MGAs Can Differentiate by Closing the Data Trust Gap

MGAs that deliver consistent, transparent data can reduce carrier reconstruction work, strengthen confidence and compete more effectively for capacity.

(Image credit: IIR.)

Delegated authority drives the modern insurance market. Managing general agents (MGAs) scale by pairing underwriting expertise with distribution agility. Carriers and reinsurers reach specialized risk without building the capability in house. The model works.

The model works so well that premium generated through MGAs and other delegated underwriting authority enterprises grew 15 percent to $89.9 billion in 2024, a fourth consecutive year of double-digit growth, according to AM Best. More than 700 MGAs now meet the NAIC reporting threshold for individual MGA premium disclosure.

The model also carries a tension few participants name out loud. Carriers bear the risk but depend on data produced closest to it by MGAs. Between those facts sits a challenge that quietly shapes every capacity conversation: trust.

Every Reporting Cycle Is a Trust Audit

Delegated authority relationships rest on alignment in theory. Continuous validation sustains them in practice. Carriers do not extend trust based on past performance or underwriting reputation alone. They reassess it every time data arrives—through bordereaux, performance reports and exposure updates.

Consistency matters as much as accuracy. A profitable program still raises questions when:

  • Data definitions shift between reporting periods
  • Exposure fields arrive incomplete or inconsistently populated
  • Classifications diverge from prior submissions

Small inconsistencies accumulate. The result rarely announces itself as lost confidence. It shows up as hesitation—a reluctance to rely on the data exactly as presented.

Carriers Rebuild the View

Carriers compensate. They ingest bordereaux, restructure them, normalize the fields and run their own analysis. They rebuild exposure views. They recalculate performance metrics. Some maintain entire parallel reporting frameworks inside the carrier organization, purpose-built to reconstruct what the MGA already sent.

Regulatory expectations reinforce that scrutiny. The 2025 NAIC Financial Condition Examiners Handbook directs examiners to evaluate how insurers oversee their MGA relationships, including MGA contracts, policies and financial accounts, as well as the insurer’s procedures for monitoring MGA activity and periodically auditing MGA operations. Independent validation is not simply a matter of carrier preference; effective oversight of delegated business is a governance responsibility.

In such cases, two versions of the truth can emerge: the MGA’s view of the portfolio and the carrier’s reconstructed view. The two are likely to be directionally consistent without ever matching exactly. Reconciling them consumes time, introduces friction and delays decisions. The deeper signal matters more—the carrier cannot rely on the data in its original form without additional validation.

Data Friction Becomes Growth Friction

This dynamic does not stay inside operations. It reshapes how the relationship evolves. Carriers working with data they struggle to interpret or reconcile move cautiously, and that caution can have meaningful effects, including:

  • Slower approvals for new programs
  • More conservative capacity deployment
  • Extended timelines for renewals and repricing
  • Heightened scrutiny during audits and performance reviews

No one labels these outcomes data problems. Uncertainty drives them—uncertainty rooted in inconsistent or opaque information.

For the MGA, the effect stays subtle and turns material. Growth continues. It just slows. Opportunities take longer to convert. Strategic conversations demand more validation than they should.

The Structural Blind Spot

An asymmetry sits at the center of the delegated authority model. MGAs operate close to the risk. They generate detailed, program-level data and see underwriting activity as it happens. Carriers depend on that data to understand risk they own outright, and they receive it after the fact, in varying formats and often at reduced levels of detail.

An MGA may believe it provides sufficient transparency, while the carrier still sees an incomplete or difficult-to-reconcile picture. The gap persists anyway—not a gap in intent, but in shared understanding.

Carriers have grown less willing to tolerate that gap. Non-affiliated MGAs—those not owned by insurers—produced 46.6 percent of U.S. MGA premium in 2024, exceeding the 45.3 percent produced by carrier-affiliated MGAs, according to Conning’s 2025 study. As a growing share of delegated business flows through MGAs outside carrier ownership, an MGA that requires extensive data reconstruction becomes more vulnerable to being deprioritized.

From Data Delivery to Data Confidence

For forward-looking MGAs, this is not simply a volume problem. Reporting more data does not close the gap. Delivering data a carrier can trust on arrival, and validate without rebuilding, closes it. That shift means moving past fragmented, program-specific processes toward a unified approach built on:

  • Standardized data structures spanning every program and carrier relationship
  • Consistent definitions that hold steady over time
  • Normalized ingestion regardless of source system
  • Clear lineage and auditability behind every reported figure

The objective is not to build another reporting process for every capacity partner. It is to establish a governed data foundation from which each partner’s requirements can be produced consistently.

Internal efficiency is one benefit. The larger strategic objective is a shared version of the truth—one both MGA and carrier can rely on without extensive reconciliation.

Trust Compounds. So Does Its Absence.

Growth raises the stakes. More programs mean more data sources. More carrier relationships mean more reporting expectations. Complexity compounds fast, and without a strong data foundation, trust erodes at exactly the moment scale demands it. Friction increases. Carrier-side workarounds expand. Decision cycles stretch.

The inverse can be equally powerful. Consistent, transparent, interpretable data can accelerate reporting cycles, simplify audits and improve alignment across both organizations. Trust compounds. So does the ability to grow.

A New Basis for Differentiation

Underwriting expertise will always matter. Distribution will keep differentiating. Another factor increasingly determines which MGAs scale most effectively: how clearly and confidently they demonstrate their business to the partners providing capacity.

Capital is selective. Scrutiny is rising. Data credibility is no longer a back-office concern. It has become a strategic asset in the competition for capacity. MGAs that recognize this and invest accordingly become easier to work with—and they move faster, secure stronger relationships and scale with fewer constraints.

In delegated authority, that may prove the most durable advantage of all.

When AI Agents Escape, Who Pays?

Bruce F. Broussard, Jr. // Bruce F. Broussard, Jr. has more than 30 years of P&C and Life & Annuities insurance experience focusing on insurance data solutions. He has served in various roles, including solution & data architect, project & program manager, and insurance application manager. Prior to founding Percipience, Broussard led the Data & Analytics practice at MVP Advisory Group and served as SVP & General Manager of the Data Solutions Business Unit for Insurity. He led the product strategy, development, client implementations, and sales for Insurity’s DataHouse product (formerly IEV). DataHouse became the leading P&C data solution under Bruce’s leadership. Previously Broussard was an Associate Partner for IBM’s Insurance Consulting Practice, where he led IBM’s global insurance data practice. Bruce also served as VP, Application Development for Pan-American Life Insurance Company, responsible for all US and International systems. Broussard holds a Bachelor of Science degree in Computer Science from Louisiana State University and completed the IBM Executive Program at Wharton School at the University of Pennsylvania and received his Machine Learning certification from the Massachusetts Institute of Technology (MIT). Bruce holds FMLI and ACS designations.

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