Can MGAs stay ahead in personal lines pricing?

Pricing agility, richer data and digital trading capabilities are becoming the defining competitive advantages for ambitious MGAs. Vicki Summerhayes reports.

 

The agility challenge

Managing general agents (MGAs) are renowned for their entrepreneurial spirit, flexibility and ability to move quicker than traditional insurers. But in today’s fast-paced, competitive, personal lines market, pricing has become a key battleground. Speed is critical, particularly in a soft market when the margins for error are slim.

As a result, modern MGAs are overhauling their business models and technology stacks to remain competitive and meet changing broker and customer expectations. By moving away from traditional slow-moving review cycles to real or near-real-time rate adjustments, MGAs can respond instantly to market shifts, while balancing growth and profitability demands. Failing to do so leaves MGAs vulnerable to lost business, poor conversion rates and margin erosion.

Pricing investments

A growing number of MGAs are therefore transitioning from ad-hoc, underwriter-led pricing to technology and data-driven pricing engines, with API-first architectures that can instantly deploy rate changes to broker panels and distributors.

Karen Hogg, COO at Stella Insurance, describes her firm’s recent pricing investments: “We were handling pricing with the underwriting team, but it’s a very different skill set. We have now created a pricing team, invested in market tools and developed our AI capabilities. It’s been a big step change, increasing our agility and speed to market. We are now able to make weekly or even daily rate changes,” she says.

“MGAs that don’t have the agility of an insurer-hosted pricing (IHP) system are going to struggle over the next few years,” argues Jordan Barnard, head of strategy at Paragon. “Not only will their pricing be out of date but they risk being selected against as a result. It typically takes six to eight weeks for an MGA to change rates via broker software houses. We can do it in hours,” he adds.

Harnessing data and continuous feedback loops

Data sits at the heart of pricing agility. The ability to harness and deploy the ever-expanding volumes of data at their disposal is a critical differentiator for MGAs. “Historically we were sitting on a huge, underutilised data set, but that data is now becoming our biggest asset,” explains Hogg.

We have created a pricing team, invested in market tools and developed our AI capabilities. It’s been a big step change, increasing our agility and speed to market.

Karen Hogg, Stella Insurance

MGAs are now using much richer external datasets to improve pricing accuracy and customer experience, increasing conversion rates with fewer referrals or manual interventions.

“Enriched data also means we are able to understand risks in far more granular detail,” adds Barnard. “Bigger companies have access to exactly the same data, but they haven’t used it in the same way. The data isn’t the differentiator anymore. The differentiator is whether your business is designed to make decisions at that level of detail. We underwrite at unique-property-reference-number (UPRN) level for risks like flood, subsidence and storm, because we believe individual properties deserve individual decisions.”

In addition to third-party data, MGAs like Stella and Paragon have recognised the unique insights that can be derived from factoring internal claims data into the pricing cycle.

“Bringing claims in-house has significantly improved Paragon’s pricing agility,” says Barnard. “Understanding the risk profiles behind claims frequency and severity in real time is incredibly valuable and means you can adjust pricing accordingly. If you just receive a bordereau at the end of a month it’s often too late or the data is too opaque,” he explains.

Some MGAs are creating valuable feedback loops by feeding live management information (MI), such as quote volumes, conversion and referral rates, directly back into their pricing engines. This allows firms to quickly spot when changes are needed. Such data is proving particularly valuable in tight rating environments when firms cannot afford to wait weeks to discover if conversion rates have dropped in a particular area or demographic.

With broker partners sharing quote data, MGAs are expanding their visibility beyond bound policies to analyse what business they have declined or missed out on.

“We use quote and sold data to understand where pricing can improve conversion,” says Gary Humphreys, chief commercial officer at Saturn Group. “By referring that data back into our pricing models, we have a continuous feedback loop in terms of both market and risk pricing.”

Forward-thinking MGAs are therefore moving away from static monthly reporting, building real-time dashboards that track live metrics. Hogg describes the value that diagnostic tools and dashboards bring to Stella: “We can take action quickly and monitor the impact of changes. Within two weeks, we know whether a change is working or not from both a top- and bottom-line point of view. The speed with which MGAs can analyse data, implement change, monitor and adjust is so important, bringing real growth and loss-ratio advantages.”

