In a recent interview with Reinsurance News, Gavin Lillywhite, senior vice president of business development at Xceedance, discussed how data analytics and generative artificial intelligence (GenAI) can enhance the managed general agent (MGA) business model.
“There is much hype and hype around the potential of agent AI to transform the insurance and reinsurance industry. As we have seen with past technological breakthroughs, the prudent course of action is to carefully consider the benefits while not losing sight of the challenges,” Lillywhite began.
He explained that even if there is a degree of skepticism, it is safe to say that there are solid models of agent AI capabilities available today that could give “MGAs the opportunity to stay ahead of their competitors by delivering superior levels of service and efficiency to their customers.”
Lillywhite emphasized that MGAs that effectively implement this technology can enjoy game-changing advantages, allowing them to take on some risks that were previously excluded as being too complex.
“Historically, MGAs have benefited from speed and niche markets, but that flexibility is no longer sufficient on its own. Expectations have increased among carriers and brokers, who know that adopting a consistent, data-backed approach to risk selection and portfolio management is critical if they are to remain competitive over the long term.
“Operators want a clearer understanding of how to select risks and price. Data analytics, in particular, can strengthen the underwriting case, improve the quality of reporting and help MGAs demonstrate discipline throughout the cycle, particularly in soft markets where capacity becomes cautious,” he said.
Lillywhite went on to stress the importance of combining internal expertise with external data sets. “By combining the experience of underwriters and management teams with external data sets such as location intelligence, claims trends, embedded technology and process risks, or industry benchmarks, MGAs can make more precise decisions. They can also enjoy the benefits of a more comprehensive view of risk exposure without adding significant operational burden.”
He also touched on the role of claims management and artificial intelligence, explaining that as claims costs fluctuate, MGAs need analytical tools to track cost drivers and emerging patterns. “Simply outsourcing to a third-party adjuster (TPA) is not enough – strong human oversight is critical,” he stresses.
Adding: “AI can help identify trends and patterns that are driving up costs and eroding margins as the market softens. With today’s claims technology able to process simple claims end-to-end and act as advisory agents for complex claims, MGAs can break away from the old practice of automatically hiring TPAs for all claims and actually retain more in-house claims management and tighter controls on service and leakage than in the traditional TPA model. These operational improvements can help MGAs maintain pricing adequacy, ensuring product portfolios are aligned with operator expectations.”
Regarding the impact of GenAI, Lillywhite said that the technology, applied to a range of daily tasks, can significantly improve MGA’s underwriting and operational efficiency. “GenAI can read emails, documents and attachments, extracting unstructured information in minutes, reducing manual work and allowing underwriters to focus on assessing risk rather than processing data. It also helps underwriters make consistent decisions. By organizing risk metrics and highlighting anomalies, GenAI provides a clearer basis for underwriting judgments. It becomes easier to compare similar submissions, and through an iterative and continuous learning process, it documents the reasons why certain decisions were made.
“Operational efficiencies are improved because routine processes such as boundary preparation, policy checking or claims classification can be automated through AI-driven tools. This reduces cycle times, helps avoid errors and gives teams more time to do relationship-driven work.
“The cumulative effect of this technology is to enhance and improve the client and broker experience. With faster assessments and clearer responses, MGA can provide faster quotes and more transparent feedback. This increases service reliability, which can be a key differentiator when brokers choose where to do business,” he said.
On the scale of efficiency gains, Lillywhite cited previous industry research from McKinsey, which showed that insurers can reduce operating expenses by up to 40% through automation and digitization, which highlights the scale of efficiency gains that MGAs can achieve by adopting AI-powered workflows.
While the potential for AI to drive efficiency improvements in MGAs is clear, Lillywhite provides practical guidance for those wondering how to get started. “A useful first step is to identify key day-to-day tasks where AI can be applied. This could be specific low-risk workflows, such as underwriting submissions or border processing, where MGAs can test AI and see quick and clear benefits. By taking a centralized approach, the entire company can be aligned, reducing the likelihood that certain parts of the organization will resist such change,” he explains.
He further emphasized the value of early wins and data organization. “Rather than waiting for the perfect solution, MGA is advised to collate existing data, even if it is incomplete. Early wins can build confidence and lay the foundation for future analytics initiatives without the need for a complete technology overhaul.
“Measuring changes and results—such as reduced labor hours, faster quote turnaround, improved accuracy, or more consistent reporting to carriers—is critical to success. It also creates a solid business case for expanding AI adoption to other parts of the organization. After these early, clear wins, MGAs should consolidate this data and these processes before scaling up. MGAs often manage data from multiple brokers and carriers, which can lead to inconsistencies. Establishing a central source of truth improves the quality of underwriting decisions and reduces the risk of rework throughout the value chain.”
Another benefit of AI highlighted by Lillywhite is its ability to clean and standardize data sets. He explains: “AI-assisted validation and data extraction helps convert PDFs, emails and spreadsheets into structured fields. This reduces operational friction and makes it easier to feed analytical models with reliable information.
“When MGAs share more accurate and timely insights, operators feel more confident in performance and help them align their needs with those of MGAs, building stronger long-term partnerships.”
Ultimately, these advances require deep-seated cultural changes, which Lillywhite says can be a challenge for many organizations. “It is critical that management encourages a ‘data-first’ mindset across the business. This may include reviewing AI insights during underwriting rounds or claims meetings, formalizing analytics as part of day-to-day decision-making rather than as a separate technical function. If team members want to get the most from AI, they will need to continuously improve their skills. With the right training, teams will realize that they are still at the core of the business and the application of AI can enhance their day-to-day work.”
Lillywhite concluded: “AI is undoubtedly a complex and challenging technology that will impact every business, but the potential benefits are clearly huge. For those MGAs trying to figure out how to grow, working with a trusted technology provider can be a relatively easy way to get started without huge cost outlays.”
