Different Underwriting Process: How MGAs Evaluate Risk using MGA Underwriting Workbench

MGA Underwriting Workbench 

For Managing General Agents (MGAs), underwriting is not just an operational function, it is the core of the business. The ability to assess risk accurately, move quickly, and make sound decisions at scale is what separates MGAs that grow from those that stall.  

Yet the technology supporting underwriting has historically been rigid, often forcing MGAs to adapt their processes to the system rather than the other way around. At the same time, the market is becoming increasingly commoditized, driven by price-sensitive buyers and intense broker competition, making underwriting discipline more important than ever. 

As a part of an educational video series for MGAs, John Willemsen emphasized that underwriting discipline means carefully assessing and selecting risks, pricing policies correctly, and avoiding business that could lead to losses. He noted that during the Insurtech boom, many companies focused heavily on technology and rapid growth, sometimes at the expense of sound underwriting practices. Long-term MGA profitability still depends on consistent decisions, sound judgment, and disciplined execution. 

Broker relationships also remain central. As one major broker noted during a Sutherlands webinar, business is placed with underwriters they trust. That trust is built through reliable decisions, responsiveness, and consistency over time. No algorithm alone can replicate that. 

Recognizing the evolving needs of delegated authority markets, Insillion has built an MGA underwriting workbench designed to combine automation, flexibility, and human expertise across the full underwriting lifecycle. 

Why MGAs Need a Flexible Underwriting Workbench 

Every MGA operates differently. Some manage high volumes of small commercial risks with lean teams. Others focus on specialty or complex products where each submission requires careful analysis. Some write admitted business with standardized profiles, while others operate in the non-admitted market, underwriting niche, or emerging risks that require tailored decision-making. A recent Deloitte survey showed that 68% of insurers are focused on technology that improves customer experience, with policy servicing automation ranking high on that list. 

 A one-size-fits-all underwriting platform simply does not work for MGAs. The best Insurance underwriting workbenches are built to adapt to allow MGAs to configure processes around their products, capacity relationships, and growth strategy. Insillion's underwriting workbench is built to adapt to the MGA, not force the MGA to adapt to the platform. 

What Is an MGA Underwriting Workbench? 

An MGA underwriting workbench is the central workspace where underwriters receive submissions, evaluate risks, generate quotes, collaborate with carriers, and manage approvals, all without switching between emails, spreadsheets, and disconnected systems. 

With Insillion, underwriters log into a dashboard that provides a consolidated view of quotes, premiums, referrals, workload priorities, and submission activity across carriers, teams, and users. This gives immediate visibility into pipeline performance and helps teams prioritize work efficiently. 

Three Ways MGAs Can Approach Underwriting on Insillion 

Insillion supports three distinct underwriting approaches. MGAs can adopt one, combine elements of several, or move along the spectrum as their business scales. 

 1. Assisted Underwriting 

Assisted underwriting keeps underwriters firmly in control while removing operational friction. Automation improves submission intake, data preparation, and workflow orchestration, commonly used for simpler products such as auto insurance or smaller commercial policies. 

Automated Submission Intake 

Submissions often arrive through email or external broker systems. Instead of relying on manual triage, the platform can automatically:  

• Extract data from ACORD forms  
• Organize loss runs and statements of values  
• Identify missing information  
• Create quote-ready files in minutes  

That means underwriters spend less time preparing submissions and more time assessing risk.

Straight-Through Processing (STP) 

 For straightforward submissions that fit an ideal risk profile, predefined rules determine eligibility and pricing, enabling approvals with minimal human intervention. According to Datos Insights, to achieve the required agility and 60–70% straight-through processing, insurers are moving away from monolithic core systems in favor of AI-driven underwriting workbenches that are modular, flexible, and API-enabled. 

When mandatory fields are missing, the platform can automatically request additional information before the submission enters the underwriting queue, reducing rework and delays. 

Non-Straight-Through Processing (NSTP) 

When risks require human judgment, submissions are routed automatically to the appropriate underwriter based on: 

• Product line 
• Geographic region 
• Workload distribution 

Underwriters validate system-generated insights and adjust pricing within predefined authority limits. 

For multi-location risks, each location carries its own property values, construction details, limits, and coverages, while shared coverages are managed at the policy level. The workbench also includes a built-in collaboration chat, keeping underwriters, brokers, and agents aligned without relying on separate email threads, supporting the long-term trading relationships that drive MGA growth. 

2. Augmented Underwriting 

As products become more complex, underwriters need better insight, not just speed. Augmented underwriting combines human expertise with AI-supported analysis, third-party data, and automated workflows, and the co-pilot model that has emerged as the sweet spot across the commercial insurance industry. 

