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 said that the importance of underwriting discipline means the ability to carefully assess and select risks, price policies correctly, and avoid writing business that could lead to losses. He says during the Insurtech boom, many companies focused heavily on technology and rapid growth, sometimes at the expense of sound underwriting practices. John emphasizes that while technology is valuable, strong underwriting discipline, careful risk selection, pricing accuracy, and consistent decision-making are ultimately what determine long-term profitability for an MGA. While acknowledging that some level of efficiency improvement is realistic 

From the webinar organized by Sutherlands, a large broker summed it up plainly that they place business with underwriters they trust. That trust is built over time, through decisions made well, through favors given and returned, and through being a reliable partner when things get difficult. No model replicates that. 

How does MGA Underwriting Workbench help?  

One of the clearest truths in delegated authority is that every MGA operates differently. Some run lean teams that handle high volumes of small commercial risks. Others focus on complex, specialist lines where every submission requires careful human judgment. Some write admitted business with standardized risk profiles. Others operate in the non-admitted space, underwriting specialized or higher-risk exposures that standard carriers avoid, serving niche or emerging markets that demand a more tailored approach. 

Some MGAs have sophisticated data infrastructure already in place. Others are building from the ground up. This is why a one-size-fits-all underwriting platform simply does not work for MGAs. The tools, the workflows, and the degree of automation that make sense for one organization may be entirely wrong for another. 

And regardless of how much automation an MGA adopts, one thing remains constant, the human element does not disappear. Even with advanced analytics, relationships continue to shape business decisions. AI may handle many routine tasks, but underwriters remain responsible for interpreting complex situations, managing broker relationships, and making strategic calls that no algorithm can fully replicate. AI is exceptionally good at generating structured analysis quickly and consistently. Humans remain essential for reading the nuance and making contextual decisions that actually stick. 

Insurance underwriting workbenches are built with this reality in mind. Rather than prescribing a single underwriting model, Insillion gives MGAs the flexibility to design an approach that fits their book, team, and growth ambitions and to evolve that approach over time as their needs change. 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. 

Three Ways MGAs Can Approach Underwriting on Insillion 

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

 1. Assisted Underwriting 

For MGAs that want to keep underwriters firmly in control while eliminating operational friction, assisted underwriting provides the right balance. In this model, the underwriter remains the primary decision-maker, while automation improves submission intake, data preparation, and workflow orchestration. This approach is commonly used for simpler or standardized insurance products, such as auto insurance or smaller commercial policies. 

Automated Submission Intake 

Underwriters receive submissions that have already been structured and enriched with supporting documents, such as loss run PDFs and ACORD forms. By the time a submission reaches the MGA underwriting workbench, much of the manual preparation work has already been completed, allowing underwriters to focus directly on evaluating the risk. 

Straight-Through Processing (STP) 

For straightforward submissions that fit an ideal risk profile, the automated underwriting workflow platforms with the fastest processing speed for MGAs enable straight-through processing. 

These cases typically involve: 

• Standard coverage requirements
• Minimal loss exposure 
• Clearly defined risk parameters 

In these scenarios, predefined rules determine eligibility and pricing, enabling approvals with minimal or no 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. 

Non-Straight-Through Processing (NSTP) 

When risks require human judgment, submissions are routed automatically by fastest automated underwriting platforms for MGAs to the appropriate underwriter based on: 

• Product line 
• Geographic region 
• Workload distribution 

The underwriter reviews the submission, assesses the risks, and adjusts pricing or coverage if necessary. Approval mechanisms operate within predefined underwriting authority limits, allowing underwriters to approve risks within their assigned thresholds. 

Managing Complex Submissions 

Specialty insurance submissions often involve complex policy structures, multiple exposures, and detailed documentation. In these cases, assignment rules can route submissions to more experienced underwriters or trigger escalation workflows. MGA underwriting workbench solutions improve the quality of submissions by structuring incoming data and identifying missing information early. This reduces manual preparation work and accelerates underwriting turnaround times. 

MGA Underwriting Workbench specializes in moving processes from NSTP to STP, helping MGAs reduce per-policy issuance costs and accelerate turnaround times significantly.  

The insurance underwriting workbench also includes a built-in collaboration chat, providing a dedicated communication space for each proposal, keeping underwriters, brokers, and agents aligned without relying on separate email threads or external tools. Because good broker service depends on open, consistent communication, this feature supports the long-term trading relationships that drive MGA growth. 

