Big Data and the Insurance Industry: Using Data to Increase Your Bottom Line

Big Data can have a positive impact on all insurance processes — from underwriting, pricing, risk assessment, and reinsurance, to curating personal recommendations and other strategic marketing decisions. This article will explore the various types of data available to insurance companies and the technology stack needed to implement them.
10 min read
24/11/20
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By Peter Rakowsky
Global Innovation Manager
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Big Data and the Insurance Industry: Using Data to Increase Your Bottom Line

Big data can make insurance premiums more affordable for customers, which increases the number of clients for every single insurer. That is because greater data-driven premiums improve risk transparency and facilitate better customer behavior, whereas non-controllable consumer hazards can go under the governmental umbrella.

Big Data for Insurance Premium Levels

Let’s explore other uses for big data and how insurance companies can efficiently manage this data using big data and predictive analytics software.

Big Data in Insurance: Core Data Types

Geospatial Data

Geospatial data tells insurance companies the exact location of their clients.

Insurers can use this data to determine how close a policyholder is to potential risks, including flood zones, fault lines, toxic waste, and crime zones. For casualty insurers, life insurance, travel policy providers, property insurance, and health insurance companies, this can be invaluable.

Since the exact location of a customer is known, vital documentation like claim acknowledgments, cancellation notices, or non-renewal notices will arrive at the correct address.

To start using geospatial data, a company needs to have a GIS (Geographical Information System), like QGIS, MapInfo, and ArcView. GIS is a framework that collects, manages, and analyzes geographical information. It can be integrated with apps and shared with anyone, anywhere. The final data is then delivered using maps and 3D scenes for interpretation.

Companies that cannot afford their own GIS and geospatial tools are likely to get location intelligence data from external agencies. Some of the top third-party software providers offering this service are Korem, AND, BetterView, Delos, TomTom, Navigation Data, Digital Map Products, PrecisionHawk, HazardHub, Hover, Guidewire, and many others. Examples of companies using geospatial data to assess risks include IAG, Amica Mutual Insurance, and USAA.

Bottom line

To use geocoding data, your company needs a GIS that is frequently updated. With precise data, insurance providers can assess risks and confirm the delivery of important documents, both of which are essential to the underwriting process.

IoT Data

Internet-of-things (IoT) data is used extensively in the automotive insurance industry to record data on policyholders’ driving patterns. To collect this info, telematics (communication and tracking tools) and black boxes must be installed on drivers’ mobile phones or vehicles. These can include GPS trackers and other electrical sensors. These devices allow for premiums adjustment depending on the individuals’ records.

Data from telematics devices is positively influencing the growth of usage-based insurance (UBI), a common insurance framework where policyholders pay as they go. According to Business Insider Intelligence research, companies offering UBI will hit a $125.7 billion market cap by 2027.

Health and life insurers use physical activity wearables (connected IoT gadgets) to record blood pressure, steps walked, calorie consumption, and other body activities. Examples of medical IoT devices include connected contact lenses, glucose sensors, and depression sensors. The data obtained from these wearables is then used to inform proper underwriting, pricing, and claims management.

In a world where health concerns are on the rise, IoT data can be used to promote better practices through incentives like reduced premiums, gym membership reimbursement, or free access to a wellness program. These personalized offers are exactly what most people look for. So ultimately, your company will see an increase in customer base.

Property insurers use drones, smart alarms, air conditioners, automated home security systems, and other devices to get accurate info on the scale of damages. And as time goes by, many homes in the United States and other parts will be connected, making IoT data easily available.

To start using IoT data, a company needs to convince its customers to wear a wrist device or install telematics in their phones. But this is just the beginning. IoT data is largely unstructured and therefore needs additional efforts for cleansing. Moreover, it is required to be kept in a cloud. This means a company wishing to dabble in this data must have a cloud computing infrastructure in place. You can turn to DataArt for a cloud migration or infrastructure setup.

If your data is not sufficient to generate accurate predictions, it would be advisable to look into third-party data. Once you have all the data you need, an analytics program becomes the next item to consider. Data analytics programs are tasked with cleaning, sorting, and interpreting data into profitable decisions. AIG, Progressive Insurance, AVIVA, American Family Insurance, and Aetna are early entrants into the IoT data world.

Bottom line

IoT data will be an important growth factor for many insurance companies. However, to profit from it, data management must flawlessly harmonize info from the company's own pool with data obtained from the third parties. Failure to do that can result in the havoc that leaves a company unable to put their most valuable resource to good use. DataArt can help by providing robust data analytics tools and cloud storage solutions.

Insurer’s Own Data

Insurer’s own data, also called first-party data, is the information collected by the insurer from their own customers. This information can include residential addresses, emails, phone numbers, and purchase history.

Companies can obtain first-party data through interviews, form submissions, transactions, or through marketing efforts like comments, likes, and email clicks. This data can inform all your marketing strategies, from content production to pricing.

