The Role of Big Data in Personalizing Insurance

Big data in insurance has the potential to transform the insurance business, which has relied on traditional data analysis methods for way too long.

Big data services help insurers provide detailed, personalized services that dive deep into what each customer wants from the insurance company. They help tremendously with fraud detection, risk assessment, customer retention, cost management, and many other critical aspects of the insurance industry.

The way the insurance companies of the 2020s handle these challenges is fascinating. It disrupts the traditional models of the insurance industry and offers customers service of the new generation that is so different from what we came to expect from insurers.

In this article, we will look into how big data is used in the insurance industry and personalized insurance, particularly in some real-life cases of insurance companies.

The importance of data in personalized insurance

Out of all types of insurance, big data powers personalized, consumable insurance the most. Personalized insurance offers customized services up to the point of hyper-personalization through data analytics, artificial intelligence (AI), IoT, API, and automation. It provides insurers with a profound understanding of their customer base, resulting in better engagement and policies tailored to each customer's circumstances.

Compared to traditional insurance, data analysis in personal insurance is faster and more efficient. The hyper-personalization in insurance extends to pricing, coverage, and risk assessment, among other things. While some personalized offers existed in the insurance industry even before the current digital revolution, the use of predictive analytics contributed to gaining much more profound insights.

Personalized insurance benefits significantly from the open insurance concept, where customers have real-time access to their insurance data. This helps customers make informed decisions about their coverage.

Personalized insurance can also adjust pricing based on real-time data and individual risk assessments. This could mean lower premiums for customers who demonstrate safer behaviors or choose lower-risk lifestyles based on the data from IoT devices and wearables.

Personalized insurance simplifies the customer journey, providing a smoother and more targeted experience than traditional insurance.

Here are some stats that demonstrate the importance of big data in the personalization of insurance:

• About 95% of customers are willing to share personal data in exchange for personalized insurance (Source ).
• In 2017, two-thirds of companies expected a 6% to 10% revenue increase by adopting personalized insurance and big data (Source ).
• 41% of insurance consumers report willingness to switch providers based on whether they offer digital services or not (Source ).
• 88% of insurance customers demand more personalized insurance products, and 21% believe insurers do not personally cater to them at all.

Uses of big data in the insurance industry

The impact of big data on the insurance industry is unprecedented, but how exactly is big data used in the insurance industry? Let's explore examples and use cases.

1. Personalized service

The first thing that comes to mind when considering big data's impact on insurance is personalized service. Through analyzing the vast amount of customer data, insurance companies can offer hyper-personalized services to customers that cater to their individual needs and risk profiles. Big data in insurance offers valuable insights into customer preferences and behaviors. With this information, businesses can create better communication and engagement strategies.

There are hundreds of examples of insurance companies using big data in hyper-personalization in insurance, as personalized services are the main reason why big data is used in insurance in the first place. For example, Allianz caters to its travel insurance offerings based on information about recently booked flights. It takes less than a second to personalize these offers based on this information (Source ).

John Hancock offers the John Hancock Vitality program in combination with its life insurance (Source ). With this program, customers can get in-app rewards for their healthy behaviors, which are trackable through wearables, such as exercising, getting regular checkups, and eating healthily. Bonuses include premium reductions, travel discounts, retail discounts, and discounts on food in the grocery store. The app leverages big data applications in healthcare to great success.

2. Fraud detection

Big data in insurance personalization can be used to identify patterns that indicate fraudulent activities. Basically, big data in insurance today is the best way to detect and prevent fraud, including determining fraud in claims and applications.

Fraud in insurance can be detected through various methods, including predictive modeling, social media analysis, telematics, and natural language processing (NLP). Here are several examples of how fraud can be detected:

• A large health insurance company might use predictive models to detect anomalies in healthcare providers' billing patterns, such as charges for services far exceeding normal regional averages.
• An auto insurance company can use social network analysis to uncover networks of claimants and service providers filing suspiciously similar claims across unrelated incidents.
• NLP can be used to identify inconsistencies in customer stories through the analysis of chatbot interactions.
• Telematics data can help uncover the vehicle speed during a car accident. The insurance report will reveal that the customer committed fraud by lowering the vehicle's speed during the incident in his claim.
• A life insurance company can use big data and AI to monitor policy applications and claims for patterns indicative of life insurance fraud, such as discrepancies in medical history or beneficiary information that emerge from cross-referencing external databases.

3. Risk assessment

Big data enables more accurate and sophisticated risk models by incorporating various factors, including external data sources such as weather patterns, geolocation data, and social media trends. Big data insurers can use the acquired data for dynamic pricing strategies that reflect individual risks.

For example, Nationwide, an auto insurance company, has the SmartRide program that monitors potential customers for six months before they sign up for the program. This works both for risk assessment and personalization of insurance policies. It also helps to determine whether the customers are eligible for the program in the first place.

Analyzing social media and online activity data can offer insights into lifestyles and behaviors that may impact risk assessments. However, insurers must carefully navigate privacy concerns and regulatory boundaries when using this type of data, as there are lines between collecting data for insurance purposes and violating privacy.

Big data enables insurers to segment risk at a much more granular level than traditional methods. For instance, health insurers can use data from fitness trackers for deep analysis of individual health behaviors. They can profile customers based on their activity levels and cardiovascular health.

