advantages. In this article, we focus on companies in the insurance industry that are implementing applications of data science to deliver efficient, risk-adjusted solutions by detecting fraudulent activity and providing a personalized customer experience. The best place to start is by looking at some of the technological trends being used by insurance companies today.

Growing Trends in the Insurance Industry

Customer Experience & Coverage Personalization

With access to a customer’s behavioral, geographic, social, and account data, AI-enabled chatbots can provide seamless, automated, and personalized buying experiences. These bots are quickly becoming the industry standard. According to a 2020 MIT Technology Review survey of 1,004 business leaders, customer service (via chatbots) is the leading application of AI being deployed today. The study shows that 73% of respondents indicated that by 2022, it will still be the leading use of AI in companies.

Behavioral-Based Policy Pricing

In the auto insurance industry, we are seeing ubiquitous IoT sensors provide personalized data to pricing platforms, allowing safer drivers to be rewarded by paying less for auto insurance (known as usage-based insurance). These techniques have expanded beyond auto insurance, and we are now seeing health & dental insurance companies also use IoT sensors that provide people who maintain a healthier lifestyle with a lower rate for insurance. A recent article highlighted dental insurance company Beam Digital for their use of IoT technologies. This company provides a smart toothbrush to every customer and monitors their oral health, while using this information to support a dental insurance plan. Beam sends the customer notices and encouragement if their brushing habits are falling short of the required standard. The company hopes this will result in improved dental hygiene and reduced premiums.

Faster, Customized Claims Settlement

Online interfaces and computer-vision enabled virtual claims adjusters now make it streamlined and more efficient to settle and pay claims following an accident, while simultaneously decreasing the likelihood of fraud. Customers are now also able to select their preferred provider’s premiums that will be used to pay their claims (known as peer-to-peer/P2P insurance). Data science applications have enabled the required higher-fidelity predictions based on events, in real-time, using large datasets rather than samples to make the best guess.

Industry Leaders That Are Adopting AI/ML

With advancements in AI/ML applications, more insurance companies are now actively leveraging preexisting data to increase the depth of understanding they have of their customers. Companies like State Farm, Liberty Mutual, Allstate, and Progressive are among a few of the industry leaders that are adopting AI and ML applications into their business model.

Allstate Insurance

Greg Firestone, Vice President of Data Science at Allstate Insurance, explained in a recent interview why his company began leveraging anti-fraud technologies to mitigate fraudulent claims. “It’s very hard to measure sometimes, but it’s happening,” Firestone said. “The best prevention is really being aggressive: using AI and data to find fraud. Data is your friend in this regard. Fraud is a problem that impacts all insurance companies, and we need to focus on it and make sure the fraudsters realize that we’re not easy marks.”

The company leverages an AI-based solution to monitor and flag suspicious claims, however, they understand that keeping an eye on future fraud trends will still require a human touch. Large insurance companies process thousands of claims daily, making it impossible for a team of human analysts to thoroughly review each instance for fraudulent activity. Thus, many insurance companies are leveraging advanced AI systems to automate this process, which allows them to reserve their teams for claims the AI-based solution has flagged as suspicious.

Liberty Mutual Insurance

Last year, in an official press release, Liberty Mutual announced a strategic relationship with Groundspeed Analytics, Inc. to cut the time to extract submission data by 50% through the use of Artificial Intelligence (AI). “Properly evaluating customer submission documents is one of the most critical aspects of the underwriting process, and current methods don’t take advantage of the value locked in these documents.” By leveraging the available data in submission documents in a “data first” approach, Groundsped is helping Liberty Mutual to make better risk selections, improve time-to-quote, and deliver better customer service.

Progressive Insurance

In recent news, Progressive Insurance is reportedly leveraging Machine Learning algorithms for predictive analytics based on data collected from customer drivers. Progressive claims that “its telematics (integration of telecommunications and IT to operate remote devices over a network) mobile app, Snapshot, has collected 14 billion miles of driving data.” By feeding the labeled data which connects accidents with the accordant driving data, the insurer could identify a pattern and predict a new customer’s likelihood of causing accidents by simply gathering hours of their driving data. This data collection process could encourage the drivers to monitor and optimize their driving habits, and possibly decrease their number of accidents. As for the insurance company, increasing further data science capabilities allows them to gather a better outlook on the possible return and risk.

Customer Acquisition Through Predictive Analytics

Traditionally, insurance agents have relied on relationship-selling supported by lead generation tools. Today, new tools exist to help insurance carriers start to predict customer needs for insurance products. These tools use predictive analytics to look for “active signals” of customer intent and then tie in relevant insurance products. For example, knowing that a construction company has just won a large contract is a good signal that they might want additional umbrella insurance. Also, knowing that a business has just secured its first institutional round of funding is a good signal that the firm needs Directors & Officers insurance. Broadly speaking, algorithms use these predictive signals to look for specific events, or business life cycle activities (e.g. starting a business), to offer new and relevant insurance products that fit each customer’s needs. Moreover, algorithms can be used to identify other related businesses that have similar characteristics (e.g. revenue size, industry type, location) to an insurance company’s existing customer base. Diving into the world of online betting and casinos, one fact becomes increasingly evident on this page : the diverse range of odds available across different markets during varying intervals of a game or match. This intriguing world of numbers isn't merely about luck; it's a game of strategy, analysis, and timing. Take, for instance, the phenomenon of varying odds. Why does one bookmaker offer a specific coefficient while another might present something entirely different? These variations are not arbitrary. They arise from a myriad of factors ranging from real-time game events to the internal policies and risk assessments of the bookmaker. For bettors, this ever-changing landscape poses both a challenge and an opportunity. The challenge lies in the need to constantly update oneself, to stay abreast of the myriad changes happening in real-time. It's about anticipating events in the game, understanding the strategy of teams or players, and predicting how these factors might influence the odds. The opportunity, on the other hand, lies in the potential for profitability. By understanding these shifts and making informed decisions, a bettor can leverage the best odds to maximize their returns. It's a tantalizing dance between risk and reward, strategy and instinct. Yet, the beauty of the online betting realm, especially in India, isn't merely in its complexity. It's in the accessibility it offers. With many platforms allowing transactions in Indian rupees, even if they are not based in India, the Indian punter feels more at home, more integrated into this global community.

Leveraging AI and ML capabilities for gathering and analyzing social, historical, and behavioral data allows companies to gain a more accurate understanding of their customers and provide better products and services. The three industry leaders mentioned in this article are just a few of many companies harnessing the power of applied science capabilities to better understand their customers and their data. Through more precise risk prediction, personalized customer policies, and automated settlements, both insurance providers and customers can benefit from the impact of science applied to technologies in the insurance industry.