This information lets companies better structure target segments and predict their behavior. Moreover, this data is perfect for personalization efforts.

If you already collect your own data but do not know how to maximize the benefits associated with it, turn to DataArt. We will help you utilize your existing data to unlock new insights about your customers.

Bottom line

Once a company has a data collection system in place, software can help analyze that information to provide concrete insights on your client base.

Hazard Data

Hazard data is the information collected during and after a hazardous event. This includes information on the intensity of an earthquake, soil condition, distance from wildfire, flood depth, and so on. This data can be obtained through a company’s telematics devices or from other third-party agencies, like RMS, Impact Forecasting, JBA, HazardHub, and KatRisk.

Property and casualty insurers are the biggest consumers of hazard data. Insurers in these industries need this data to assess property risk and price their policies based on an individual’s risk score. Examples of companies using data include AllState and Security First Insurance.

To start using hazard data, companies should refine internal data instruments or purchase it from third parties (a fairly costly process). Most companies operate a large number of simultaneous data streams, which can quickly overburden a small team. Luckily, software programs and APIs can help alleviate the workload.

Bottom line

Hazard data can help property and casualty insurers forecast a property’s susceptibility to disasters. This enables them to price their policies competitively and cut down on losses. Investing in automation and other APIs that process data faster is vital.

Behavioral Data

Behavioral data is the information gathered from customers’ actions. Human beings are predictable, and, by watching their behavior patterns, an insurance company can learn how and when they make decisions. This strategic move can help companies target consumers with the right offer or information.

The main sources of behavioral data are first-party and third-party agents. The former involves lead gathering, cookie tracking, agent interactions, and other in-house data harvesting methods.

But to get a holistic view of a customer buying journey, insurers need to tap into third-party sources. This data can provide information on how a customer is interacting with other websites, what else they are buying, and how long their journey has been.

Behavioral data is essential to a variety of insurance industries. For companies just starting off with behavioral data, it is essential to segment data, perform risk modeling, and individually target customers. APIs and predictive analytics can help you manage and analyze this data. Examples of insurance companies using behavioral data in their approaches include Neos, London-based ZEGO, and Lemonade.

Bottom line

Highly successful insurance companies like Lemonade are also heavily customer-centric. They rely on behavioral data to tailor their services and meet each client’s expectations.

Online Media Data

Online media data is the information collected from customers’ social media profiles which is then used to influence underwriting decisions. Many life and home insurance companies use social media posts to calculate an individual’s premium. Examples of companies using online media data include Hiscox, Liberty Mutual, Metlife, Aegon, and QBE.

This data can be obtained from analyzing social media information from sites like Facebook, Twitter, Instagram, and Linkedin.

To get started with online media data, there are a few things insurance companies need to do. The first is building a community of followers and responding to their queries. These agents will also be responsible for curating and publishing up-to-date content.

Bottom line

Online media data is a powerful resource in the underwriting process. Live feeds of user data can help companies make well-informed decisions.

Simultaneously, there are so many gray areas when it comes to using customer data like seeking permission, not sharing with third-parties, and using it only for insurance purposes. Make sure your company is not breaking any rules.

Case Studies for Big Data in Insurance

Big data in insurance is no longer a new concept. Many traditional companies are undergoing massive technological transformations to leverage the full potential of their data.

Infinity Insurance

Like all insurance companies, Infinity faced a problem with fraudulent claims. As a property and casualty policy provider, it deals mostly with higher-risk drivers and properties. These customers have an increased chance of making fraudulent claims.

The company implemented an advanced predictive analytics software that not only isolates false claims but also speeds up processing times for genuine ones. The company was able to spot around 88% of fraudulent claims, which shortened the claim investigation process, and reduced its losses.

Big Data Analytics for Insurance Fraud Detection

Automated Data Analytics for a U.K. Insurance Provider

A large insurance provider in the U.K. was grappling with inefficiencies in their actuarial software. Their existing program took weeks to perform key duties like risk and profit analysis. This resulted in the company incurring high expenditure costs and missing opportunities.

The company turned to DataArt for a robust and up-to-date automated data analytics platform. This program would be tasked with analyzing data sources and allowing easier and secure access to both internal and external data. As a result, they experienced faster data processing times and lower costs.

DataArt worked alongside a team of actuaries to create a user-centric web interface that allowed easy input and viewing of data. The changes dramatically increased efficiency and enabled the company to enroll many new clients. The project became a long-term relationship, and DataArt now offers further automation and re-platforming of newer systems.

Conclusion

Big data is one of the best ways insurance companies can make profitable moves. Unfortunately, vast amounts of data still remain unused, creating untapped potential.

Processing huge amounts of complicated data requires strong technology. If you look at big data trends, you will realize many insurers are modernizing their legacy system to allow for better integration. This means tapping into robust AI tools and advanced predictive analytics.

If your company is considering delving into the world of big data, DataArt can help. We can guide you through the process of software acquisition and development, as well as the integration process.

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