Underwriting at the speed of AI

AI has transformed some underwriting teams and barely touched others. Some insurance types and operating models are already highly automated. Others are still tied to manual processes: scanning broker emails, re-keying data, chasing missing documents. Most sit somewhere in the middle. But wherever you are, time is still lost on repetitive work that adds little value.

There’s plenty of AI interest in insurance, but most tools don’t fit how the industry actually works. Data arrives in inconsistent formats. Legacy systems block full automation. And even the best insurance-specific products tend to solve narrow problems, with one-size-fits-all designs that add friction, like switching systems or logging into tools that don’t fit existing workflows.

At vector8, we see this every day. Most underwriting leaders aren't asking for more AI. They're asking how to make it work in messy, high-pressure environments. The real opportunity isn't in headline-grabbing innovation but in practical progress that fits the way underwriting actually runs.

Key challenges

These are the challenges that underwriting teams bring to us most often — the operational blockers that stall progress or stop AI from scaling:

● Time lost to manual document review, data entry and back-and-forth with brokers

● Inconsistent, incomplete or poorly structured submissions

● Difficulty spotting data manipulation or fraud without intensive checks

● Disconnected systems and workflows slowing down decision-making

● Legacy infrastructure that limits automation and adds operational overhead

● High volumes and limited time mean some enquiries are reviewed late or not at all

AI-powered underwriting applications

These are the use cases that should be on your radar. This is where AI is already delivering results or has clear near-term potential. We’re seeing these play out in live projects, with insurers applying AI to improve underwriting performance and reduce friction.

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Extracting data from documents is nothing new, but AI models can now handle unstructured formats with far more speed and accuracy. AI pulls key fields like previous premiums, limits or renewal terms and presents them to underwriters for rapid review and confirmation.

● Reduces re-keying and manual data entry

● Surfaces suspicious or inconsistent values earlier

● Helps underwriters spot errors, fraud or data manipulation

● Keeps humans in the loop: AI assists, underwriters decide

● Builds a clearer audit trail and reduces downstream duplication

AI identifies what’s missing in a submission, processes inbound emails and automatically follows up with brokers or customers to request required documents or information.

● Cuts back-office admin by automating repetitive follow-ups

● Speeds up submission readiness and review

● Frees up underwriters to focus on decision-making

● A realistic first step toward AI-led orchestration

Underwriters can ask natural-language questions about previous policy documents, for example, “Was flood cover included?” or “What was the sum insured in 2021?” and receive accurate, sourced answers instantly.

● Reduces time spent digging through PDFs and legacy documents

● Helps underwriters assess risk and continuity more efficiently

● Supports decision-making with clear, auditable source links

● Built on RAG (retrieval-augmented generation) for control and consistency

AI reads and prioritises incoming broker or customer emails, classifying them by urgency and content, and routing them to the right handler or team.

● Reduces inbox triage time

● Ensures high-priority queries are handled fast

● Works seamlessly across underwriting teams and submission workflows

● Can be tuned to regional workflows or SLAs

AI analyses images or video submitted by customers (e.g. of vehicles or property) to extract details like licence plates, damage and condition to support faster risk evaluation and fraud detection.

● Moves AI into the heart of risk management

● Enables richer, evidence-led underwriting

● Supports innovation in pricing and product design

● Not widespread yet, but a clear next step for insurers ready to lead

These steps aren’t standalone fixes. When joined up and integrated into existing underwriting tools, they create a seamless flow between AI capabilities and human decision-making.

AI that delivers now. And scales with you.

AI conversations in underwriting are still being driven by what's new rather than what’s needed. There’s pressure to apply GenAI, agentic AI or the latest trend, rather than what's useful. Too often this leads to experiments that go nowhere or tools that don’t fit how the business actually works.

We’ve seen this pattern across multiple clients. What works is starting from operational need, not the latest capability. Building with reuse in mind. Designing for messy data, not perfect inputs.

At vector8, we focus on a modular approach that solves specific operational problems and can be reused across teams, systems and product lines. That’s what turns short-term wins into long-term capability, and avoids rebuilding the same solution twice.

If you're facing similar challenges or want to explore a pragmatic path to scalable AI, let’s talk.

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