What is RiskMatch?
RiskMatch is a data analytics and placement platform designed for commercial insurance brokers who need to analyze, benchmark, and place complex commercial risks. Originally founded in 2013 in Stamford, Connecticut as an independent insurtech startup, RiskMatch was acquired by Vertafore in 2018 — the same company that owns AMS360 and Sagitta, the two most widely used agency management systems in the US. This acquisition gave RiskMatch access to an unprecedented dataset: the policy and claims data flowing through tens of thousands of agencies using Vertafore's AMS platforms. For commercial insurance brokers, placement — matching a client's risk profile with the carrier most likely to offer favorable terms — has traditionally been a relationship-driven, intuitive process: the broker knows which carriers "like" which types of risks based on experience. RiskMatch transforms this process into a data-driven one, using analytics trained on millions of commercial policies to answer: for this specific risk profile, which carriers have historically written similar risks, what premiums and terms did they offer, and how does this risk compare to industry benchmarks? As of 2026, RiskMatch has analyzed over 50 million commercial insurance transactions and is used by thousands of commercial lines brokers to improve placement outcomes — higher hit ratios (percentage of submissions that become binds), better premium-to-exposure ratios, and faster placement cycles.
The core problem RiskMatch solves is information asymmetry in commercial insurance placement. When a broker receives a commercial risk submission — a manufacturing company seeking general liability and property coverage, or a technology company seeking cyber and professional liability — they need to decide which carriers to approach. The wrong choice wastes time: if a carrier doesn't have appetite for that type of risk, the submission goes nowhere, and the broker loses days or weeks before moving to the next carrier. Meanwhile, the client is waiting and may be receiving competing quotes from other brokers. RiskMatch's analytics engine analyzes the risk characteristics (industry, revenue, employees, location, claims history, coverage needs) and compares them against a database of carrier appetite and historical placement data to recommend the carriers most likely to quote competitively. For a broker placing a $500,000 manufacturing account, getting the carrier shortlist right on the first try can mean the difference between winning the account in 2 weeks and losing it to a faster competitor. Brokers using RiskMatch report an average 30% improvement in hit ratios (the percentage of submissions that result in a quote) and a 20% reduction in placement cycle time.
Key Features
Risk Benchmarking & Portfolio Analysis
RiskMatch's benchmarking engine allows brokers to compare an individual risk or an entire portfolio against industry norms. For an individual risk, the broker can see: how the client's premium, loss ratio, and coverage structure compare to similar businesses in the same industry, revenue band, and geography; whether the client is over-insured or under-insured relative to peers; and which coverage enhancements other similar businesses typically purchase. For example, a broker might discover that a $10M revenue construction client is carrying $2M in general liability limits while the industry benchmark for similar contractors is $5M — a clear coverage gap that represents both a risk to the client and a revenue opportunity for the broker. For portfolio-level analysis, agency principals can see: how their entire book of business compares to market averages across lines (workers' comp loss ratios, property premium-per-$1,000-of-value, etc.), concentration risk (are too many clients in one industry, geography, or with one carrier?), emerging risk trends (which industries in the portfolio are showing increasing claims frequency or severity), and carrier performance (which carriers are providing the most competitive terms for the agency's typical risk profile). This portfolio-level analysis helps agency leaders make strategic decisions: which industries to target for growth, which carriers to deepen relationships with, and where the agency's book may be underpriced relative to risk — creating vulnerability to carrier non-renewals or rate increases.
Carrier Appetite Matching & Market Intelligence
The carrier appetite matching engine is RiskMatch's most-used feature and the primary driver of its ROI for brokers. When a broker inputs a risk profile, the engine analyzes it against a continuously updated database of carrier appetite data — which carriers are actively writing which types of risks, in which geographies, at which premium levels, with which coverage requirements. The appetite data comes from multiple sources: actual placement data from the Vertafore ecosystem (real policies that were written — showing which carriers actually bound which risks, not just which carriers said they were interested), carrier-submitted appetite guides (formal statements from carriers about their target risks, updated quarterly), and broker-contributed intelligence (brokers can tag carriers with notes about their recent experience — "Carrier X has been aggressively quoting middle-market manufacturing in the Southeast," "Carrier Y is tightening underwriting on habitational risks"). The engine generates a carrier shortlist ranked by match score: carriers with the strongest appetite and best historical outcomes for this type of risk appear first. Each carrier listing includes: appetite strength score (1-100), historical win rate for similar risks, average premium and terms, typical turnaround time, and any known restrictions or exclusions. The engine also identifies "stretch" carriers — carriers that don't explicitly target this type of risk but have occasionally written similar risks at competitive terms, representing an opportunity for brokers willing to invest the extra effort in submitting to a less obvious market. For complex or hard-to-place risks (environmental liability, directors & officers for distressed companies, coastal property with high catastrophe exposure), the carrier matching engine is particularly valuable because the universe of willing carriers is small and sending submissions to the wrong carriers wastes precious time.
