Accenture’s Stefan Sieger and Jeevan Thangudu believe that despite strong earnings and well-capitalized balance sheets, many reinsurers are still struggling to translate favorable market conditions into sustainable earnings growth and fully realize the value of artificial intelligence.
The constraints, they argue, are no longer capital, capabilities or risk appetite. Rather, it lies in operational inertia: Legacy systems, fragmented workflows, and manual processes continue to slow underwriting decisions, precisely at a time when speed and precision have become decisive sources of competitive advantage.
In interviews with Reinsurance News, executives outlined the results of conversations with 11 senior operating leaders from six major global reinsurers in North America, Europe and Asia.
Discussions focused on the limitations of current operating models, efforts to redesign underwriting workflows, and how to deploy artificial intelligence in a live production environment.
“When the underwriting executive of a large reinsurer described their workflow as ‘like flying an Airbus 380 and changing engines mid-flight,’ he was not exaggerating,” Sieger observed.
Seager continued: “Despite strong earnings and ample capital reserves, many global reinsurers have operating systems and processes that slow decision-making at the moments when speed and accuracy matter most. We call it operational inertia. It does not threaten earnings today, but it will limit earnings growth tomorrow.”
For Thangudu, companies making meaningful progress aren’t just experimenting at the edge; They are re-architecting the underwriting workflow from end to end.
He added: “They auto-renew by default so expert time is focused on exceptions, compress quoting time from days to hours with ingestion and classification redesign, and tie underwriting decisions directly to real-time accrual and retrospective views.
“The returns are measurable: lower operating costs, higher productivity and faster, tighter capital deployment.”
Executives explained that operational inertia does not immediately erode profitability in hard markets; rather, it limits earnings growth in unstable and underinsured areas where strict speed determines who seizes opportunities.
However, as pricing conditions begin to soften and market cycles shift, the ability to operate quickly and accurately becomes even more important to maintaining margins and delivering profitable growth.
Legacy systems, fragmented data, manual underwriting steps and compliance drag reportedly exacerbate this inertia.
Executives added, “As disaster losses surge, they reinforce structural realities.
“Earnings growth is as important as financial growth. Resilience means being able to continue underwriting business with discipline, speed and pricing confidence when volatility rises. It requires operating models that scale, not slow, underwriting judgments.”
Seeger further delves into manifestations of operational inertia, noting that while reinsurance markets often appear stable on the surface, behind this stability it is built up through “day-to-day” friction.
Sieger continued, “Manual workflows dilute underwriting focus. Data quality gaps translate directly into pricing uncertainty. When submissions are formatted inconsistently or boundaries contain incomplete fields, underwriters incorporate uncertainty into pricing or delay decisions. The result is slower turnaround and lower hit rates.”
“System fragmentation forces rework across functions. Disconnected receiving systems, actuarial models, and cumulative views require repeated handoffs. Each handoff adds latency and increases the likelihood of errors.”
Thangudu said reinsurance also has structural features that make operational design central to the value of AI.
He added, “The treaty business has significant renewal volume. In many portfolios, renewals account for 90% of volume. Logical baselines are automated by default, using existing risk and claims history. Expert judgment should focus on exceptions rather than routine renewals.
“Facultative business is bespoke, broker-driven. Here, the highest value lies in triage and decision support that protects scarce expert time. Speed and consistency impact quote hit rates and portfolio quality.
“Bordereaux uptake directly impacts underwriting quality. Chaotic bordereaux and unstructured submissions can lead to uncertainty loading and obscure early signals. Standardization and lineage control are underwriting discipline, not operational hygiene.
“Retrospective and catastrophic aggregation require a trusted accumulation view. Static batch reporting cannot support dynamic portfolio turns during volatile conditions. Real-time accumulation and retroactive views improve the discipline of entry and exit decisions.
“Broker asymmetry determines competitive outcomes. Brokers often see market changes before reinsurers do. Speed, completeness and consistency impact access and selection.
“Operational redesign enables underwriters to pursue new business that previously seemed uneconomical as peak cycles were consumed by refresh rework.”
Leading reinsurers are beginning to deploy artificial intelligence at scale in select areas, specifically to enhance risk selection, improve pricing consistency and accelerate decision-making throughout the underwriting workflow, using catastrophe volatility as the ultimate stress test, executives said.
By integrating climate, geospatial, loss, exposure and market data, companies can more dynamically assess accumulation and price with greater confidence in areas where uncertainty is highest.
They also emphasized that the impact of AI is inseparable from data architecture.
The pair continued, “Modern reinsurers are investing in ingest engines that can accept any data that arrives, including documents, boundaries and contract or claim attachments. These inputs are standardized and connected to downstream systems via APIs.
“In practice, many are combining internal capabilities with external data and technology partners, reflecting a more ecosystem-driven approach to building AI-powered underwriting capabilities.”
“Together, the architecture creates real-time visibility for underwriting, claims and actuarial teams. It enables more reliable AI deployments because data lineage and ownership are clearly defined. It also produces faster, more consistent quotes and a clearer audit trail.”
Importantly, as Seager noted, none of the senior operating leaders interviewed described a goal for changing underwriters.
“Technology alone does not eliminate operational inertia. Leading reinsurers are establishing cross-functional centers that bring together underwriting, data science and technology integration. One organization described establishing a shared services center to centralize claims and technology accounting. This change improves turnaround time and accuracy while freeing up underwriting capacity,” Sieger said.
He continued, “Governance evolves as capabilities evolve. Decision-making is being streamlined so that risk ownership is clear on the first line, while second- and third-line oversight remains effective without creating approval bottlenecks that slow underwriting decisions.”
The duo concluded that reinvention has already begun, with many companies moving from experimentation to selective scaling and early production deployment. Reinsurers that align underwriting volume, data architecture, underwriting workflows, talent and governance will be better able to deploy capital with precision, an ability that increasingly defines leadership in volatile markets.
As described in this article, Accenture’s insurance research team, led by Andre Schlieker, conducted in-depth interviews with 11 senior core operating managers from six leading global reinsurance companies in North America, Europe and Asia. The interview explores operating model constraints, underwriting workflow redesign, and AI deployment in live production environments.