Insurance underwriting teams: bridging analytics and intuition

When data says one thing and gut instinct says another, what should underwriting teams do? This tension sits at the heart of modern insurance operations. In a recent episode of the Underwriting Intelligence podcast, Karen Dayal, Chief Underwriting Officer for Commercial Lines at Aviva UK and Ireland, draws on over 20 years as a pricing actuary turned underwriting leader to answer that question.

Her perspective offers a blueprint: successful teams build genuine partnerships between analytics functions and underwriters, turning traditional handoffs into measurable collaboration where both sides own outcomes together. Here are the key themes from the conversation.

Key takeaways

• Backtesting builds trust over time by demonstrating measurable results where underwriters followed analytics guidance.

• Targeting skeptics rather than eager volunteers as pilot participants creates the most powerful advocates for change across the organization.

• Delivering quick wins with early measurable results closes the credibility gap left by decades of unfulfilled promises.

• Hiring for intellectual curiosity and diverse backgrounds (like accountancy) adds different communication styles and bottom-line focus to analytical teams.

• Successful insurance underwriting teams build genuine partnerships between analytics functions and underwriters with shared accountability: actuaries must own recommendations alongside the underwriters who act on them.

Why effective collaboration matters for insurance performance

When analytics and underwriting functions operate in silos, organizations waste resources and miss opportunities. Models get built that never influence decisions. Insights sit in presentations rather than shaping actual risk selection. Analytical teams produce sophisticated outputs that underwriters never adopt, creating expensive overhead with no return on investment.

Dayal emphasizes that ignoring analytical guidance renders the entire function pointless. If analytical teams deliver recommendations without understanding underwriting realities, their work stays theoretical. As a result, organizations miss chances to improve risk selection, optimize pricing, and respond to emerging market conditions.

Effective teams solve this by creating feedback loops, backtesting past recommendations to demonstrate value and building evidence that expands collaboration into new areas. This requires analysts who understand business constraints and underwriters who remain open to what the data shows.

How insurance underwriting teams balance data and intuition

Modern underwriters have access to more data than ever: external feeds, internal metrics, third-party risk scores, CAT models. The challenge isn't access; it's synthesis. The most effective analytics teams understand their role isn't to maximize information flow but to prioritize it. As Dayal describes, underwriters consistently ask for help rationalizing all those data points into clear recommendations they can overlay with their own intuition.

Friction emerges when data tells a story that conflicts with an underwriter's intuition. Teams that lean too heavily on one approach squander the value of both perspectives. Productive synthesis requires sitting down together to explain what's driving the difference of opinion, with both sides engaging openly and productively - rather than retreating to entrenched positions.

Building trust in insurance underwriting teams

Dayal outlines several practices that have helped her teams build productive working relationships between actuarials and underwriting functions.

Shared accountability

Traditional approaches created tension where models were delivered with limited shared accountability. The new approach demands actuaries own their recommendations alongside underwriters, not just offering analysis and handing it off, but actively sharing the strategy and its outcomes.

As Dayal explained on the Underwriting Intelligence podcast: "If it fails, I'm there. I'm there saying, hey, I told them with all the information I had, this is what I told them to do. So actually, it's on me. I'm not putting it on you."

This matters because underwriters carry the weight of their P&L. When actuaries share that accountability, underwriters gain confidence to act on analytical guidance even when it feels counterintuitive.

Backtesting to demonstrate value

Trust builds through evidence. Dayal describes how her team backtests recommendations: "We will say, come back in a year of our performance and we can show you that where you trusted us, the results are better for that. And then over time, you build up that kind of trust in each other."

Choosing champions wisely

When rolling out new approaches, Dayal recommends targeting skeptics rather than eager volunteers as pilot participants. A converted skeptic becomes the most powerful advocate for change across the organization.

Delivering quick wins

Teams must deliver immediate value, not distant visions. Dayal acknowledges that underwriters have heard years of promises about better data and models that never materialized. Breaking ambitious projects into phases with early measurable results changes the dynamic entirely.

Hiring for intellectual curiosity and diverse perspectives

Building effective insurance underwriting teams requires intentional hiring practices. Dayal's team moved beyond traditional hiring patterns of math graduates pursuing actuarial qualifications, emphasizing intellectual curiosity alongside technical ability. Bringing in team members from non-traditional backgrounds, such as accountancy, has added different communication styles and a constant focus on bottom-line impact.

Real-world example: insurance underwriting analytics in action

For climate risk and renewables, CAT models inform decisions about climate-related exposures, but their application goes beyond risk avoidance. The team uses exposure management capabilities to help grow their renewables book. Consider solar farms in Texas, which are particularly susceptible to convective storms that send ice balls the size of baseballs into panels. Analytical insights enable specific client advice, like slightly changing the tilt of panels to reduce damage risk. This kind of work turns underwriting from pure risk selection into consultative partnership.

