How granular underwriting data powers advanced analytics capabilities

Insurers have long known that data is the foundation of effective underwriting. But too often, the reality falls short of the ambition - submission data arrives unstructured, clean-up is manual, insights are fragmented across systems. Underwriters and actuaries end up spending more time fixing data than using it - leaving critical decisions under-informed, and portfolios vulnerable.

We’ve explored the downstream impact of poor data capture and cleansing: inefficiency, inconsistency, and missed insight. But clean, structured data is just the start. What truly unlocks performance is what happens next—when that data flows seamlessly into the pricing model itself.

The hidden cost of fragmented analytics

Even in well-resourced carriers, teams often rely on post-bind reports and retrospective analysis, which creates four major challenges:

1. Manual, time-consuming reporting

Actuaries routinely burn hours stitching together data to run simple tests or generate reports. It’s not just inefficient - there’s also a massive opportunity cost, taking time away from higher-value strategic work.

2. Inconsistency and governance risks

When data is manually pulled and interpreted, results vary. For example, individual teams may be building their own logic into spreadsheets, and when there’s just local workarounds with no single source of truth - governance gaps and potential for errors are rife. Without a consistent data foundation, insurers risk introducing yet another layer of uncertainty into their business—blind spots that make it difficult to assess portfolio-level risk holistically and proactively. It's not just an operational inefficiency; it's another area where unknown risk can creep in undetected.

3. No live view of portfolio performance

Most insurers lack the ability to see the real-time impact of a new policy on their broader book of business. They bind the risk on the merits of the individual risk, which in itself is not a bad thing. However, the danger arises when the risk is underwritten without understanding the impact on the wider portfolio. For example, underwriting another risk in an area where they are already overweight from an aggregation point of view. Without live, in-model portfolio context, underwriters might miss opportunities to diversify geographically (inadvertently compounding catastrophe risk where they should be shielding against it) or undermine their target loss ratio.

4. Limited MGA transparency

For MGAs, the stakes are even higher. Carriers increasingly expect granular, credible data that demonstrates underwriting discipline and risk selection quality. With structured data and real-time insights, MGAs can present richer reports, build trust with partners, and showcase their ability to manage portfolios with precision. This confidence and credibility directly impact their ability to retain and grow capacity over time.

What does structured, in-model data make possible?

With platforms like hx Renew, structured data isn’t just captured—it’s embedded in the model and accessible at the point of pricing. That enables a new class of analytics capabilities, right where they’re needed most. For some insurers, the challenge is foundational: without structured data capture, any form of real-time analytics is out of reach. For others, the issue lies in speed and coordination—fragmented systems create friction, delay insight, and slow down decision-making. Regardless of maturity, structured in-model data unlocks the ability to act faster, with more confidence.

Real-time portfolio views

Understand the impact of a policy on your broader portfolio—across metrics like rate change, price adequacy, expected loss ratio, and aggregation—while pricing it, not after the fact. This contextual view empowers underwriters to make better decisions proactively.

In-model benchmarking

Benchmark the risk you're pricing against similar risks already written and empower underwriters to renegotiate with brokers. Because data is clean, structured, and consistently captured in hx Renew, benchmarking isn't reliant on underwriter experience or siloed tools—it's available in-model and in real time.

Live rate change monitoring

Go beyond exposure-driven calculations. With granular data, you can break rate change down into changes in terms, limits, exposure, or risk profile. This enables more sophisticated, transparent, and accurate rate change assessments - delivered instantly.

Portfolio segmentation

When pricing tools and policy systems are linked, portfolio segmentation becomes much more powerful. Rather than battling fragmented data sources, insurers can confidently segment their book by geography, class of business, exposure type, or any other variable - supporting richer insight and more strategic steering.

On top of all the above, because the data in hx Renew is granular and structured, you can unlock batch testing (to calibrate models faster) and what-if analysis (to assess strategy shifts or external shocks).

Intelligence starts inside the model

The future of underwriting insight isn’t a dashboard - it’s in the model itself. By embedding structured data capture into the pricing process, insurers move from slow, manual reporting to fast, contextual decision-making.

Because when insight is embedded, not appended, it supports clearer, faster, and more informed decisions at the point of underwriting.

Want to take a glimpse into the future of underwriting? Read our report The Vision and Road to Underwriting 3.0 to discover how underwriting work is changing for the better.