Placement Analytics & Performance Tracking
RiskMatch's placement analytics module tracks the performance of the brokerage's placement efforts and identifies opportunities for improvement. Key metrics include: hit ratio by carrier, industry, broker, and office (what percentage of submissions result in a quote? What percentage of quotes result in a bind?); cycle time analysis (how long does it take from submission to quote, and from quote to bind, broken down by carrier and risk complexity — identifying carriers that are consistently slow to respond); declination analysis (for submissions that were declined, why? Is there a pattern — certain industries or risk characteristics that are consistently declined across carriers — that suggests the agency should pre-qualify those risks differently or adjust its carrier panel?); premium benchmarking (how do the premiums obtained for similar risks compare across carriers and against market averages — identifying whether the agency is consistently getting competitive pricing or leaving money on the table); and broker performance comparison (within multi-broker agencies, identifying which brokers have the best placement outcomes and what practices differentiate them). This visibility transforms placement from an art into a science — instead of relying on individual brokers' relationships and intuition, agency leaders can identify systematic patterns and make data-driven decisions about carrier relationships, broker training, and market strategy.
AMS Integration & Data Enrichment
As a Vertafore product, RiskMatch has deep, native integration with AMS360 and Sagitta — the two AMS platforms used by the majority of mid-to-large commercial lines brokerages in the US. For agencies on these platforms, RiskMatch can pull policy data directly from the AMS with minimal setup — enriching the AMS data with benchmarking and analytics without requiring separate data exports or manual uploads. For agencies on other AMS platforms (Applied Epic, Applied TAM, etc.), RiskMatch supports data integration through file exports and API connections, though the experience is less seamless than the native Vertafore integration. The data enrichment works bidirectionally: RiskMatch reads policy and claims data from the AMS to power its analytics, and writes analytics insights back to the AMS so that brokers can see benchmarking data and carrier appetite recommendations directly within their existing AMS workflow. This inline integration is critical for adoption — brokers don't want to log into a separate system for analytics when they are working on a placement; they want the analytics to appear in the tool they are already using.
Predictive Modeling & Emerging Risk Detection
RiskMatch's predictive modeling capabilities help brokers look forward rather than backward — identifying risks and opportunities before they become obvious. Key models include: loss ratio prediction (based on a risk's characteristics and historical data for similar risks, what is the predicted loss ratio over the next policy period? This helps brokers identify accounts that are likely to experience adverse loss development — enabling proactive risk management conversations with clients before claims spike); renewal pricing prediction (based on market conditions, carrier behavior, and the risk's performance, what is the likely renewal premium range? This helps brokers prepare clients for potential increases and start re-marketing early if needed); emerging risk identification (analysis of industry-level claims data to identify risk types that are experiencing increasing frequency or severity — e.g., social engineering fraud claims in financial services, extreme weather claims in certain geographies, cyber claims in healthcare — enabling brokers to proactively recommend coverage enhancements to clients in affected industries); and cross-sell opportunity scoring (for existing clients, which additional lines of coverage are they most likely to need and purchase, based on industry benchmarks and peer behavior?). These predictive capabilities position the broker as a strategic advisor rather than a transactional intermediary — bringing data-driven insights to client conversations that add value beyond the placement transaction itself.
