Drowning in Intelligence, Starving for Time
It’s 9:47 AM at a Lloyd’s syndicate specialising in political violence and terrorism cover. An explosion at a manufacturing facility in São Paulo triggers the cascade: security alerts from three monitoring services, push notifications from news aggregators, preliminary incident reports from on-the-ground sources, and a stream of social media updates. By 10:15 AM, the senior underwriter responsible for Latin America exposure has received more than a dozen discrete pieces of information about a single event.
By 11:00 AM, she’s still trying to answer basic questions: How many of our policyholders have exposure in this location? Is this an isolated incident or part of a broader pattern? Should we be proactively reaching out to brokers, or waiting for more clarity? The intelligence keeps arriving—updated casualty figures, statements from local authorities, analysis from consultants—but the operational questions remain unanswered.
By 5:30 PM, eight hours after the initial alert, the exposure assessment is finally complete. Four policyholders potentially affected. Estimated aggregate exposure documented. Communications drafted for broker outreach. The work is thorough, defensible, and… exhausting. And tomorrow, there will be another event. And another.
This is the intelligence paradox: unprecedented access to information, insufficient capacity to act on it.
More Intelligence, Less Capacity
The specialty insurance market has never had better access to geopolitical intelligence. Twenty years ago, underwriters relied on periodic risk reports and analyst briefings. Today, real-time monitoring services track incidents as they unfold. Consultancies provide granular country assessments. Data platforms offer searchable databases of historical events. The volume and velocity of available intelligence has increased by orders of magnitude.
Yet operational capacity to process and act on this intelligence has remained essentially flat—or, accounting for increased portfolio complexity and regulatory expectations, has arguably decreased. The average syndicate hasn’t doubled its analytical headcount to match the doubling of intelligence sources. The 24-hour day hasn’t expanded to accommodate the 24-hour news cycle.
The predictable response has been to add more intelligence services. Better coverage, deeper analysis, more frequent updates. But each additional service compounds the core problem: more material to synthesize, more sources to reconcile, more alerts to triage. The bottleneck isn’t the quality of intelligence. It’s the manual work required to translate intelligence into operational action.
The Translation Layer
Between “an event has occurred” and “here’s what we’re doing about it” lies what we might call the translation layer: the systematic work of connecting intelligence to context, context to exposure, exposure to decision, and decision to action.
For the underwriter in our opening scenario, the translation layer consumed eight hours:
- Identifying which policyholders have exposure in the affected area
- Determining whether the incident represents a covered peril
- Assessing whether this event signals broader deterioration
- Comparing current exposure to historical portfolio patterns
- Drafting communications that reflect all of the above
- Routing approvals through appropriate channels
None of this work is optional. None of it can be done carelessly. And almost none of it requires the senior judgment that justifies an experienced underwriter’s salary—it requires systematic application of domain knowledge to structured problems.
This is true across the specialty insurance workflow. Renewal assessments require compiling incident history for hundreds of locations. New business requires comparing submissions against existing portfolio exposure. Aggregation monitoring requires continuous synthesis of policy data and current events. In each case, the intelligence exists. The intelligence is often excellent. But the work of translation from intelligence to action is manual, time-consuming, and increasingly unsustainable.
Why Traditional Solutions Fail
The traditional response—hire more analysts—provides real value but faces fundamental constraints. Analytical talent is expensive and scarce, particularly talent that combines geopolitical expertise with insurance domain knowledge. More importantly, analytical capacity scales linearly at best. Ten analysts can do roughly ten times the work of one analyst, but they can’t do it ten times faster when a crisis demands simultaneous assessment of dozens of locations.
The alternative response—engage more intelligence services—adds volume without adding processing capacity. A underwriter subscribed to three intelligence services may have better coverage than one with a single service, but they also have three times as much material to synthesize during a crisis. The paradox intensifies: more information, same amount of time, same number of hours in a day.
Both responses treat the bottleneck as an intelligence problem. But the bottleneck isn’t intelligence—it’s the translation layer between intelligence and action.
The Actual Problem
The specialty insurance market isn’t suffering from insufficient intelligence. It’s suffering from insufficient capacity to convert intelligence into operational decisions at the speed and scale that modern portfolios demand.
This matters because the economics of specialty insurance depend on operational efficiency. Combined ratios are under pressure. Regulatory expectations for documentation and response speed are increasing. Client expectations for service have been shaped by other industries where digital infrastructure eliminated manual processing decades ago. The market can’t sustainably solve a structural bottleneck with marginal headcount increases.
The solution isn’t better intelligence, and it isn’t more analysts. The solution is eliminating the translation layer—automating the systematic work that currently consumes analytical capacity, so that human expertise can focus on the judgment calls that actually require human expertise.
That transformation is possible. But it requires recognizing that the industry isn’t facing an intelligence problem. It’s facing an infrastructure problem.
