In a recent interview with Reinsurance News, Gavin Lillywhite, Senior Vice President of Business Development at Xceedance, discussed how data analytics and generative AI (GenAI) can elevate the Managing General Agent (MGA) business model.
“There has been much fanfare and hype in the market about the potential of agentic AI to transform the insurance and reinsurance industries. As we have seen with past technological breakthroughs, the prudent course of action would be to look seriously at the benefits, without ignoring the challenges,” began Lillywhite.
He explained that even with a healthy amount of scepticism, it’s safe to say that today, there are reliable agentic AI capability models that can allow “MGAs the opportunity to steal a march on competitors by offering clients superior levels of service and efficiency.”
Lillywhite emphasised that MGAs which effectively implement this technology can enjoy game-changing advantages, allowing them to underwrite some risks that have previously been excluded because they were deemed to be too complicated.
“MGAs have historically benefited from speed and niche focus, but that ability to be nimble is no longer, in itself, enough. The expectations of carriers and brokers have increased, and they know that if they are to remain competitive over the long-term, having a consistent, data-backed approach to risk selection and portfolio management is essential.
“Carriers want clearer visibility of how risks are selected and priced. Data analytics, in particular, can strengthen underwriting rationale, improve reporting quality, and help MGAs to demonstrate discipline across the cycle, particularly in softening markets where capacity becomes cautious,” he said.
Lillywhite went on to highlight the importance of combining internal expertise with external datasets. “By combining the experience of their underwriters and management teams with external datasets 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 having a fuller view of exposures without adding a significant operational burden.”
He also spoke about claims management and the role of AI, explaining that as claims costs fluctuate, MGAs need analytical tools that track cost drivers and emerging patterns. “It is not enough to simply outsource to a third-party Adjuster (TPA) – robust human oversight is vital,” he stressed.
Adding: “Where AI can help is in spotting trends and patterns that are driving up cost and eroding margin as markets soften. And with today’s claims technology able to handle simple claims end-to-end and act as an advisory agent for complex claims, MGAs can tear up the old playbook of automatically engaging a TPA for all claims and actually retain more claims management in-house and keep tighter control on both service and leakage rather than the traditional TPA model. These operational improvements can help MGAs to maintain pricing adequacy, thus ensuring that portfolios stay aligned with carrier expectations.”
On the impact of GenAI, Lillywhite said that the technology is applied to a range of everyday tasks and can significantly improve the underwriting and operational efficiency of MGAs. “GenAI can read emails, documents, and attachments to extract unstructured information in minutes, reducing manual effort and freeing up underwriters to focus on evaluating risk rather than processing data. It can also help underwriters to make consistent decisions. By organising risk indicators and highlighting anomalies, GenAI offers a clearer foundation for underwriting judgement. It becomes easier to compare similar submissions, and by iteration and an ongoing learning process, it documents why certain decisions are made.
“Operational efficiency is improved because routine processes such as bordereaux preparation, policy checks, or claims triage can be automated through AI-driven tools. This shortens cycle times, helps avoid errors, and gives teams more time for relationship-driven work.
“The cumulative effect of this technology is to strengthen and improve the customer and broker experience. With faster assessments and clearer responses, MGAs can provide quicker quotes and more transparent feedback. This increases service reliability, which can be a key differentiator when brokers choose where to place business,” he said.
On the scale of efficiency gains, Lillywhite referenced previous industry research from McKinsey, which suggested that insurers are able to reduce operational expenses by up to 40% through automation and digitisation, highlighting the scale of efficiency gains available as MGAs adopt AI-enabled workflows.
While the potential for AI to drive efficiency gains for MGAs is clear, Lillywhite offered practical guidance for those wondering how to begin. “A useful first step is to identify a critical everyday task where AI could be applied. This could be a specific low-risk workflow such as underwriting submissions or bordereaux processing, where MGAs can test AI and see quick and obvious benefits. By taking a focused approach, the entire company can stay aligned – reducing the potential for some parts of the organisation to resist this change,” he explained.
He further emphasised the value of early wins and data organisation. “Rather than waiting for a perfect solution, it is advisable that MGAs organise their existing data, even if it is incomplete. Early wins build confidence and create a foundation for future analytics initiatives, without requiring a full technology overhaul.
“Measuring change and outcomes – such as reduced manual hours, faster quote turnaround, improved accuracy, or more consistent reporting to carriers – is vital to success. It also creates a credible business case for expanding AI adoption into other parts of the organisation. After securing these early, demonstrable wins, MGAs should consolidate this data and these processes before scaling up. MGAs often manage data from multiple brokers and carriers, which leads to inconsistency. Establishing a central source of truth improves the quality of underwriting decisions and reduces rework across the value chain.”
Another benefit of AI underlined by Lillywhite is its ability to clean and standardise datasets. He explained: “AI-assisted validation and data extraction help convert PDFs, emails, and spreadsheets into structured fields. This reduces operational friction and makes it easier to feed analytics models with reliable information.
“When MGAs share more accurate and timely insights, carriers gain greater confidence in performance and it helps them to align their appetites with those of the MGA, leading to stronger long-term partnerships.”
Ultimately, these advances require deep-rooted cultural change, which Lillywhite said can be challenging for many organisations. “It is critical that management encourages a ‘data first’ way of thinking throughout the business. This can involve reviewing AI insights during underwriting rounds or claims meetings to normalise analytics as part of daily decision-making rather than a separate technical function. Team members will need to continually improve their skills if they are to get the most from AI. With proper training, teams will realise that they remain central to the business, with AI applied to enhance their daily work routines.”
“AI is undoubtedly a complex and challenging technology that will impact every business, but the potential gains are clearly enormous. For those MGAs trying to work out how to get going, partnering with a trusted technology provider can be a relatively simple way to get started without a huge cost outlay,” concluded Lillywhite.