However, as Humphreys points out: “One of the biggest challenges for pricing is handling the sheer volume of data.” Capturing and processing that information requires modern data infrastructure, combining computational power and cloud storage, along with specialised skill sets.

Unsurprisingly, AI plays an increasingly important role in streamlining data preparation and unlocking deeper analysis. “It speeds up the data wrangling process, spots data anomalies and gets data into the best possible shape for pricing models and technical teams, while reducing overheads,” says Humphreys.

Hogg also points to a specific practical example: connecting AI to Stella’s Snowflake database allows her team to query live retention rates across specific brokers, age groups or vehicle types.

It typically takes six to eight weeks for an MGA to change rates via broker software houses. We can do it in hours.

Jordan Barnard, Paragon

Efficient trading and reducing manual processes

Broker partners often judge MGAs on their service quality and responsiveness, with digital trading capabilities becoming a key competitive differentiator. Apart from particularly specialist or non-standard risks, full-cycle electronic data interchange (EDI) trading and policy fulfilment are increasingly non-negotiable.

“Full-cycle EDI has existed for quite a while now but, in reality, very few MGAs have it,” says Paragon’s Barnard. “Yet it takes underwriters so much time to process the simple admin tasks that full-cycle EDI can handle. If you factor in the fully loaded cost of an underwriter’s time on tasks such as mid-term adjustments (MTAs), the cost-benefit analysis is very clear.”

Rather than wasting time rekeying data, managing routine referrals, or tackling manual MTAs and renewals, underwriters should be concentrating on exceptions, complex risks, and product development.

To tackle referrals, Barnard underlines the importance of capturing and categorising referral data to decide what needs to be changed. “For example, if you are asking for confirmation on whether a property is fully underpinned for every subsidence referral, why not just add it to the quote process and stop the referral coming in?” he says.

The role of partners

Choosing the right capacity and technology partners is critical for MGA success. “You kiss a lot of frogs before finding the right ones,” observes Stella’s Hogg. “As we grow, we are focused on developing long-term partnerships built on trust and transparency.”

On the capacity side, that trust is demonstrated through multi-year capacity commitments, flexible risk appetite frameworks and pre-approved underwriting parameters that eliminate the need for carrier sign-off for every rating adjustment.

Technology suppliers must be flexible and able to respond quickly to change. Being stuck in a technology change stack means you can quite often miss an opportunity.

Gary Humphreys, Saturn Group

External chokepoints are often the biggest threat to an MGA’s agility. “We need partners that can keep pace. We move very quickly, and any delays often stem from waiting on external decisions or builds,” notes Hogg.

Saturn’s Humphreys agrees, emphasising that: “Technology suppliers must be flexible and able to respond quickly to change. Being stuck in a technology change stack means you can quite often miss an opportunity.”

Technology vendors also play an increasingly important role in adapting to heightened regulatory scrutiny. “Capacity providers have always been fairly demanding regarding data volume and quality but, in recent years, there has been much greater regulatory focus,” Humphreys points out. “With Consumer Duty in particular, oversight and governance requirements have grown considerably. Tech providers are helping with continuous evidencing and automated reporting, while AI tools are increasingly important in mitigating the cost of these compliance activities.”

Future focused

Looking to the future, for MGAs to succeed in personal lines they must play to their size and agility strengths, capitalising on freedom from the governance layers that hamper traditional insurers. “These advantages mean that we can innovate, adopt new tools and deploy AI much faster than larger insurers. It allows us to power up far quicker,” argues Hogg.

“An insurer’s scale inevitably changes the speed at which decisions can be made. One of the strengths of the MGA model is that, with the right delegate authority agreements and technology, we can respond to emerging trends much more quickly,” Barnard adds. “The MGAs that succeed will be fully tech-enabled and genuinely data driven. We’re specialised and focused enough to track everything happening across our books and respond in real time. That agility is where our true opportunity lies,” he concludes.

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