• 86% of underwriters burn 2+ hours daily on manual data entry, while 95% of US insurers admit their pricing technologies fail to deliver, creating a $45 billion opportunity for carriers adopting agentic AI systems that achieve 50% faster submission-to-quote cycles and 2-point loss ratio improvements.
• Agentic underwriting deploys multi-agent ecosystems (intake, risk profiling, pricing, and compliance) within a mesh architecture that preserves human oversight, enabling specialty P&C carriers to scale submissions without linear headcount growth while maintaining regulatory auditability.
• Only 4% of carriers have agentic AI in production today, but 22% plan adoption by 2026—creating a first-mover advantage window for transformation leaders who pilot narrow use cases (marine cargo, cyber) and build modular data foundations before competitors close the 6.1× shareholder return gap between AI leaders and laggards.

Why Specialty Underwriting Needs a Rethink

Specialty property‑and‑casualty (P&C) insurers face some of the most complex submissions and rapidly evolving risks in the industry. When a broker forwards a 50 MB email packed with spreadsheets, PDFs and handwritten slips, underwriters may spend hours hunting for critical data, validating it and re‑keying values into pricing tools.

Hyperexponential’s 2025 State of Pricing Report shows that 86 % of underwriters still spend more than two hours each day on manual data entry, while half believe these inefficiencies limit their ability to underwrite effectively. Data chaos isn’t just an annoyance; incomplete or inconsistent records, poor formatting and ambiguous labels introduce friction and delay decisions. In a market where new exposures (cyber, climate, geopolitical) expand the surface area of risk, slow submission‑to‑quote cycles can cost business.

Hyperexponential’s survey found that 95 % of US insurers believe their pricing technology needs improvement, 47 % say platforms have not delivered on promises, and more than a third have lost business due to slow or inconsistent pricing.

Amrit Santhirasenan, co‑founder and CEO of hyperexponential, argues that the industry must move from a defensive posture—where technology is used merely to reduce costs—toward an offensive strategy where AI enhances decision‑making. As a qualified actuary and software engineer who has built pricing tools for insurers such as Aegis London, Aviva, Conduit Re and Convex, among others, Amrit has seen the inefficiencies of manual workflows firsthand. He believes that agentic underwriting—AI systems that perceive, reason, and act autonomously within human-defined parameters—offers a path to faster, more consistent, and data-driven decisions. This brief explores what agentic underwriting could look like for specialty P&C insurers and how executives can prepare their organisations for this shift.

The anatomy of agentic underwriting

Agentic underwriting is not a single algorithm; it is a multi‑agent ecosystem that coordinates tasks across the underwriting value chain. McKinsey & Company describes systems where one agent handles submission intake and data extraction, another profiles risk, a third selects pricing models and orchestrates the workflow, and others ensure regulatory compliance or capture feedback to improve models. These agents operate within a mesh architecture that allows them to plug into core systems, third‑party data sources and rating engines. Crucially, humans remain in the loop: underwriters review recommendations, override suggestions and provide feedback that the system uses to learn and improve.

The perception–reasoning–action cycle is at the core of agentic systems. First, AI perceives the submission by ingesting documents, extracting entities and flagging missing information. Next, it reasons—using machine‑learning models and actuarial logic—to assess risk factors, identify similar exposures and recommend pricing scenarios. Finally, it acts by populating the rater, generating quotes, triaging the submission or requesting additional information. A feedback agent captures outcomes and underwriting judgments to continually refine the models. This approach enables underwriting decisions to evolve in real-time as new data becomes available, while maintaining an audit trail necessary for regulatory oversight.

Hyperexponential’s Approach: From Messy Submissions to Actionable Data

Hyperexponential’s hx Renew platform illustrates what agentic underwriting looks like in practice. It handles complex submissions with AI. The Data Ingestion Library leverages large language models (LLMs) and intelligent preprocessing to ingest messy submissions, parse them and map unstructured inputs to structured fields. It can perform both one-to-one mappings (e.g., addresses, limits) and semantic inference for qualitative fields, such as multi-factor authentication adoption. Features include file pre‑screening and shrinking to reduce compute cost,s and a transparent review layer so underwriters remain in control.

What differentiates hx Renew is that ingestion is deeply integrated with the pricing workflow. Once the data is structured, it can automatically populate a rater because the platform understands the underlying schema. As pricing models evolve, the ingestion library updates its mappings without re‑engineering rules, enabling actuaries to deploy new models without disrupting downstream processes. Structured submission data becomes instantly usable, building a data asset over time and unlocking opportunities for portfolio analytics, triage automation and benchmarking.

Hyperexponential emphasises that AI should augment the underwriter rather than replace them. The platform features a review interface that allows underwriters to validate the extracted data and adjust assumptions. By eliminating manual re-keying, underwriters can focus on assessing risk drivers and negotiating terms effectively. This aligns with Duck Creek’s observation that underwriters currently spend 30–40% of their time on administrative tasks, such as data entry. In hx Renew pilots, clients have reported 50 % faster submission‑to‑quote times and two‑percentage‑point improvements in loss ratios. The platform's modular design enables actuaries to build and deploy raters ten times faster. It has helped insurers write over US$45 billion in gross written premiums across more than 40 enterprise customers worldwide.

