Human In The Loop: Elevating AI with Human Expertise in Insurance

AI doesn’t replace human expertise in insurance — it amplifies it.

Across MGAs, reinsurers, and carriers, we’re seeing the same thing: AI is empowering people to perform better. It’s taking on the repetitive, manual work that slows teams down and giving professionals more time to focus on what they do best: applying judgment, making decisions, and delivering value.

But the most effective insurance AI is built around a human-in-the-loop (HITL) model. It keeps people involved at key decision points — so experts don’t just review the output, they actively help shape it. Their feedback improves accuracy, refines systems, and keeps AI aligned with the complexity of real-world insurance operations.

The insurers seeing the biggest returns from AI aren’t replacing their teams. They’re equipping them with better tools. And HITL is one of the most important principles behind that shift.

What Is Human In The Loop In Insurance?

HITL Breakdown

• A collaborative approach where insurance professionals provide input, validation, and oversight at strategic points in AI-driven processes
• Balances automation efficiency with human judgment and domain expertise
• Maintains human accountability and control over final decisions

Human-in-the-loop (HITL) AI is exactly what it sounds like: a feedback loop between AI systems and the experts who use them. The AI does the heavy lifting, such as extracting data, triaging risk, and surfacing insights, but people remain involved at key points to review, validate, and improve the output.

In insurance, that means underwriters, claims, and operations teams aren’t removed from the process — they’re embedded in it. Their oversight ensures outputs are accurate, compliant, and reflective of business context.

HITL also actively improves the capabilities of AI systems over time. When experts correct or confirm outputs, that input becomes a training signal. The AI learns from real-world use, continuously refining its performance based on human feedback.

It’s a purposeful approach that keeps human expertise central while allowing AI to process information at scale, surface patterns, accelerate repetitive tasks, and continuously learn from the people who know insurance best.

Examples in Insurance:

• Claims processing: AI will prioritize notifications, but the claims team will validate impact, reserve requirements, and more.
• Underwriting: AI handles intake and pre-qualifies submissions based on data, while underwriters review, apply judgment, and validate opportunities.
  
  

Why Human Judgment Still Matters in AI-Driven Insurance Workflows

AI can process and synthesize massive amounts of data, flag anomalies, and spot patterns no person could see.

But it performs better when human expertise is involved — adding the context and nuance that isn’t always captured in the data, from edge cases to conflicting information.

Every time a human validates, corrects, or overrides an AI decision, it creates a feedback loop that trains the system to perform better. Over time, the AI becomes more accurate, more aligned with your workflows, and more useful to your teams.

Key Benefits of Human-in-the-Loop AI in Insurance

Human-in-the-loop (HITL) workflows improve more than just individual decisions — they support accuracy, accountability, and ongoing learning at scale. When human judgment is built into AI-driven processes, insurers get the best of both worlds: speed and scale from automation, paired with oversight and expertise where it matters most.

Improved Regulatory Compliance

Insurance is a heavily regulated industry. Decisions around pricing, claims, and eligibility must be explainable, auditable, and compliant.

HITL workflows:

• Allow humans to review AI outputs for regulatory alignment
• Create a clear audit trail for internal and external reviews
• Ensure accountability in high-impact decisions

Preserved Domain Expertise

Insurance professionals hold valuable knowledge that isn’t always captured in process flows, data or documentation.

HITL enables insurers to:

• Apply expert judgment in real time to non-standard cases
• Use human feedback to retrain and improve AI models
• Retain institutional knowledge even as teams evolve

Reduced Bias in Automated Decisions

AI models are only as objective as the data they’re trained on, and historical data often carries embedded bias.

HITL provides a safeguard by:

• Letting humans catch patterns that may seem biased or inconsistent
• Preventing unfair treatment of applicants or policyholders
• Promoting ethical, context-aware decision-making

Continuous Learning and Performance Improvement

Human input doesn’t just improve individual outcomes — it trains the system itself. Each correction or confirmation feeds back into the agent and model, making it more accurate and aligned with your real-world decision-making.

This creates a cycle where:

• Human feedback refines the agent model
• AI adapts to the nuances of workflows
• Teams gain confidence and speed in using AI outputs

Human-in-the-Loop in Action: Brisc’s Submissions Agent

Brisc’s Submissions Agent is an example of how human-in-the-loop AI works in practice.

The agent automates the intake of submissions, extracting relevant data from emails, attachments, and documents, and presents that data in a clean, usable format. Teams are then able to explore, interrogate, validate, and refine the results.

Submission Data Extracted and Displayed

The Submissions Agent parses the contents and populates structured fields in the dashboard, surfacing key details like entity name, line of business, prior losses, and coverage terms.

Source Transparency

Every extracted value is paired with contextual insight. Users can click into any field to see where the data came from — SOVs, loss runs, engineering reports — and how it was interpreted.

Human Validation

Underwriters can review and edit extracted values, confirm or reject fields, and ask questions about the data — with every action feeding back into the model to make it smarter over time.

Push to Workflow
Once reviewed, the data can be exported or pushed into internal systems, with the click of a button.

With Brisc’s HITL design, underwriters stay in control while the AI takes care of the manual work. Submissions get processed faster, accuracy improves, and fewer opportunities are lost in the shuffle.

By automating intake and surfacing clean, structured data instantly, your team is free to focus on writing the most profitable business — driving better risk selection and higher win rates.

Driving Innovation With Human-In-The-Loop AI

The future of insurance isn’t humans vs. machines. It’s humans with machines — systems that learn and improve because your experts are in the loop.

HITL is a strategy for getting more out of AI, with the accountability, compliance, and judgment the insurance industry demands.

At Brisc, we’ve built this model into everything we do. It empowers teams, accelerates decision-making, and creates a foundation for continuous learning and profitable growth.

See it in action. Book a demo today.