Blog Post: The Economics of Insurance Data Programs
By Michael Schwabrow
In our previous blog posts, I have laid out the business case for why insurers should monitor the ROI of their data programs. We have touched on the people that should be involved in this effort along with ensuring that carriers are using the right data analytics technologies for their business. We will wrap up the blog series with two articles that will delve into the economics of insurance data programs and introduce the Cloverleaf ROI of Insurance Data Calculator. If you are a carrier still figuring out how to build a quality data program, hopefully, this post will be educational and valuable for you.
Spend on Insurance Data Program
To start, we will review insights about where Property & Casualty (P&C) insurance companies typically spend this budget for managing their insurance data program from a people, process, and technology perspective.
Employee Talent Acquisition
Carriers can leverage data science to craft successful customer acquisition approaches, create tailored offerings, evaluate potential risks, and support underwriting processes.
Data scientists are one of the most in-demand jobs across all industries with financial services (including insurance) as one of the top destinations for this professional. While there are more people touching insurance data than data scientists and related roles, we are only going to focus this section on these functions and not include actuaries, claims professionals, or underwriters.
The average salary for a data scientist in the U.S. is $82,640 /year according to ZipRecruiter. However, as you can see in the Glassdoor overview of the same role within Liberty Mutual the salaries exceed the range listed on ZipRecruiter reaching $172K a year. The Bureau of Labor Statistics (BLS) says the mean income for a data scientist within carriers is $113K so let’s go with that number. The BLS figures indicate that data scientists take up 0.95 percent of jobs in the insurance industry representing one of the industries with the highest concentration of data scientist employment.
If you are Liberty Mutual using the .95 number in the industry against their 45,000 employees, they are potentially employing well over 400 data scientists.
As an example of a mid-sized carrier, let us take one of the Forbes 2024 Best Mid-Sized Employers – Mercury Insurance, which employs 4,700. Mercury could employ roughly 45 data scientists.
Given that over one hundred open jobs come up when searching Liberty Mutual for data science-related numbers, perhaps these numbers are not far off. That means they could be spending over $45M a year on data science salaries, not to mention the cost to recruit, train, and retain key talent.
Process – Compliance
Prior to the advent of GenAI, insurers already had to have robust compliance programs to protect their business. Like technology spending, we cannot provide exact spending figures for insurers, but industry averages place spending anywhere from hundreds of thousands of dollars a year for a small carrier to over $100M a year for a large carrier. This could encompass employees, internal controls, and regtech.
This does not include the $308.6B annually the Coalition Against Insurance Fraud estimates costs the industry annually.
How does compliance connect with data programs?
It is circular in nature. If companies do not spend properly on the people and processes to ensure compliant insurance data programs, then they will incur fines which will in turn reduce their profitability and operating budgets. This will then shrink the amount of money they can spend on talent unless they find the funds elsewhere and a downward spiral can continue.
Technology
Where does technology fit into the equation? First, let us examine the project IT spend by insurers.
According to data provided by Gartner, global insurer IT spending will be approximately $232B in 2024. Data from Market.us has the figure even higher at $446.4B in 2024 including hardware, services, and software. A third perspective on insurer spending comes from Datos Insights which shows that 2024 will be the highest year tied with 2023 in the past decade in terms of the percentage of premium spent on IT at 4.2%. You could compare the figures in the Datos Insights chart against what the NAIC reported for average written premium in to get additional approximate spending in the U.S. going back to 2019. The Institutes also summarize some of the NAIC data here.
While we cannot apply a set figure for every carrier in terms of how they prioritize their IT budgets, we can give a glimpse into how much is spent on insurance analytics. According to data from Fortune Business Insights the global insurance analytics market size was valued at USD 12.65 billion in 2023 and is projected to grow from USD 14.50 billion in 2024 to USD 44.77 billion by 2032.
With the right technology (insurtech and non-insurtech), insurers can make up for some of the gaps in people and processes. However, properly run insurers will have these three elements seamlessly intertwined to help protect the ROI of their insurance data.
Insurer Claims and Underwriting Fraud and Loss
The last area in this article that we will focus on is Claims Management and Underwriting fraud and loss. Issues in this area are an outcome of poor strategy from a people, process, and technology perspective that leave insurers more vulnerable to spotting potential fraudulent customer behavior.
Without proper data analysis, trained employees, and technologies to spot potential fraudulent red flags, these instances can slip through the cracks and cause insurers to lose money on claims that they should never have to pay for. As I mentioned earlier it is approximately $308.6B annually according to Coalition Against Insurance Fraud.
Conclusion
This post was the setup for the final article in our series which will provide best practices in the four areas we covered today along with introducing the ROI of Insurance Data Calculator. The Calculator will be a constant barometer that insurers can rely on to determine when and where they need to make improvements in these core areas.
The best practices and tools we will unveil in our final post will be invaluable assets to help your insurance business derive meaningful insurance insights. These insights will help insurance carriers to continuously reach new heights of operational excellence, growth, profitability, and customer satisfaction.