Why Specialty Carriers Should Care: Benefits and ROI

With the rise of emerging risks such as cyber, climate or new frontiers of economic activities. Specialty underwriters compete on their ability to respond quickly and price accurately.

Agentic underwriting delivers tangible benefits:

• Speed and capacity. Automating data ingestion, triage and initial pricing frees underwriters to focus on complex judgement calls. Hyperexponential reports that faster responses directly improve bind ratios because brokers reward carriers that quote quickly. McKinsey notes that agentic systems can handle thousands of submissions, enabling carriers to scale without linear increases in staff.
• Consistency and accuracy. By structuring data and applying standardised models, agentic systems reduce subjective variation across underwriters. Duck Creek points out that agentic AI improves underwriting accuracy, leverages diverse data sources and ensures models remain compliant through human oversight. Hyperexponential’s clients have seen improved loss ratios and greater confidence in pricing decisions.
• Data‑driven insights. Agentic underwriting creates a rich dataset of submission details, pricing factors and outcomes. Executives can analyze rate changes, portfolio performance, and exposure concentrations in real-time. Portfolio‑level insights support decisions about appetite, reinsurance and capital allocation.
• Improved broker and customer experience. Faster, consistent quotes build trust with brokers, who appreciate clear reasoning and quick turnarounds. Customers may also receive more tailored terms as underwriters leverage third‑party data (cybersecurity scans, climate models, IoT sensors) to refine coverage, aligning price with actual risk.
• Talent empowerment. Underwriters become strategic advisers who interpret model outputs and shape appetite, rather than data clerks. Actuaries gain a unified platform where they can develop, test and deploy models in Python and push changes to underwriters in minutes. Hyperexponential also offers training and certification to upskill teams.

Challenges and considerations

Adopting agentic underwriting is not a plug‑and‑play exercise. Executives must address several challenges:

• Data quality and integration. Agentic systems require clean, standardised data across policy administration, exposure databases and external sources. Many carriers operate on legacy systems with siloed data. McKinsey warns that half of the AI transformation effort is spent on change management and data integration. Insurers should invest in data governance and API-driven integration layers to avoid tomorrow's legacy issues.
• Explainability and governance. Pricing decisions must be auditable and compliant. Agentic systems need transparent models, audit trails and override capabilities. Duck Creek emphasises that human oversight is essential to ensure ethical and regulatory compliance. Insurers should establish AI governance boards that include underwriters, actuaries, compliance officers, and IT professionals.
• Talent and culture. Successful deployment depends on upskilling underwriters and actuaries to work alongside AI tools. Hyperexponential provides Python training and has built a community of actuarial engineers. Leaders must also communicate that AI augments human judgment rather than replacing it.
• Incremental adoption. Overambitious transformation often fails. Hyperexponential recommends addressing specific pain points, such as submission ingestion, before tackling full automation. Start with a pilot line (e.g., marine cargo or cyber) and expand as models improve. According to a Celent survey, only 4 % of carriers had agentic AI in production in early 2025, but 22 % plan to adopt it by 2026.

Strategic Roadmap for Fortune 500 Transformation Executives

For a Fortune 500 transformation leader, agentic underwriting is both a technological and strategic journey. The following roadmap aligns with the persona’s goals and pain points:

1. Define the burning platform. Use case studies to show how slow underwriting loses deals and erodes broker relationships. Share metrics like the 86 % manual data entry burden, the 95 % dissatisfaction with pricing technology, and the 30–40 % time wasted on administration. This creates urgency and justifies investment.
2. Pilot a narrow use case. Identify a specialty line with high submission complexity and data chaos. Partner with a platform like hx Renew to ingest and structure submissions. Set clear KPIs—submission‑to‑quote time, underwriter productivity, and loss ratio—so the board can see ROI within months. The persona values 6–9 month pilot‑to‑deployment cycles; hyperexponential’s track record of partnering with carriers such as Aviva and Convex provides credible proof.
3. Build a data foundation. Invest in data cleansing, standardised schemas and API connectivity. The mesh architecture advocated by McKinsey ensures agents can plug into internal and external sources. Adopt a modular approach so components can be swapped as technologies evolve.
4. Establish governance and talent development. Form a cross‑functional AI governance board. Develop explainability guidelines and ensure underwriters can override AI decisions. Provide training in Python and AI literacy to actuaries and underwriters. Recognise and reward teams who adopt new tools.
5. Scale and extend. After demonstrating success, expand agentic underwriting to other specialty lines and explore additional agents—for example, integrating real‑time climate or cyber data. Utilize the structured data asset to develop portfolio-level insights, refine appetite, and support reinsurance negotiations. Keep human judgment central, especially in volatile or unmodelled risk scenarios.

Conclusion: A Call to Action for Transformation Leaders

Agentic underwriting is becoming a necessary operational paradigm that can transform the specialty P&C insurance industry. By automating tedious tasks, enabling real-time insights, and preserving human judgment, agentic systems promise faster growth and better risk selection. Yet success will depend on disciplined execution: cleaning data, building modular architectures, training people and governing AI responsibly. For transformation executives tasked with modernising global insurers, now is the time to pilot, learn and scale. Those who act will not only improve their combined ratios but also redefine how insurance responds to the risks of a volatile world.

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