Technology is rapidly transforming how insurers and brokers approach their work. At its core, however, the insurance industry continues to rely on one consistent principle: Better data leads to more accurate risk calculations, which in turn lead to higher profits.
Today, a number of technological tools help insurers and brokers access better data than ever before. Artificial intelligence and machine learning can analyze vast datasets in new ways, while predictive analytics can provide powerful new insights. Brokers who leverage predictive analytics find their role transformed; those who embrace the transformation stand to build better relationships with customers and insurers.
Artificial intelligence (AI) is revolutionizing the work of insurance companies, agents and brokers. AI is a large family, encompassing a number of tools. Some of these are adapted to spot patterns in data or guide users through tutorials. Others, like predictive analytics, focus on anticipating the future.
Predictive analytics allows its users to combine large historical datasets, statistical algorithms and machine learning tools to make clear, accurate predictions about future events.
“Predictive analytics are tools that use data to help you see into the future. But it’s not a crystal ball. Instead it tells you the probabilities of possible outcomes. Knowing these probabilities can help you plan many aspects of your business,” writes Michael Zunenshine at CRM.org.
As predictive analytics have developed from the data mining of past decades, “we’ve seen an evolution of greater granularity and richness of data, new sources of data, and more cost-effective technologies for storing, processing, and analyzing that data,” says Peggy Brinkmann, an actuary at Milliman.
Today, predictive analytics makes it possible to analyze data sets too large for humans to analyze effectively on their own. By tackling analysis of large data sets in order to predict trends, predictive analytics provides a new perspective on customers’ personal data.
“Such personal data can complement the traditional sources used in insurance... to generate real-time insights about a person’s lifestyle and habits that can be used to create a competitive advantage,” writes Alex Gayduk, CEO at flight insurance provider Panzly and at insurtech company Fortifier. It can also be used to help insurers and brokers predict what a customer will likely do or encounter next.
Predictive analytics takes data a step beyond merely sorting or organizing, as in a database. Yet it stops short of recommending what the user should do with the insights it generates. That step is left to the expertise of the person employing predictive analytics.
“Outdated or bad data results in 46% of companies making bad decisions that can cost billions,” writes digital transformation consultant Douglas Karr, founder of the Martech Zone. Access to larger data sets can ensure that more situations and information points are accounted for, leading to better decision making.
When a data set becomes too vast for humans to analyze on their own, however, the quality of decisions made from the data depends not only on which data is included, but also on which tools are used to analyze it. Efforts to analyze big data with AI begin by focusing on pattern-spotting within the existing data; predictive analytics focuses on making data-informed projections about what will happen next, based on what is already represented in the data set.
For example, predictive analytics might help an insurance company, agent or broker monitor claims history in a particular neighborhood or business district and predict what type of claims a business is most likely to see. Predictive analytics can also examine construction costs and weather patterns, allowing users to predict both risk and prices more accurately.
Armed with this information, brokers can better assist customers in finding the right insurance coverage. As predictive analytics becomes commonplace, more brokers will use the tool.
Some insurers are already embracing the power of predictive analytics. “On average, leading analytics-driven [insurance] companies spend almost five times more money than their peers do on advanced analytics solutions,” writes Mark Rusch, vice president of insurance at GoodData. The right combination of analytics and data sets thus create an advantage for insurers and brokers that invest in these tools.
As more insurers and brokers embrace predictive analytics, they change the way insurance does business as well.
By 2030, according to McKinsey’s Ramnath Balasubramanian, Ari Libarikian and Doug McElhaney, there will be fewer insurance agents and brokers. Those who remain will have adapted to the use of predictive analytics and other technologies that handle many jobs long done by hand, from filling out forms to calculating risk.
Agents, brokers and other insurance professionals will “become more adept at using advanced technologies to enhance decision making and productivity, lower costs, and optimize the customer experience,” they write.
To transition along with the technology, insurance brokers will need to focus on adopting a relational and educational role in addition to understanding new tools. The human elements of connecting customers to the insurance they need will deepen as AI takes on more of the technical and administrative aspects of a broker’s role.
Predictive analytics are already changing the way brokers think about risk, coverage and costs. Using predictive analytics can help brokers change their approach to building strong customer relationships as well.
Predictive analytics and similar tools disrupt not only how insurance brokers do their work, but also how they think about the insurance business. Traditionally, insurance has relied on pooling customers with similar coverage needs in order to distribute risk, balancing customers who face catastrophic losses against those who don’t suffer such loss.
Tools like predictive analytics, however, demand a more personalized approach to insurance. “The tremendous volume of data and the personalization promise through accurate individual prediction indeed deeply shakes the homogeneity hypothesis behind pooling,” write researchers Laurence Barry and Arthur Charpentier in a 2020 article in Big Data & Society. Instead of seeing customers as examples of a general, homogenized set of risks and needs, predictive analytics creates both the challenge and the opportunity for brokers to see their customers as individuals.
At the same time, predictive analytics and other tools are shouldering more of the routine yet often tedious work traditionally handled by humans within an insurance broker’s office. Workflow automation, for example, can handle tasks like filling out forms and gathering key documents, writes Paula Williams, content director, robotic process automation at IBM.
With more of the routine administrative tasks out of the way, insurance brokers facing the challenge of personalizing their approach to customers also benefit from the free time and mental energy available to do so. Artificial intelligence can automate many tasks, freeing up time and resources for brokers to focus on what artificial intelligence cannot do: Build genuine human connections, based on understanding a customer’s unique needs, educating the customer about their risks and providing support at crucial moments.
Insurance companies have already begun embracing AI-enabled tools, including predictive analytics, to enhance underwriting and improve customer relationships. In fact, insurers that lag behind in this area could face serious adverse consequences.
“Insurers that continue relying on traditional ways of underwriting could start a negative spiral that would be difficult to reverse. They may face adverse risk selection, could drop off preferred lists of distribution partners, and may have a more difficult time recruiting and retaining skilled professionals,” write Britton Van Dalen, Kelly Cusick and Andy Ferris at Deloitte.
Brokers who continue to work with these insurers could experience similar effects on their own work. Those who embrace predictive analytics, however, can build stronger customer relationships that ultimately benefit everyone involved in the placement of insurance coverage.
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