How MGAs use AI to improve profitability and secure capacity

Managing General Agents (MGAs) play a crucial role in the insurance ecosystem, offering specialized coverage in niche markets. However, their success hinges on efficiency, underwriting discipline, and their ability to secure reinsurance capacity. Without profitability, an MGA risks losing its competitive edge and, worse, its capacity partnerships.

Fortunately, artificial intelligence (AI) is reshaping underwriting by enabling MGAs to enhance efficiency, optimize risk selection, and strengthen their relationships with capital providers.

Before exploring how AI is driving profitability, it's worth understanding the operational and strategic challenges most MGAs are facing today. These challenges create friction and inefficiencies that AI is uniquely suited to address.

5 challenges that hold MGAs back

1. Lack of real-time portfolio data: Most MGAs are forced to make decisions using outdated reports or fragmented insights. Without real-time visibility into portfolio performance, it’s hard to adjust course, stay on strategy, or demonstrate control to capacity providers
2. Time wasted on unqualified or unwinnable submissions: According to Federato’s 2025 State of Underwriting, underwriters spend 26% of their time on unwinnable deals, and 25% of submissions fall outside the portfolio’s appetite. This misalignment drags down hit ratios, burns cycles, and eats into profitability.
3. Strategy is difficult to operationalize: Appetite guidelines and program rules are typically documented in PDFs or shared informally. Without embedding strategy directly into the underwriting workflow, consistency suffers.
4. Capacity and reinsurance relationships are at risk: In a tightening capital market, demonstrating discipline and performance is essential. If the portfolio doesn’t reflect the strategy promised to capacity providers, renewal becomes a challenge.
5. Pressure to scale without increasing cost: Many MGAs are trying to grow quickly with lean teams. But disconnected systems and manual processes make it hard to scale underwriting without adding overhead or compromising accuracy.

Solving these challenges is key to unlocking profitable growth, and that’s where AI can play a transformative role.

How AI underwriting transforms MGA challenges into profit

Addressing these challenges creates space for meaningful change. With the right tools, MGAs can move faster, act with greater precision, and deliver stronger results. AI is already enabling that shift by making underwriting workflows more efficient, aligning action with appetite, and increasing transparency across the portfolio.

Here’s how it translates to improved profitability.

1. Maximizing underwriter impact

Underwriter expertise is one of the most strategically valuable resources at an MGA’s disposal. In lean operating environments, the challenge is to move faster while focusing underwriter attention where it drives the greatest business value.

Key capabilities that enhance underwriter efficiency without increasing headcount include:

• AI-enabled submission triage: Automates submission intake by identifying high-potential risks aligned with appetite and historical performance, enabling immediate focus on the most viable opportunities.
• Winnability scoring: Surfaces submissions with a high likelihood of binding, helping underwriters prioritize time on winnable deals and avoid expending effort on low-probability placements.
• Integrated automation and data access: Reduces manual overhead by streamlining data ingestion from internal and third-party sources, freeing underwriters to concentrate on high-value risk analysis.

By realigning workflows around underwriter judgment and portfolio strategy, MGAs can materially expand capacity and improve performance.

2. Write more in-appetite business

Many MGAs face a persistent challenge: misalignment between stated risk appetite and the business that ultimately makes it onto the books. Appetite is dynamic and shifts as the portfolio evolves, requiring real-time visibility and adaptive decision support.

AI-driven solutions enable MGAs to maintain tighter alignment between underwriting activity and strategic portfolio objectives by:

• Tracking appetite in real time: Continuously monitors bound risk across key dimensions (e.g., class, geography, limit profile), alerting underwriters to emerging over-concentrations or appetite drift.
• Prioritizing in-appetite submissions: Uses AI to sort and prioritize risks that align with current appetite, ensuring underwriters spend time on the most relevant opportunities first.
• Operationalizing strategic shifts: Instantly reflects changes in underwriting guidelines or portfolio strategy within underwriter workflows, closing the loop between leadership intent and front-line execution.

By ensuring that underwriters are always working in sync with a current, data-driven view of appetite, MGAs can improve portfolio quality and drive more consistent underwriting profitability.

3. Use data-backed guardrails to strengthen reinsurance relationships

For MGAs, access to capacity hinges on the strength of their relationships with risk capital providers. Demonstrating consistent underwriting discipline and strategic alignment is key to building trust and securing favorable terms.

AI-driven tools reinforce underwriting rigor and transparency by:

• Embedding dynamic decision support: Delivers real-time, context-specific guidance at the point of risk selection, helping underwriters consistently align with portfolio strategy and guidelines.
• Enabling real-time portfolio analytics: Offers real-time visibility into how individual decisions affect aggregate performance, empowering MGA leadership to manage exposure, track profitability, and adjust strategy as needed.
• Generating credible, data-backed reporting: Produces audit-ready evidence of underwriting discipline, supporting transparency and building credibility with capital partners.

By operationalizing strategy and making performance measurable, MGAs are better positioned to negotiate favorable reinsurance terms, deepen partner relationships, and scale profitably.

Positioning MGAs for profitable growth

By adopting AI-driven tools, MGAs can overcome structural inefficiencies, improve portfolio performance, and reinforce their value to capital partners. In a competitive and capacity-constrained market, those who invest in intelligent underwriting workflows will be best positioned for sustainable, profitable growth.