How Brokers Use RiskMatch in Practice
RiskMatch is not a tool that brokers use all day, every day — it is a strategic resource deployed at specific moments in the placement cycle where data-driven decisions have the highest impact. The most common usage patterns among experienced RiskMatch users: New business pre-qualification: before committing significant time to a prospect, the broker runs the risk through RiskMatch to confirm that viable markets exist and to get a preliminary indication of likely premium range. If RiskMatch shows that only 2 carriers have appetite and both are quoting 30% above market, the broker can have an honest conversation with the prospect before investing weeks in a losing effort. Renewal strategy: 90-120 days before a major account renews, the broker uses RiskMatch to benchmark the current program against market and identify alternative carriers. This data arms the broker for the renewal negotiation — "Based on current market data, your premium is 15% above benchmark for similar risks. We recommend re-marketing to these 3 carriers who have been quoting aggressively in your industry." Carrier panel management: agency leaders periodically analyze their entire book through RiskMatch to identify carriers that are underperforming (consistently high premiums, slow response times, restrictive terms) and carriers with growing appetite in the agency's sweet spot — informing decisions about which carrier relationships to invest in. Producer coaching: agency principals use RiskMatch data to coach producers — identifying which producers are sending submissions to the wrong carriers (low hit ratios) or accepting below-market premiums (leaving commission dollars on the table). These practice patterns are not theoretical — they reflect how top-performing commercial brokerages integrate RiskMatch into a data-driven placement culture rather than relying solely on individual broker intuition and carrier relationships.
Pros & Cons
Pros
- Unmatched dataset via Vertafore integration: Access to 50M+ anonymized commercial insurance transactions gives RiskMatch benchmarking and carrier appetite data that no independent startup can replicate. This data moat is the platform's strongest competitive advantage.
- Native AMS360/Sagitta integration makes adoption frictionless: For the majority of the commercial brokerage market that uses Vertafore AMS products, RiskMatch appears within the existing workflow — no separate login, no data export, no learning curve. This integration depth is the key driver of daily active usage.
- Carrier appetite matching directly improves placement outcomes: The feature that most directly impacts revenue — knowing which carriers to approach — is RiskMatch's strongest capability. Brokers consistently report higher hit ratios and faster cycle times when using the carrier matching engine.
- Portfolio-level analytics enable strategic agency management: Beyond individual placement support, RiskMatch gives agency leaders visibility into their entire book's health — concentration risk, carrier dependency, profitability trends — that supports better business decisions.
Cons
- Vertafore ecosystem dependency — best experience requires AMS360 or Sagitta: Agencies on Applied Systems platforms (Epic, TAM) or other AMS platforms get a diminished experience. While RiskMatch supports data integration with non-Vertafore platforms, the native integration depth is not available. For Applied-dominant agencies, the value proposition is weaker.
- Pricing is opaque and bundled with larger Vertafore contracts: RiskMatch is typically sold as part of a broader Vertafore product suite — making it difficult to isolate its cost and ROI. Small agencies may find the minimum contract size prohibitive.
- Predictive models are only as good as input data quality: The benchmarking and prediction features require clean, complete AMS data. Agencies with inconsistent data entry will see lower-quality insights. Data cleanup is often a prerequisite for realizing the platform's full value.
- Limited to commercial lines — no personal lines support: RiskMatch is exclusively for commercial insurance. Personal lines agencies will find no value in the platform.
FAQ
How is RiskMatch different from just knowing your carriers?
Experienced brokers develop strong intuition about carrier appetite through years of placements — but this intuition is inherently limited to the broker's personal experience. A broker who has been placing manufacturing risks for 15 years knows which carriers like manufacturing, but they don't know about carriers they've never used, and they may miss shifts in carrier appetite. RiskMatch's data covers millions of placements across thousands of agencies — providing a much broader view of the market than any individual broker's experience. It also captures changes in carrier appetite in near-real-time (as new placement data flows in) while a broker's intuition may be based on outdated information.
Is RiskMatch only for large commercial brokerages?
RiskMatch's sweet spot is mid-to-large commercial brokerages placing accounts with $25,000+ in premium. Very small commercial accounts (BOP-level, under $10,000 premium) are typically placed through standardized markets where carrier appetite is well-known and the analytics value is limited. However, for any brokerage that regularly handles complex or hard-to-place commercial risks — regardless of overall agency size — RiskMatch provides meaningful value. Vertafore offers scaled-down RiskMatch packages for smaller agencies.
Does RiskMatch share my agency's placement data with other agencies?
RiskMatch uses anonymized, aggregated data from across the Vertafore ecosystem to power its benchmarking and carrier appetite models — but individual agency data is never shared in identifiable form. The benchmarking data shows industry averages and distributions, not individual agency or client performance. Carriers cannot see which specific agencies are submitting which risks. Vertafore's data governance is governed by its SOC 2 Type II certification and customer data processing agreements.
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