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Where Do Insurers Stand on Analytics and Data Intelligence?

Recent years have been unusually challenging for property and casualty insurers. 2017 set new records for the number of billion-dollar natural disasters in the U.S., according to data from the National Oceanic and Atmospheric Administration. 2020 was also one of the most expensive years on record in terms of natural disasters. Additionally, P&C insurance investments suffered in the early months of COVID-19, as McKinsey’s Chris Bradley and Peter Stumpner note.

Today, insurers are scrambling to support their core business in an era of decreasing auto insurance spending, increasing property damage and disasters, and ambivalence from investors. Analytics and data intelligence offer a way for insurers to improve their business process and maintain a competitive edge.

Analytics and Data Intelligence in 2022

The terms “data analytics” and “data intelligence” are often used in close connection to one another. In some contexts, they’re even used interchangeably.

Analytics and data intelligence are related to one another, but they’re not interchangeable. Broadly speaking, data intelligence is an umbrella category encompassing all the ways in which data can be explored and examined in order to gain insights. Data analytics is one process within the umbrella of data intelligence.

Data analytics collects and processes raw data in order to reveal any trends or patterns that appear in that data, writes Jagreet Kaur Gill at AI platform The collection and processing functions are designed with key questions or goals in mind. Data analytics that seek to understand changes in climate and weather patterns, for instance, will work differently than data analytics that seek to understand why insurance customers abandon the application process halfway through filling out a quote form for auto insurance.

Analytics and data intelligence are broad categories. Their function and application can seem nebulous. Yet these tools can be applied to a number of concrete areas in insurance, write Yannick Even and Jonathan Anchen at Swiss Re.

For example, AI-enabled data analytics can be used to process text quickly in documents (including contracts), email and chat logs. The result is a better understanding of individual customers and contract terms, which enables agents, brokers and customer service representatives to provide better service.

Despite lagging in the early stages of analytics and data intelligence adoption, insurance is catching up quickly — and even driving innovation. “This change is happening so rapidly that the insurance industry today exhibits a higher maturity in data analytics than several other industries,” write Suhas Sethi and Yogendra Goyal at business process management company WNS Global Services.

Insurance and Data Intelligence

Analytics and data intelligence are no longer merely nice to have. “Best-in-class performers are putting distance between themselves and competitors by building advanced data and analytics underwriting capabilities that can deliver substantial value,” write Kia Javanmardian and fellow researchers at McKinsey.

McKinsey research has found that when analytics and data intelligence are used for underwriting, insurers see improvements in several areas:

  • 3 to 5 point improvement in loss ratios.
  • 10 to 15 percent increase in new business premiums.
  • 5 to 10 percent customer retention in the most profitable insurance categories.

Data intelligence can also be used to spot rising trends and new market opportunities, the authors note.

Artificial intelligence is a strong driver of improved analytics and data intelligence. A WNS/Forrester study reports that:

  • 72 percent of responding businesses used AI-enhanced tools to improve business intelligence.
  • 67 percent used text analytics and **65 percent **used speech analytics to better understand data, including customer interactions.
  • 54 percent relied on natural language processing, often embedded in chatbots and similar tools, for better data insights.
  • 45 percent were using machine learning platforms to improve analytics and data intelligence.

Overall, the study finds that 97 percent of businesses report operational benefits stemming from their advanced data analytics and intelligence practices.

As with every technology, AI-based analytics and data intelligence garnered skepticism in its early days. At first, insurers lacked the information they needed to determine whether AI was a flash in the pan trend or an enduring business tool.

Today, AI falls firmly into the latter category, thanks to its ability to transform the business of insurance at every stage, writes Thomas Löchte at IKOR. Data ecosystems help insurers underwrite and distribute insurance policies in response to real-time events. For example, data intelligence allows insurers to provide embedded or point of sale coverage tailored to each customer’s specific needs.

Data intelligence can’t do its job alone. “The idea that AI solutions can be bought, plugged in and left to run on their own devices is, frankly, science fiction,” says Zhiwei Jiang, CEO of insights and data at Capgemini. Insurers will need staff with the skills necessary to guide and develop data intelligence and analytics. The ability to interpret and apply the results of those analyses is required as well.

Best Practices for Robust Analytics and Data Intelligence

AI-enabled analytics and data intelligence aren’t a panacea. While the technology can revolutionize underwriting and distribution, its implementation raises challenges for insurers.

A 2019 EIOPA study found that insurance companies faced common challenges in implementing up to date data intelligence. These challenges include:

  • Meeting regulatory requirements and staying informed of regulatory changes.
  • Addressing consumer trust surrounding AI and data security.
  • Ensuring data is accurate and that analysis and data use follow ethical standards.
  • Protecting against cyber risks, such as hacking and data theft.
  • Hiring and training staff in the face of looming skills shortages related to data intelligence.

Mere access to data isn’t enough. Maintaining the capacity to collect, store and analyze data is necessary for insurers. Without this capacity, insurers lose the opportunity to glean insights from data and put that information to use for themselves and their customers.

Another looming challenge for insurers is the skills gap between what their existing teams can do and what robust analytics and data analysis demand.

Combining capacity, tools, processes and skills will lead insurers into a data intelligence future. Currently, insurers have much work to do in order to realize that vision. One Capgemini study finds that “only 18% of insurance organizations had the tools, technologies, people, processes, skills, and culture in place to derive full value from the growing volume of data to which they had access,” write Seth Rachlin and fellow study authors.

According to the study, the top 18 percent of organizations shared several common features that drive their data intelligence success. Over 80 percent ensured that data literacy was a core skill shared across teams, not limited to subject matter experts, and 84 percent had “role-based data upskilling programs” for most of their employees. Seventy-eight percent said that their upskilling programs included not only standard artificial intelligence and machine learning topics, but also data intelligence skills like model training, course correction and maintenance.

The road ahead in analytics and data intelligence for insurers demands attention to data capacity, security and analytical tools. It also requires attention to skill-building among insurance professionals. When teams have the right skills and tools, they can make the most of data intelligence and deep analysis to improve underwriting and distribution.

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