AI and the Modern Data Flow

We now have the ability to look at data differently, leveraging innovations to identify risk, extrapolate insights and see the bigger picture big data offers.

Blue wisps of light

If you compare a 1922 Ford Model T with a 2022 Tesla Model S, it’s easy to focus on the differences.  However, since the inception of the automobile, there are some basic features that have endured—there are still four wheels, seats and a steering wheel, and the goal is still moving people safely from one destination to another. The innovations made over the last 100 years have made this core structure safer, faster and more efficient.

Insurance—at its core—is all about data processing. Methods for collection and analysis have become more sophisticated over the years, but data still drives everything in the industry. And now we have the ability to look at data differently, leveraging innovations to identify risk, extrapolate insights and see the bigger picture big data offers. Digital capabilities have moved risk research beyond paper forms and phone calls and allow complex real-time comparisons to power more informed and strategic decisions.

Insurance carriers have long been early adopters of new technologies to aid in data procurement and computation. Much like automobile production, the insurance industry is continually streamlining its collective production line, moving from punch card tabulators in the mid-20th century to be one of the first and most prolific adopters of computerization.

This pursuit gave rise to the insurtech industry, which specializes in innovations to better identify risks and opportunities for insurers and insureds. Artificial intelligence (AI) is the next logical step in the evolution of insurance data collection and analytics. In just the last few years, AI and machine learning algorithms have provided commercial insurance carriers a much faster, more thorough and more accurate depiction of business risk. AI helps expand, deepen and interpret the range of available data sources, making commercial risk more searchable, accessible, and rapidly actionable. 

In commercial insurance, it’s often the first quote returned that binds the business. The digital age has trained consumers to expect immediate responses to their inquiries. According to the Salesforce 2021 State of the Connected Customer Report, 88% of customers expect companies to accelerate their digital initiatives to keep pace.  

See also: How AI Can Solve Prior Authorization

A Tailored Fit for Bespoke Strategies

More and more leading insurance carriers are trying on AI for real-time data and advanced analytics to reimagine risk evaluation, enhance efficiency, optimize rate-setting, improve customer experience and customize offerings. 

AI and machine learning programs have shown to fit perfectly within the insurance business model. The proliferation of insurtech options offers a clear path forward to optimize all aspects of the insurance lifecycle with analytics and predictive data technology, benefitting insurers and insureds alike. As an example, with just a business name and address, the Planck platform provides real-time business data, customized insights, submission validation, submission prioritization, risk-scoring, risk management and automation options for straight-through processing.    

The ACORD 2022 Insurance Digital Maturity Study, based on an analysis of the 200 largest insurers in the world, indicates an unambiguous connection between digitization and insurer performance. In a recent interview, ACORD President and CEO Bill Pieroni said, “Digitization is the key enabler for the vast majority of strategic imperatives and allows insurers to address them in a timely manner.” And, according to Deloitte’s 2022 Insurance Industry Outlook, which includes 424 insurance respondents from around the globe, 74% expect to increase spending on AI. 

In 2015, I contributed to an article for the Harvard Business Review, “Know What Your Customers Want Before They Do,” where I warned that “the technologies and strategies for crafting next best offers are evolving, but businesses that wait to exploit them will see their customers defect to competitors that take the lead.” What AI truly offers commercial insurance carriers is a distinct competitive advantage to better understand and service businesses. It is possible to know the risks before your customers realize them—or before something happens.

Over the past few decades, our increasingly digital economy has dramatically expanded the amount of both available data and data sources. Billions of internet users around the world contribute daily to this expanding online library—including photos, blogs, customer reviews and social media posts. Anyone can access this data, but the future of commercial insurance is being guided by those equipped to read the insights.  

See also: Insurtech Success Stories: Still Waiting for Godot

Crowd-Sourced Data and The Parable of the Ox

I first discovered The Parable of the Ox in “The Wisdom of Crowds” by James Surowiecki. I’ve seen several industry-specific iterations and parodies since, but the main story beats are always the same. A contest challenges hundreds of people to guess the weight of an ox. While some guesses came in too high and others too low, the average of all the guesses submitted was almost exactly right. By connecting individual data points, a reliable response method emerged. This process was taken a step further by creating models of what the submitted guesses might be and using those data points to predict the correct response.  

AI and machine learning models create the same revelatory inroad, but on a much larger scale. The Planck platform, for instance, finds all relevant business data in real time and refines the information into valuable insights. Machine learning amplifies the process by modeling and creating additional risk insights and building a gold standard. 

Using big data to leverage crowd wisdom at this level would be impossible through manual research. Digital assistance creates these opportunities for bespoke solutions. Underwriters are still making the decisions, but with an enormous amount of help to support their process.

For example, one carrier employed a basic randomized formula to select organizations from their written policies for a post-bind audit. This audit would identify opportunities to cancel or propose changes to coverage, limits or rates. Because this was a manual process—taking about two hours per policy—they were only able to audit a small percentage of their book.

Using an AI-based scoring algorithm, Planck was able to look at the entire book of business to model and identify the businesses most likely to require underwriter follow-up. This exploration created significant process efficiencies, identified new revenue opportunities and generated value for the carrier and their insureds.  

Data superpowers can extend further into the customer experience by offering preemptive guidance to mitigate risk. According to a 2021 report from Beazley, insurance customers are looking for more from their insurance partners to meet their changing business needs. If a new restaurant wants to remain open until 2:00 AM, you could use AI to collect surrounding business data, police reports, police station proximity and other data points to offer insight into potential risk of assault or criminal activity. These insights could be applied to other areas of the policy lifecycle, as well, such as market research, prospecting and renewals.

As early adopters of digitization, the insurance industry clearly recognizes the value of capably mining and refining big data. However, that early adoption can often be a significant impediment in the form of legacy systems. The first step in the process is finding a vendor that aligns with your business and taking them for a test drive. See how the provided solution can be applied to your approach to customer acquisitions, submissions, underwriting and renewals.   

Quality insurtech solutions aren’t meant to replace existing systems—or underwriters, for that matter. Rather, they augment process and capability to handle modern data flow. The goal remains the same, but faster, more accurate and more efficient. And digital laggards are likely to be left in the dust by insurers with strategies driven by AI. 

Leandro DalleMule

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Leandro DalleMule

Leandro DalleMule is general manager, North America, at Planck.

He brings 30 years of experience in business management to the team. Prior to Planck, he spent six years as AIG's chief data officer. He also was the senior director of big data analytics for Citibank and head of marketing analytics for BlackRock and held leadership roles in Deloitte's advanced analytics practice.  

He holds a B.Sc. in mechanical engineering from University of Sao Paulo, Brazil, an MBA from the Kellogg School of Management and a graduate certificate in applied mathematics from Columbia University.


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