For years, insurers poured resources into modernizing consumer interfaces and touting their improvements. Meanwhile, underwriting remained largely untouched.

It was slow, stable and resistant to evolving. The lack of change was probably a source of comfort to tech-wary executives. A submission would come in. Requirements would be exchanged. Eventually, a quote would follow. The system wasn’t broken. Perhaps healthy margins were counterproductive, dulling any urgency to modernize underwriting and forcing leadership to ask: Why fix what isn’t broken?

That inertia of inaction came with a hidden cost. Underwriting operations remained reactive and increasingly outdated compared to industries like banking, where back-end processes embraced advanced algorithms and automation years ago. Instead, the function of underwriting fell behind—a lagging microcosm of the broader insurance industry.

That’s changing fast.

Why Change Can’t Wait: Four Pressures Forcing Transformation

Several converging forces are making the status quo untenable:

• First, the nature of risk itself has evolved. Climate volatility, cyber risk, supply chain fragility and geopolitical unrest have made risk harder to model and more dynamic than ever. The old playbook, analyzing static data and making point-in-time decisions, no longer keeps pace with how risks emerge, escalate and evolve.

• Second, customer expectations have risen, demanding faster, more tailored underwriting decisions. They compare their insurance experience not to other insurers but to digital interactions they have with banks, retailers and technology companies.

• Third, the volume and complexity of data have exploded. While data availability isn’t the problem, its fragmented, unstructured nature creates a flood of PDFs, spreadsheets, emails and broker submissions with no unified standard. Underwriters often sift through 10 or more systems daily just to assess a single risk.

• Finally, a talent crisis looms. Underwriting veterans are aging with deep institutional knowledge that walks out the door as retirement accelerates. Meanwhile, the next generation may not be interested in an industry still seen as needing to catch up, technologically speaking. Put plainly, the world’s best tech talent is not heading toward the insurance industry to propel their individual careers. They can choose instead to work for companies at the forefront of technological innovation, potentially offering more competitive compensation—i.e., the Magnificent 7 (Microsoft, Amazon, Meta, Apple, Alphabet, Nvdia, Tesla).

Without modernizing, insurers risk falling into a vicious cycle: outdated processes drive talent away, making modernization even harder.

The New Underwriting Model: Proactive, Data-Rich, Customer-Centric

Leading insurers are redefining the role of the underwriter—moving from passive risk assessment to proactive, insight-driven decision-making powered by data, AI and automation. Traditionally, underwriting was reactive: evaluate risk after submission, apply rules and deliver a quote. Today, the focus is shifting toward continuous risk monitoring, real-time insights and pre-claim engagement.

Borrowing lessons from banking’s use of credit scoring and real-time fraud detection, insurers are integrating internal and external data across product lines—home, auto and health—to build holistic, dynamic risk profiles. Some are even making these risk scores customer-facing, akin to a credit score, encouraging risk-mitigating behaviors.

Technology is accelerating this transformation. AI-driven triage tools now evaluate submission quality, broker behavior and external risk signals before the underwriter even touches the file. Generative AI tools augment humans—summarizing submissions, highlighting gaps against underwriting guidelines and recommending next-best actions. Yet, human oversight remains essential. Insurers are embedding “human-in-the-loop” controls, allowing underwriters to validate AI recommendations, exercise judgment and build trust in decision outcomes.

This evolution is not about replacing underwriters. It’s about freeing them to focus on higher-value work: nuanced risk selection, strategic broker negotiations and active customer engagement. Leading carriers are also embedding underwriters into broader risk advisory ecosystems, collaborating with third-party partners like ADT, Nest or IoT providers to proactively reduce risk.

The path forward will reward insurers who enable underwriters with the right tools, data and operating models—turning underwriting from a back-office function into a strategic growth engine.

What’s Holding Insurers Back?

While the technology to enable proactive risk management exists, execution gaps persist—rooted in both organizational and technical challenges. Many insurers remain hesitant to fully digitize underwriting infrastructure, often viewing technology investment as discretionary rather than foundational to future growth and profitability.

Talent remains a critical bottleneck. AI specialists are in short supply, and the insurance sector struggles to compete with tech and finance for top talent. However, as modernization gains momentum, a positive feedback loop could emerge: modernization attracts talent, which in turn accelerates transformation.

Yet, insurers taking bold steps, like offering third-party data services for free in hopes of lowering claims, face hurdles. Data remains fragmented across product lines, making even basic customer unification, such as linking home and auto policies, difficult.

Standardizing incoming data, especially from unstructured broker submissions, remains foundational. Without clean, structured inputs, advanced decision systems falter.

Equally important is building underwriter trust in AI recommendations—requiring governance models, auditable decision logic and clearly defined “ground truth” datasets to measure AI performance.

Finally, technology ecosystems remain siloed. Even when AI tools are deployed, their impact is limited if they aren’t integrated into core policy, pricing and CRM platforms. Sustainable change will come from aligning people, processes and technology around clear business objectives like speed, precision and profitability.

