Submission management is one of the most crucial operational aspects for an MGA, broker or any agency. With each year, the way submission management is defined changes, and in 2026 submission management will be defined by the way MGAs shift towards “Smart Scaling.”

With iNube, one of America’s leading commercial insurance producers was able to witness this transformation in real-time. With multiple issues in submission management, the producer faced some pivotal challenges in achieving a smooth submission management journey. 

This case study will highlight the challenges faced by the producer and the intelligent Gen AI-assisted class code mapping and submission data extraction implemented by iNube’s Submission Management, alongside their existing systems, to not just get rid of the challenges but also unlock the road towards scalability. 

Read on to explore the entire story. 

Quick glance at the MGA 

Our client is one of America’s leading providers of P&C insurance providers. With a decade of experience in offering the best Commercial insurance products, they remain committed to delivering transparent and relationship-driven Insurance products that are specifically designed for bringing clarity in a complex landscape. 

With this trust fostered over the years, they remain fully committed to clear communication, dependable responsiveness, and a long-term strategy for offering the best solutions to their customers.

Top operational concerns

The producer dealt with few of the critical roadblocks in achieving a seamless submission management journey. Here’s what they faced in a nutshell:

Manual data entry across multiple quote forms 

The producer essentially relied upon manual effort for filling out quote forms, increasing effort, turnaround time, and also the risk of errors. 

Fragmented ACORD form generation 

The ACORD forms are essentially generated separately from the quote workflows, thus leading to much repetitive data entry, more time involved for carrier submissions, and also inconsistent information flow. 

Incomplete underwriting information capture 

The critical underwriting details would often get missed because of the reliance upon manual effort; this resulted in the customers having to provide additional information.

Loss run collection and claims history sharing 

Collecting and standardizing the loss runs and claims history from the customers is largely manual and also often delayed. 

Absence of operational avenue for increasing headcount 

As submission volumes grew, scaling teams wasn’t always feasible due to cost and operational constraints. 

Increased turnaround time and error risk 

The extended processing cycles would often lead to delays and a much higher likelihood of manual errors.

How iNube helped turn around the situation 

While the challenges were quite critical, the team at iNube meticulously examined the operational roadblocks and integrated an intelligent GenAI-powered journey into their existing agency management process. Their renewed process looked like this: 

Dynamic Optimized Questionnaire 

A comprehensive superset of questionnaires was introduced, consolidating requirements from multiple commercial insurance carriers across specific lines of coverage into a single, unified digital intake.

The engine dynamically adapts by automatically updating relevant questions when a new line of coverage or state is added, ensuring accuracy and consistency without manual intervention.

Admin View Configuration 

The admin configuration interface enables users to customize the questionnaires shared with customers through intuitive digital screens.

Questions can be easily enabled or disabled at both a general level or tailored to specific lines of coverage, allowing precise control based on business requirements.

Digital customer intake via configurable portal 

The customers receive a portal-based digital form with stepper-style sections, thus ensuring a guided and structured data capture experience. 

AI-assisted risk classification 

During manual review of the submitted forms to the MGA, there will be AI assistance in filling out the class codes on the basis of risk details, location ZIP, and also descriptions. 

GenAI-Assisted Class Code Review

After AI assigns initial class codes, users can refine and validate them through an embedded GenAI assistant—adding risk context and arriving at accurate classifications before submission. 

These technology implementations introduced not just uniformity in their existing process of submission management but also brought structure to the way the MGA collected data and managed it with the carriers and the end customers. Overall, these technology integrations helped them deliver a superior customer experience.

Impact that cut through the noise 

• Faster quote turnaround – Shorter submission and quoting cycles 

• Improved data accuracy- cleaner, complete and standardized data capture 

• Reduced customer fatigue – Single form intake eliminates the repetitive questions 

• Seamless multi-carrier submissions – Single standardized data across multiple carriers offers uniformity 

• Enhanced customer experience – Guided, digital and frictionless form journey 

• Lower operational costs – Reduced rework and manual effort

Numbers that built Trust

• 70% reduction in submission timeline
• 50% fewer errors in data entry
• 30% faster quote turnaround