Challenges facing modern insurance underwriting teams

Underwriters have heard decades of promises from pricing actuaries about better models that never materialized. Dayal acknowledges this credibility gap in underwriting transformation efforts directly, arguing it can't be closed with promises, only with results.

Excel dependency compounds the problem. Underwriters know exactly where their data lives, how to manipulate it, and how to extract what they need. That muscle memory, built over years of daily use, means new platforms that remove familiar controls face automatic resistance. The concerns are substantive:

• Losing the ability to review previous analysis and audit their own work

• Reduced flexibility to adapt processes to specific needs or edge cases

• Diminished visibility into workflows and underlying calculations

Platforms that succeed address these concerns head-on, preserving the functionality underwriters rely on while adding capabilities like version control, audit trails, and real-time portfolio visibility that spreadsheets can't deliver.

The future of insurance underwriting: AI and advanced analytics

Rather than increasing resistance, advanced AI tools have opened underwriters to greater engagement with analytics. Increasingly, underwriters are asking "show me what you've got" rather than defending traditional approaches. Underwriters recognize they risk falling behind if they don't engage with evolving capabilities, and AI tools that support rather than replace human judgment gain acceptance far more readily.

The principles that built trust in earlier analytics initiatives still apply: tight scope, rapid iteration, and demonstrated value before expansion.

How the hx platform bridges actuarial and underwriting collaboration

Collaboration between actuaries and underwriters depends on the tools they share. When models live in disconnected spreadsheets and underwriters work in separate systems, even the best intentions around partnership struggle to translate into daily practice.

The hx platform unifies both functions in a single workflow. Decision Engine gives actuaries an integrated development environment to build and deploy pricing and triage models, while Pricing & Rating surfaces those models in an interface underwriters actually use at the point of decision. Because both functions operate on the same platform, the feedback loops Dayal describes are built into daily workflow by default, rather than requiring a manual exercise.

Portfolio Intelligence gives both actuaries and underwriters shared visibility into how pricing decisions play out across the book of business. When teams can see portfolio-level impact alongside individual risk decisions, the data-and-intuition synthesis Dayal advocates for becomes practical rather than aspirational. That's the operational foundation that the partnership Dayal describes requires: a shared environment where models translate directly into decisions, and decisions feed back into better models. See how the hx platform unifies actuarial and underwriting workflows.

Frequently asked questions about insurance underwriting teams

These questions extend the themes covered in the podcast conversation with additional practical guidance.

How do insurance underwriting teams balance data analytics with traditional expertise?

Effective insurance underwriting teams synthesize analytical insights with underwriter intuition rather than choosing one over the other. When data diverges from an underwriter's gut feeling, teams sit down together to understand what's driving the difference, with both sides engaging openly to reach better decisions.

What is the role of actuaries in modern insurance underwriting teams?

Actuaries in modern insurance underwriting teams own their recommendations alongside underwriters, sharing accountability for outcomes rather than handing off models and walking away. This shift means actuaries engage with underwriting realities, market conditions, and portfolio performance rather than operating in isolation.

How should organizations structure the first year of analytics-underwriting collaboration?

Start with a single line of business or risk category where analytics can demonstrate clear value quickly. Select a skeptic rather than an enthusiast as the pilot champion, backtest results after 12 months, and use that evidence to expand into additional areas. Phased rollouts with measurable milestones build more credibility than ambitious full-portfolio programs.

What skills are most important for insurance underwriting team members?

Intellectual curiosity combined with diverse perspectives strengthens insurance underwriting teams. Beyond technical skills, teams benefit from members with different backgrounds, such as accountancy, who bring varied communication styles and maintain focus on bottom-line impact.

Why do underwriters resist moving away from Excel?

Excel resistance is about control, not technology preference. Underwriters have built years of muscle memory around spreadsheet workflows and know exactly where their data lives and how to manipulate it. Successful transitions preserve the visibility and flexibility underwriters value while adding capabilities like version control, audit trails, and real-time portfolio insights that spreadsheets can't provide.

How should organizations measure the success of analytics-underwriting collaboration?

Compare outcomes where underwriters followed analytics recommendations against outcomes where they didn't. Over time, this backtesting evidence builds a track record that both sides can reference when expanding collaboration into new lines of business or risk categories.

What is the biggest mistake organizations make when integrating analytics into underwriting?

The biggest mistake is starting too big and promising too much. Underwriters have heard decades of promises about better models and data that never delivered. Organizations that break ambitious programs into phased rollouts with quick, measurable wins build credibility faster than those that attempt full-scale change from day one.