Rethinking Underwriting Priorities: Not Every Quote Request Deserves a Quote

In the case of non-captive brokers, one persistent challenge in underwriting is deciding which quote requests are worth the time. A key filter is the broker behind the submission. Underwriters routinely ask, “Has this broker brought us profitable business in the past? Do they actually place coverage with us, or just use our quotes to negotiate better deals elsewhere?”

With market conditions shifting to be more buyer-driven, carriers must evaluate more submissions without adding headcount. AI-enabled submission triage is becoming essential—helping underwriters quickly identify which risks merit attention based on broker history, submission quality and profit potential. By generating comprehensive assessments of intermediary behavior, AI allows underwriting teams to prioritize submissions more effectively. Brokers predicted to deliver profitable business can receive faster turnaround times, more competitive pricing (within reason), and greater engagement from sales and underwriting teams.

Automation Isn’t Replacing Underwriters—It’s Freeing Them for Higher-Value Work

More automation in underwriting doesn’t mean a colder, more impersonal process. In fact, it can enable the opposite. When algorithms handle the initial pricing, based on historical patterns and third-party data, underwriters are freed to focus on nuanced risk evaluation and customer engagement. This creates room for negotiation, where an underwriter can say, in effect, “Help me help you lower your rate.”

This isn’t a binary shift; it’s situational. For some policies, algorithmic quoting is sufficient. For more complex policies, such as specialty lines, “straight through processing” remains the exception, not the rule. Here, human judgment remains essential while being amplified by AI tools.

Lastly, timing plays a key role: the longer a risk sits on the market, the less likely it is to bind. Underwriters must balance speed against precision. This is also known as “option value decay.” AI helps identify and fast-track high-value opportunities before they expire.

The Tech Stack Behind the Shift

The new underwriting engine is fueled by a rapidly evolving tech stack:

• AI and machine learning models for submission triage and risk scoring.
• Telematics for dynamic auto pricing.
• IoT data streams for property risk monitoring.
• Data-sharing for secure, efficient data distribution across the insurance value chain.

At the core is AI and machine learning, used to recognize patterns, generate predictive risk scores and triage submissions with unprecedented speed. For example, a model trained on historical property claims might learn that new quote requests for similar properties involving older multi-family buildings in specific ZIP codes, combined with gaps in roof maintenance data, correlate with higher fire and water damage losses, prompting closer review or adjusted pricing.

In auto, telematics is also reshaping how risk is priced, tracking driver behavior moment-by-moment rather than relying solely on static inputs like age or ZIP code. And while not yet fully operational, blockchain has been proposed as a way to streamline data sharing across the insurance ecosystem, from reinsurers and brokers to the clients themselves. These modern tools enable underwriters to spot loss drivers that were once buried in fragmented data.

Yet technology alone isn’t enough. Carriers must also:

• Invest in data transformation pipelines to turn messy inputs into decision-ready formats.
• Establish AI governance frameworks to ensure transparency and traceability in decision-making.
• Redesign workflows and role definitions to integrate AI outputs into daily underwriter routines/

This means treating underwriting modernization as an end-to-end transformation—not just a tech upgrade.

Underwriting as the Catalyst for Broader Customer Engagement

Perhaps most exciting is underwriting’s new role as a driver of customer engagement and growth. With better data and more frequent customer touchpoints, insurers can anticipate life events and cross-sell opportunities.

A safe driver, flagged by telematics, might be a strong candidate for life insurance. A renter transitioning to homeownership presents a timely opportunity for bundled coverage.

With richer, more actionable data, underwriting now plays a frontline role in being a more strategic business enabler and revenue driver. When done right, underwriting becomes more than a risk gatekeeper. It becomes a gateway to holistic customer relationships. In other words, insurance stops being a passive safety net and becomes an active presence in the customer’s day-to-day life.

The Stakes: Modernize or Lose Relevance

The underwriting transformation is underway but far from complete. While pilots in AI triage, telematics pricing and automated quoting are emerging, most carriers remain stuck in foundation-building: cleaning data, hiring data scientists and running isolated experiments. The missing link is scale—turning promising pilots into enterprise-wide, production-grade platforms.

Winners will be the carriers with scale, capital and the conviction to treat technology as core infrastructure—not a discretionary spend. These firms invest as a percentage of revenue, run formal innovation cycles, and build strategic partnerships to operationalize AI across underwriting, claims and customer engagement.

By contrast, laggards risk becoming runoff businesses. That is, able to maintain legacy books but incapable of competing for new growth. Without speed, precision or sustained customer connection, their relevance will erode in a marketplace that increasingly rewards data fluency and responsiveness.

Yet, early signals are promising. Benchmarks show that higher tech spend correlates with stronger top-line growth and higher policyholder retention, especially in personal lines.

The future is coming into focus: AI-powered risk platforms will replace email-driven workflows with continuous, real-time data exchange. Underwriters will proactively target high-value segments, make faster pricing decisions, and optimize capacity based on broker behavior and risk profiles.

The era of fragmented underwriting is ending.