Operational drag in insurance

How fragmented systems, manual processes and poor data quality are inflating the cost of every policy and every claim.

Insurance workflows — not just underwriting but across the value chain — are suffering from a persistent drag caused by the combination of segregated legacy systems and manual processes. Dealing with tricky bordereaux containing unstructured data, handing work off between different people and departments, and manually keying information between systems that cannot be integrated: all of this slows down underwriting assessments, delays quotations and increases the cost of handling claims.

The problem is rarely visible in any single step. It accumulates across the workflow, adding time and cost at every handoff. For most insurers, the true cost of this operational drag has never been properly measured.

Poor insurance data quality, caused by manual keying, the lack of restricted drop-down fields and missing data in the original submission, leads to uncertainty at the point of risk assessment. When underwriters cannot trust the data in front of them, they price conservatively. Higher prices lead to lower conversion rates and lost gross written premium.

This is not a technology problem. It is a process problem with a measurable commercial cost. Every percentage point of conversion lost to uncertainty driven by bad data is GWP that has walked out of the door.

Bordereaux data arrives in varied and often messy formats, with inconsistencies, missing fields and non-standard structures that require significant manual effort to ingest and reconcile. This is one of the most common sources of operational drag in the London market and across specialty insurance more broadly.

Automating the ingest and processing of bordereaux data can substantially reduce turnaround times and give underwriters a clearer, faster picture of risk. The technology to do this well now exists, and the return on investment is typically rapid.

The insurers making the most progress are not simply automating what they already have. They are rearchitecting how work flows between people, systems and AI, and building the enterprise data foundations that make it sustainable. That means looking at the end-to-end workflow, identifying where handoffs, rekeying and manual reconciliation are creating delay, and redesigning the process around the data.

This approach delivers compounding benefits: faster processing, lower cost per policy, better data quality feeding back into more accurate pricing, and a more scalable operating model.

You do not need to overhaul your entire operating model to start reducing operational drag. The most effective first step is to identify the specific points in your workflow where manual processes and data quality issues are creating the most cost and delay, quantify the impact, and address the highest-value bottlenecks first.

Red Olive works with insurers to diagnose and reduce operational drag through smarter data handling, automated bordereaux processing and workflow redesign. Talk to us about where the drag is costing your business the most.