The Value of Data in the Modern Insurance Value Chain

The year was 2017 when long-time, well-respected publication The Economist decided to shake up the whole world with a bold proclamation on the front cover of its May 5 edition. The world’s most valuable resource was no longer oil, it read, but data.

It might have shocked the world and really hacked off the petroleum producers, but this revelation was nothing unheard of in the insurance sector.

Our business has long been a complex web of interrelated entities – from brokers, agents, and wholesalers, to MGAs, carriers, and reinsurers. And while money is the end goal for most of those key stakeholders, we can easily make the assertion that data is the powerful currency we possess. It allows us to pool resources, share knowledge, and plan for what’s coming next. 

Historical Perspective on Data in Insurance

In the foundational years of the insurance industry, data was primarily recorded and evaluated manually.  It was a treat for long-term stat lovers to get their hands dirty with, but didn’t have a lot of value at the moment. You might take months to compile it, weeks to analyze it, and even more time trying to explain the results to the average layperson in the industry.

As the sector expanded, the demand for accurate, readily available, and actionable data surged. Data isn’t a parlor trick that only big firms are able to harness.  It’s the backbone of any company’s ability to separate itself from the competition and carve out a niche to do business in.

 From setting premiums and evaluating risks to detecting fraud and managing customer relationships, data’s role is expansive. The sector leans heavily on bordereaux, primarily for data sharing among trading partners throughout the value chain. Unfortunately, transactional data doesn’t always provide a comprehensive financial understanding of risk portfolios. This discrepancy, coupled with manual processes that involve data sharing, often results in quality inconsistencies and incomplete datasets. Such manual processes not only increase overheads, but also place undue pressure on distribution entities and carriers, who need high-quality data for diverse functions ranging from financial reporting to regulatory compliance.

The Rising Importance of Data

While humans are still steering the ship, an overwhelming amount of our decisions are being guided by data. Not just in insurance, but across the board. When’s the last time you flew on a commercial airliner? Would it surprise you to learn that the pilots are only responsible for taking off and landing on most flights? The rest is done by an automatic pilot, and that automated system is being fed millions of data points to do things as safely and smoothly as possible.

In the modern age, defined by digital transformation, the role of data has ascended to unprecedented heights. Beyond serving as a transparency tool, data is instrumental in bridging capital and risks. The ongoing macroeconomic conditions, characterized by increasing loss rates influenced by inflation and catastrophic occurrences, have catapulted specialty underwriting into the spotlight. This focus has spurred the emergence of specialized MGAs, program administrators, and fronting companies. These entities masterfully blend underwriting expertise, precise risk selection, digital distribution prowess, and capital capacity. The insurance domain has become an attractive space for capital due to its potential for consistent returns on equity. Given the increasing importance of these entities, it’s evident that exchanging information in a seamless and secure manner is crucial.

Technology’s Role in Bridging Gaps

As specialty risk MGAs, program administrators, and fronting carriers have become more prevalent, various technological solutions have emerged to support them. While concepts like blockchain promise secure and transparent data exchanges, the immediate need is for reliable data platforms that guarantee consistent information throughout the value chain. Advanced analytics tools, underpinned by AI, offer previously unimaginable insights.  Machine Learning (ML) has proven time and again that it is capable of finding patterns in dense Big Data sets that might take humans years to find, or that they might never find at all.

These tools have the potential to revolutionize risk assessments and policy pricing, provided the underlying data is consistent and dependable.