There is a version of customer service that most insurance companies describe in their annual reports and marketing materials. Knowledgeable representatives, fast resolutions, proactive communication, a team focused on making things easier for the people they serve.

Then there is what those teams actually spend most of their day doing.

A significant portion of insurance customer service work has nothing to do with serving customers in any meaningful sense. It is data work. Reconciling a coverage detail that does not match between the carrier’s system and the agent’s records. Confirming a policy status that should already be visible to the person asking but is not, because the system they are looking at has not been updated yet. Reissuing a document because the version on file has the wrong mortgagee name.

None of this required a customer service representative in any logical sense. It required accurate data, delivered to the right place, at the right time. The representative is there because the data was not.

The way this accumulates is worth understanding. Each individual task is small. A correction takes a few minutes. A verification call does take longer but not much. A document reissue adds some back-and-forth but eventually resolves.

The problem is volume.

Across a large carrier or servicer operation, these small tasks arrive continuously, and the teams handling them spend a meaningful share of their working hours on work that exists purely because information moved incorrectly or incompletely somewhere upstream.

The customer experience on the other end of this is its own problem. A borrower calling to confirm their insurance is active does not know they have reached a team whose day is largely structured around compensating for data gaps. They experience wait times, transfers, requests to call back, and in some cases contradictory answers depending on which system the representative happens to be looking at. The frustration is real even though nobody on either side of the call is doing anything wrong. The system is just working exactly as it was designed, which is the part worth paying attention to.

Data errors in insurance tend to originate in a small number of predictable places. Lienholder information is one of the most common sources (the same financial institution appearing under dozens of slightly different names and addresses across carrier systems, which means notifications and documents routinely go to the wrong place or worse fail to arrive at all!).

Policy updates that get entered in one system but do not propagate correctly to the others connected to it are another. Manual data entry at any point in the workflow introduces the possibility of an error that will require human effort to find and fix later.

The irony is that most of the information needed to prevent these errors already exists somewhere in the ecosystem. The correct lienholder address is in the servicer’s system. The accurate policy status is in the carrier’s system. The updated coverage details are in the agent’s platform. The data is there. It is just not moving between systems reliably enough, or quickly enough, to prevent the downstream failures that customer service teams then spend their time resolving.

Automation and real-time data exchange have reduced this problem in the organizations that have invested in them. When a lienholder can submit a correction directly through a digital platform (rather than calling a service line and waiting for a representative to manually update the record), the correction happens faster, more accurately, and without consuming anyone’s time on either end of the process.

When policy updates propagate across connected systems the moment they occur, the verification calls asking whether a change has taken effect largely stop, because the answer is already visible to whoever is asking.

What tends to hold this back is not a shortage of available solutions but rather the accumulated assumption that data cleanup is just part of the job, a background cost of running an insurance operation that has always been there and will always be there. Teams get sized around it. Workflows get built around it.

And because it is distributed across enough people and enough small tasks that no single number ever captures the full cost, it rarely gets treated as the operational problem it actually is.

The representatives answering those calls are not the inefficiency. They are the symptom of one.