2. Augmented Underwriting 

As products become more complex, underwriting requires deeper analysis and broader data sources. This is where augmented underwriting becomes essential. In this model, underwriting decisions are supported by third-party risk intelligence, and human expertise. This is augmented underwriting, and it reflects 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 

MGAs can create an integrated system with modular components such as the Submission Intake, Agency Management Systems, Underwriting Workbench and Policy Admin can work together; allowing agents and underwriters to access the same information without duplicated 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. 85% to 75% of operational data is unstructured and inaccessible to core systems, as estimated by insurers 

Insillion addresses this through rule-based document validation and triage, while enrichment APIs, such as HazardHub or Veridion, run in parallel to attach real-time risk attributes automatically. 45%of insurersare using AI for document processing in their service. 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. Their job is to apply judgment to the nuance, manage the broker relationship, and make the final call. 

Rating Flexibility 

Underwriters may not have all the data they need to effectively and consistently analyze and accurately price risks quickly. But once they have all the enriched data, it also enables dynamic rating flexibility. Rule-based rating engines generate an initial premium using internal rating logic  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. 

Leading underwriters take a more consistent approach to pricing. In general, leading underwriters exhibit a more consistent approach to pricing, reacting smoothly to lapses rather than in a knee-jerk fashion based on changes in their book performance. This makes sense in the context of underwriters actively managing their book to protect and grow profitable lines of business. 

Multi-Carrier Placement for MGAs 

As MGAs scale, they often work with multiple capacity providers. Insillion's MGA underwriting workbench solutions support this through multi-carrier placement capabilities. 

MGAs can manage: 

• Line-of-business level placement, where different coverages are placed with different carriers 
• Quote-level placement, where a submission is offered to multiple carriers for pricing comparison 

Governance and Compliance 

To maintain confidentiality and governance when multiple carriers are involved, Role-Based Access Control (RBAC) ensures that each carrier only sees the data relevant to them, maintaining confidentiality and governance throughout. 

The insurance underwriting workbench also maintains a full audit trail of every decision, capturing user actions, document status, and workflow history. This supports regulatory compliance, internal reviews, and transparency in regulated insurance environments. 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. Underwriters are therefore able to focus on the aspects of underwriting that truly require human expertise rather than spending time gathering and organizing data. 

3. Continuous Underwriting 

Continuous underwriting is an emerging model where risk assessment does not end once a policy is issued, it continues throughout the entire policy lifecycle. Traditionally, MGAs determine risk and pricing using historical data, analyzing past trends to estimate future exposure.  Continuous underwriting shifts this by evaluating real-time or near-real-time policyholder behavior, allowing MGAs to dynamically adjust pricing, coverage terms, or risk exposure during the policy period. 

Usage-Based Insurance (UBI) 

Continuous underwriting is often associated with Usage-Based Insurance (UBI) models. 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 higher-risk behavior may result in pricing adjustments.  Pay-as-you-drive models are a common example, where the premium reflects how frequently and safely a vehicle is used. 

Expanding Beyond Personal Lines 

Continuous underwriting can also apply to commercial insurance lines. 

For example: 

• In commercial property insurance, real-time monitoring of building safety systems, fire detection equipment, or security controls gives insurers ongoing insight into changing risk conditions. 
• In marine insurance, shipment tracking systems can monitor location, temperature, humidity, weather, and geopolitical risk during transit, enabling insurers to reassess coverage when conditions change mid-voyage. 

These insights allow insurers to respond to risk changes during the policy period rather than waiting until renewal. 

AI-Driven Risk Monitoring 

Artificial intelligence and predictive analytics play a key role in analyzing these dynamic data streams. AI systems can identify patterns across telematics, location data, and operational metrics to generate more personalized and accurate risk predictions. This enables insurers to proactively address coverage gaps that may emerge during the policy term while maintaining more accurate pricing. 

For many MGAs, this model is still evolving. As industry expert Alexis notes, MGAs must differentiate through product innovation rather than relying on standard policies such as workers' compensation. One approach is embedding risk-monitoring "triggers" directly into policies using technologies like wearable safety sensors or smart vests. These tools allow insurers to detect potential risks early, shifting insurance toward proactive risk prevention 

However, it represents a clear shift toward proactive, data-driven insurance where risk management continues long after the policy is issued. 

Insillion's Flexibility to Design MGAs' Own Underwriting Approach 

Underwriting remains the foundation of every successful MGA. The ability to evaluate risk accurately, price it appropriately, and make consistent decisions at scale ultimately determines the profitability and sustainability of the business. AI has already improved operational throughput in insurance underwriting, but achieving meaningful profitability gains will require deeper adoption and scale. insurance underwriting workbenches allows MGAs to balance automation with expertise. MGAs that combine strong underwriting discipline with the right MGA underwriting workbench will be better positioned to scale operations, maintain profitability, and respond quickly to changing market conditions. 

 

Frequently Asked Questions

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.