It is typically used for more complex products such as: 

• Commercial property 
• Specialty insurance lines 
• Homeowners policies with unique exposures 

Integrated Risk Data 

Submissions integrate directly with Agency Management Systems (AMS) and policy administration systems, eliminating duplicate data entry. Since specialty submissions often include unstructured information across ACORD forms, loss runs, and broker-submitted packets, manual review alone is time-consuming and inconsistent. Insurers estimate that 75–85% of operational data is unstructured and inaccessible to core systems. 

Insillion addresses this through rule-based document validation and triage, while enrichment APIs, such as Hazard Hub or Veridion, run in parallel to attach real-time risk attributes automatically. 45% of insurers are using AI for document processing. This reduces manual effort and improves pricing accuracy before the underwriter has reviewed a single line. By the time an underwriter opens a submission, the risk has already been structured and pre-analyzed. The workbench can also surface prior policy data, effective dates, premium comparisons, and duplicate customer records to speed decision-making. 

Rating Flexibility 

Rule-based rating engines generate an initial premium using internal models and external risk attributes.  Underwriters can then adjust pricing within approved limits based on exposure characteristics, broker relationships, or market conditions. This ensures pricing remains data-driven while still allowing professional underwriting judgment. As per Deloitte, this approach, using commercial insurance rating software, reduces development time from 3–6 months to just 3–4 weeks to modify existing coverage.

For commercial property, rating models can incorporate: 

• Operation type  
• Revenue or sales volume  
• Selected coverages  
• Minimum premiums  
• Property values  
• Square footage  
• Construction type  
• Multi-location exposures  

While still allowing authorized underwriters to override rates when market judgment is required. 

Multi-Carrier Placement for MGAs 

As MGAs grow, they often manage multiple capacity providers. Insillion supports workflows where MGAs can: 

• Place different coverages with different carriers  
• Send submissions to multiple markets for comparison  
• Trigger referrals based on premium thresholds  
• Maintain clear access controls for each carrier  

Governance and Compliance 

Role-Based Access Control (RBAC) ensures that each carrier only sees the data relevant to them. The workbench maintains a full audit trail of every decision, whether made by AI or a human underwriter, capturing user actions, document status, and workflow history to support regulatory compliance and transparency. Premium thresholds or referral rules can also trigger carrier review workflows before binding, while underwriters retain discretion to escalate submissions when necessary. Shared notes and approval histories create stronger delegated authority controls. 

As a result, augmented underwriting also improves consistency in risk assessment. By analyzing structured data across multiple sources, AI can evaluate risks using consistent criteria, reducing the influence of human bias.

3. Continuous Underwriting 

Continuous underwriting moves risk assessment beyond policy inception. Instead of evaluating risk once a year, MGAs can use live or near-real-time data to monitor exposure throughout the policy term. 

Usage-Based Insurance (UBI) 

Technologies such as IoT sensors, telematics devices, and connected systems collect ongoing behavioral and environmental data. In auto insurance, telematics-enabled policies can reward safe driving with lower premiums, while pay-as-you-drive models price based on frequency and safety of vehicle use.

Expanding Beyond Personal Lines 

Continuous underwriting can also apply to commercial insurance lines. 

Example Use Cases 

• Fleet / Commercial Auto Telematics data can help monitor driver behavior, route risks, and claims trends. 

• Commercial Property Sensors and connected systems can track fire protection, occupancy, or security changes. 

• Marine / Cargo Shipment tracking can monitor temperature, route deviations, weather, and transit conditions. 

• Builders Risk Project milestones, delays, and site activity can support dynamic risk management. 

AI-Driven Risk Monitoring 

AI and predictive analytics analyze dynamic data streams, identifying patterns across telematics, location data, and operational metrics to generate more personalized risk predictions. This enables insurers to proactively address coverage gaps that may emerge during the policy term while maintaining more accurate pricing. As industry expert Alexis notes, MGAs must differentiate through product innovation, for example, embedding risk-monitoring triggers into policies using wearable safety sensors or smart vests, shifting insurance toward proactive risk prevention. 

Insillion's Flexibility to Design MGAs' Own Underwriting Approach 

The MGAs that outperform the market usually combine three things: disciplined underwriting, strong broker service, and efficient operations. A modern underwriting workbench helps bring those three together. It gives underwriters more time for decisions, gives management better visibility, and gives brokers faster responses. For MGAs looking to grow profitably, modernizing underwriting operations is no longer optional, it is a competitive necessity. 

Insillion not only offers a single underwriting model. It is that it offers the flexibility to choose and the infrastructure to grow with a unified platform covering submission tracking, risk distribution, resource allocation, audit trails, and broad market flexibility. 

Frequently Asked Questions