Rules-Based vs. AI Underwriting: Which One Scales Better for MGAs?

Underwriting is the engine of any MGA. It’s where risk is assessed, premiums are set, and profitability is decided. But as MGAs grow, open new lines, or take on delegated authority, traditional rules-based underwriting begins to strain.

For years, most MGAs relied on static rule sets: If this, then that. It made sense when books were small and consistent. But as submission volumes rise and risk becomes more complex, rules alone struggle to keep up.

Today, more MGAs are asking: Can we scale this process? And if not, what comes next?

This blog dives into the differences between rules-based and AI-driven underwriting - what each can (and can’t) do - and why intelligent automation is quickly becoming the new standard for scalable, profitable underwriting.

How Rules-Based Underwriting Works

Rules-based systems are built on clear, pre-defined logic: “If A, then B.” For example:

• If driver is under 25 and vehicle is over 200 horsepower, apply surcharge
• If ZIP code is in coastal region, require higher deductible
• If coverage lapse > 30 days, flag for manual review

These rules are often coded into underwriting guidelines, rating engines, or decision tables. They ensure consistency, enforce compliance, and help reduce human error.

But rules also have limits.

Where Rules-Based Underwriting Falls Short

As volumes grow and risks diversify, rigid rule sets begin to break down.

Limited Flexibility

Rules can’t easily account for nuanced or overlapping risk factors. They require constant updates to stay current with market trends, regulations, or claims outcomes.

Lack of Learning

Rules don’t adapt. If loss patterns shift or underwriting results degrade, someone must manually review and rewrite the logic - often months after the fact.

High Maintenance

Managing thousands of underwriting rules across multiple products, jurisdictions, and distribution channels becomes a full-time job - prone to inconsistencies.

Bottlenecks for Exceptions

Rules are either pass/fail. Gray areas require human intervention, slowing down turnaround times and increasing costs.

In small books, this may be manageable. But for MGAs looking to scale, it quickly becomes unsustainable.

What AI-Driven Underwriting Brings to the Table

AI underwriting doesn’t replace rules - it enhances and extends them. Instead of applying static logic, AI models learn from data to assess risk, predict outcomes, and automate decisions.

This includes:

• Machine learning models trained on historical submission and claims data
• Risk scoring engines that consider hundreds of variables in milliseconds
• Pattern recognition to detect fraud, misclassification, or outliers
• Natural language processing to extract structured data from emails or documents
• Predictive models that flag policies likely to result in loss

AI underwriting turns underwriting from a rules-only system into a dynamic, learning engine.

Key Benefits of AI for MGAs

For MGAs, the shift to AI isn’t just about automation - it’s about enabling smarter growth.

Faster Decisioning

AI models process submissions instantly, triage risk, and either approve, escalate, or reject based on learned patterns - not rigid gates.

Adaptive Risk Scoring

Models learn from past decisions and outcomes, improving over time. This reduces reliance on outdated assumptions or overgeneralized rules.

Underwriter Augmentation

AI doesn’t replace judgment. It surfaces insights and suggests actions, helping underwriters focus on edge cases where they add the most value.

Better Segmentation

By considering more data points than humanly possible, AI identifies subsegments within broad risk groups that deserve differentiated pricing.

Scalable Compliance

Rules can be embedded into AI workflows, ensuring adherence to regulatory or program-specific requirements without slowing down operations.

The Hybrid Approach: Rules + AI

The real power for MGAs comes when rules and AI work together. Rules still govern eligibility, red flags, or mandatory conditions. But AI handles nuance, prediction, and prioritization.

For example:

• Rules might exclude vehicles over a certain value from a standard program
• AI scores the remaining submissions based on likelihood of loss
• Underwriters are alerted only for borderline cases needing review
• Approved submissions are automatically rated and issued based on enriched data

This model blends consistency with intelligence - and allows MGAs to write more business, more profitably.

Real-World Impacts of Intelligent Underwriting

MGAs that embrace AI-driven underwriting see measurable benefits:

• Reduced quote-to-bind times, especially in high-volume segments
• Lower loss ratios through better risk selection
• Higher submission throughput without adding underwriters
• Fewer manual overrides and less underwriter fatigue
• More accurate pricing through enhanced data inputs and scoring

Perhaps most importantly, AI provides a feedback loop. Models are trained on what happened after policies were written - which allows MGAs to improve before the next round.

We cover this loop in more depth in The Underwriting–Claims Link: What Better Decisions Look Like Downstream, which explores how underwriting outcomes must be connected to claims performance to truly scale.

Key Considerations When Moving to AI

Shifting from rules to AI isn’t flip-a-switch simple. MGAs need to plan for:

• Data Readiness: AI models are only as good as the data they’re trained on. Clean, structured, and relevant historical data is critical.
• Underwriter Trust: Black-box models won’t fly. AI should provide explainability - so underwriters can see why a submission was scored a certain way.
• Compliance and Auditability: AI decisions must be traceable and defensible. Building governance into model workflows is essential.
• Workflow Integration: AI should enhance, not replace, current platforms. Seamless integration with existing tools and systems is key to adoption.

With the right foundation, these challenges are surmountable - and the payoff is transformative.

How Inaza Helps MGAs Scale Smarter

At Inaza, we’ve built an AI-first underwriting infrastructure designed for the unique needs of MGAs.

Our platform offers:

• End-to-end submission intake automation
• AI-powered risk scoring and triage
• Explainable underwriting models trained on client-specific data
• Seamless integration with broker portals, policy systems, and claims platforms
• Rule enforcement layered with AI flexibility
• Real-time model feedback from claims outcomes

We help MGAs move faster, write more, and underwrite with precision - without adding manual effort.

Ready to Scale Your Underwriting Without Scaling Headcount?

If your rules-based system is showing its limits, it’s time to consider a more intelligent approach. Inaza gives MGAs the tools to underwrite at scale, with confidence and control.

Talk to our team today to see how AI underwriting can unlock smarter growth for your business.