The first wave of the insurtech revolution has crested, and we are getting a clearer picture of the hits and misses. In the last decade or so, the industry has seen massive strides in digital distribution, API connectivity, embedded insurance, and agency management system modernization.
What did we miss? A lot. And our misses are piling atop one another, slowly creating a precarious house of cards.
The Insurtech Problem
Specifically, producer data quality, ownership, integration, and digitization have been largely ignored as companies race to integrate the latest and greatest tech. We've modernized quoting, underwriting, and customer-facing tools, but the producer data layer that powers distribution has yet to be addressed.
Currently, there is no single "source of truth" for producer identity, hierarchy, or compliance. Fragmented data lies scattered across carriers, MGAs, state records, and compliance systems. The reality of this information chaos is outdated records, manual input processes, duplicate and incomplete records, and severe compliance risk.
Why the First Wave of Insurtech Didn't Fix It
You may ask how a multibillion-dollar industry that the entire country relies on seemingly overlooked these critical issues. The answer lies in the complexity of the insurance industry.
The initial capital injection the industry saw followed revenue, not corporate infrastructure. We know from watching elections play out that infrastructure isn't an exciting topic, and it doesn't make for an enticing pitch to the electorate or the C-suite. Furthermore, organizations already suffered from decades of operational disorganization with highly complex layers. Remember, this isn't a sleek front-end problem, but a back office foundational issue.
Perhaps most importantly, the industry has failed to establish any clarity of data ownership. There is no standardization of which pieces are owned by carriers vs. agencies vs. MGAs. The results have led to fragmented chaos.
Understanding the Business Impact
Many organizations are loath to admit these issues have resulted in hits to revenue from multiple angles. Profit loss occurs from producers not being in compliance with state laws, commission errors, slow onboarding of new producers, data bottlenecks, and distribution channel conflicts.
The industry as a whole is exposed to compliance and regulatory risk. Companies spend large amounts of money trying to mitigate inconsistent licensing verification while navigating the minute compliance laws of 50 different states. Even with a seasoned compliance team, companies of all sizes are opening themselves up to grueling audits and costly fines.
The industry as a whole suffers from distribution inefficiency manifesting itself through slow carrier onboarding and appointment, manual credentialing processes, repetitive manual data entry across multiple systems, and cumbersome systems that either don't interact with each other or do so very poorly.
These issues are ultimately compounded through analytic errors. Across the board, we see inaccurate production reporting resulting in organizations that are unable to properly measure producer performance. In many cases, leadership is operating off of incorrect or skewed information, potentially leaving millions, or billions, on the table.
AI is Poised to Exacerbate the Issue
Every industry conference and corporate event is abuzz with AI adoption. How can it be harnessed? How will your company use it? Who will be the AI winners? The truth is we are all set up to lose.
AI models are only as good as the information that feeds it. Distribution analytics rely on accurate producer hierarchies, meaning that automated commission systems will surely crack under inconsistent data. AI promising fraud detection and compliance screening is doomed to fail without consistent accurate data. Under these circumstances AI will most likely present hallucinations and bad outputs placing companies at greater risk.
The Answer Exists, But Adoption is Needed
There is good news. We know what the industry must do to reduce risk and prevent AI collapse. Organizations need a standardized producer identity layer to ensure successful AI adoption. Defined accountability in the industry would go a long way, and it is something we need to address, but for now companies must implement their own systems.
These solutions exist and many in the industry are confronting the issue head on. But it would be foolish to chase after the AI boom without shoring up your foundations.