4. Customer acquisition

Big data tools help insurers identify market trends and customer needs, enabling them to develop products and marketing strategies that effectively target potential customers. Data also helps prioritize leads based on the likelihood that they will convert.

Big data insurers use predictive analytics to assign scores to leads based on their likelihood to purchase a policy. This model uses historical data and behavioral analytics to predict which prospects will likely become customers. By focusing on high-scoring leads, insurers can allocate their marketing resources more efficiently to increase the ROI on marketing expenditures.

For instance, Metromile identifies and targets customers who benefit most from pay-per-mile insurance, such as those who drive infrequently. By doing so, it can capture a niche market by offering highly competitive prices to drivers.

5. Customer retention

Big data for customized policies enables the so-called process of churn prediction, which helps identify when a customer is not happy with the experience and is about to leave. Based on this data, insurance companies can take targeted actions to address customer pain points.

Big data in personalizing insurance can also help predict when customers will likely renew their policies and identify factors influencing their renewal decisions. This allows insurers to tailor their renewal strategies to individual customers with their unique concerns.

For example, State Farm's customer engagement program utilizes big data to understand customer life stages and needs. It then gives customers personalized offers that increase retention. In another example, Allstate's Drivewise program engages customers by providing feedback and rewards for safe driving, positively impacting retention.

6. Data deduplication

Data duplication can be a source of constant headache for big data insurance, including when it comes to big data challenges in healthcare . Duplications occur when insurers use big data from different sources, like direct sales and customer integrations, without integrating the sources. Data duplication means that your company ends up paying more for storage, data integrity can be compromised, and data analytics might get skewed.

Data deduplication is a crucial process for insurance companies because it lets them manage large data sets efficiently when the same client appears in the database through different channels.

It involves identifying and removing duplicate copies of data, ensuring only one unique instance is retained, which can significantly reduce storage needs and improve data accuracy​. Data deduplication can clear data up for enhanced data management, better risk assessment, and even better fraud detection.

7. Cost reductions

Big data transforms insurance by optimizing all processes, leading to cost savings through adequate resource allocation. Cost reduction is made possible through many factors, including:

• Enhanced risk assessment
• Targeted customer acquisition
• Early detection of fraud
• Routine task automation
• Faster and more accurate claims processing
• Retention minimization

Big data insurance companies

Now that we have examined how big data transforms insurance, let's focus on companies that aim to make the most of big data in insurance.

Binariks

While not an insurance company per se, Binariks acts as an insurtech partner that can help insurance companies leverage big data for personalized policies. The company offers insurance software development services for insurers willing to go digital.

In terms of big data in insurance personalization, Binariks offers custom software development services to empower businesses to integrate telematics insurance technology, run real-time predictive analytics, and more.

Binariks focuses on business intelligence, IoT, big data, and analytics to create custom insurance products that make the most of personalized insurance services.

Allstate

Allstate is one of the largest insurance companies in the United States, headquartered in Illinois. It uses big data for various purposes, including personalized pricing models through its Drivewise app. This app monitors driving behavior in real time to offer discounts and rewards for safe driving habits.

They also leverage big data for predictive analytics in claims processing. Allstate is one of the early adepts of big data, as they started mining their existing data pool for big data insights in 2011.

Progressive Insurance

Progressive Insurance is an Ohio-based big-data insurance company focused on auto insurance policies.

Progressive is well-known for its Snapshot program, a usage-based insurance model that tracks driving behavior to adjust premiums accordingly by plugging into the car's dashboard. The program uses telematics technology to collect data on driving patterns, such as the time of day, mileage, and braking habits. Based on the collected data, the company offers lower rates to safe drivers. The program even provides driving tips to drivers.

AXA

AXA is a multinational insurance firm that provides a range of insurance products and services globally, including life insurance, health insurance, property and casualty insurance, and asset management. The company is primarily headquartered in Paris, France.

AXA has invested heavily in data analytics and AI to transform its services. One notable initiative is using big data for risk assessment and prevention strategies. For example, AXA utilizes data analytics to offer personalized health recommendations to policyholders through their Health Keeper app. They also employ big data for sophisticated fraud detection mechanisms and to streamline claims management processes.

Lemonade

Lemonade is a new-generation big-data insurance company founded in New York in 2015 that has leveraged big data since its inception to disrupt traditional insurance models. It offers renters, homeowners, pet, and life insurance.

Lemonade's business model is unique in that it takes a flat fee out of customers' premiums to cover operational costs. Under its Giveback program, Lemonade gives back unclaimed money to causes chosen by its customers. Lemonade collects data through an AI-powered chatbot, Maya.

Lemonade is super-personalized as it uses unprecedented amounts of personalized data in personal insurance, which means that its prices are more personalized than those of other insurers.

Conclusion

To conclude, big data in insurance is a true disruptor that changes how businesses interact with customers. Insurtech solutions based on big data are successfully leveraged by large insurance companies of the older generations and brand-new insurtech apps like Lemonade.

Big data is making waves in all insurance sectors, including auto, life, health, and many others. If you are an insurer looking to use big data for personalized insurance, you will benefit from using custom insurtech services from providers like Binariks. We can help you craft personalized big data solutions based on your unique request.