MGAs & MGUs: Underwriting in the New Dark Data Era

MGAs/MGUs depend on data to forecast losses, set policy prices, and manage financial risks. Although publicly available data represents just one component of the underwriting process, recent governmental changes and efficiency-focused measures have created significant gaps in previously reliable data streams, posing new challenges for insurers.

Environmental Risk Data
Recent budget reductions at the National Oceanic and Atmospheric Administration (NOAA) occurred shortly before this year’s hurricane season. NOAA plays a critical role in providing historical data on flood zones, wildfire activity, and hurricane occurrences. Without access to such data and modeling, insurers face significant difficulties accurately predicting catastrophic risks.

Crime Statistics
Property and casualty (P&C) insurers depend heavily on crime statistics to accurately gauge risk. Crime data significantly influences underwriting decisions in several insurance categories. Public access to the Department of Justice’s crime reports was removed earlier this year, according to a recent ALM PropertyCasualty360 article.

Climate Change Projections
Insurance companies across various segments, including homeowners’, business owners’ (BOP), and inland marine, have traditionally relied on climate-related data to accurately price policies. This data helps insurers assess risks associated with extreme weather events like heat waves and severe freezing conditions. In January 2025, the U.S. Department of Agriculture removed online content referencing the climate information, potentially impacting insurers’ ability to accurately predict and respond to climate-related disasters.

Insurers are already seeking alternative data streams, like private data providers, and increasing reliance on internal proprietary databases. However, these alternatives carry concerns about data quality and often come with significant costs both financial and operational: 

Increased Processing Time
Without reliable public data sources, MGAs and MGUs must manually compile, validate, and normalize data from various private sources. This process can extend underwriting timelines by 30-40%, delaying quote issuance and potentially losing business to more agile competitors.

Resource Diversion
Technical resources previously focused on improving core underwriting processes are now being redirected to data acquisition and validation. This shift creates operational bottlenecks and diverts attention from value-added activities like program development and client service.

Data Lake Integration Challenges
As highlighted in our previous discussions on data lakes, unstructured and inconsistent data requires significant transformation before it becomes useful. When source data quality varies, the effort to standardize it for data lake integration multiplies, creating processing inefficiencies throughout the organization. As public data sources deteriorate, solutions like Patra’s Policy Data Extract AI can become a critical lifeline:

• Rapidly process and structure diverse document types to supplement lost public data
• Provide 99% accuracy in data extraction, reducing validation requirements
• Automate the normalization of data from multiple alternative sources
• Enable quick integration of new data streams as they become available

In response to a 2024 series of insurer insolvencies involving affiliated MGAs, Louisiana updated its regulations for MGAs through House Bill 672, aligning many aspects with the National Association of Insurance Commissioners (NAIC) Model Act. The legislation introduced new financial reporting requirements, expanded examination rights for regulators, and additional responsibilities for MGAs and their affiliated insurers. The revised laws now apply broadly to MGAs/MGUs, program administrators, insurers, and investors.

A recent analysis by Pro Global of over 200 U.S. audits highlights significant compliance gaps within MGAs and TPAs. On average, each audit identified five issues, with 55% classified as high priority. The most pressing issues focused on adhering to authorized binding limits, conducting thorough sanitation checks, and ensuring complete and accurate policy documentation. Amid increasing regulatory expectations and oversight, continued reliance on manual policy checking processes creates significant financial exposure risks for MGAs/MGUs:

• Increased E&O risk from manual processing errors
• Higher operational costs from inefficient workflows
• Lost revenue opportunities due to processing delays
• Potential financial impact of carrier audits

Current business challenges, such as increasing weather-related disasters, have accelerated the adoption of digital solutions by MGAs/MGUs. Automation and digitization have become commonplace for handling premium collections, claims notifications, and routine customer interactions.

But MGA/MGU’s digital transformation journey hasn’t been without challenges. While insurance distributors have aggressively adopted and invested in new digital tools, underwriting has not seen significant efficiency improvements. Automating processes that require personal engagement — like selling additional coverage or comparing detailed policy quotations remains challenging. Many MGAs/MGUs are cautious about fully digitizing these interactions, fearing the loss of the unique value they provide.

A 14-year longitudinal study of North American underwriters conducted by Accenture and The Institutes revealed that 75% of P&C underwriters rated their organizations as either “average” or “deficient” in utilizing technology to enhance underwriting efficiency. Only 25% indicated that at least half of the underwriting process is automated. Additionally, underwriters found the implementation of Robotic Process Automation (RPA) and Natural Language Processing (NPL) to be lacking. Among personal lines underwriters, 32% rated their organizations’ use of RPA and NLP as superior, compared to just 16% of commercial lines underwriters and 10% of specialty underwriters.

Today’s MGAs/MGUs are operating in a new environment of data constraints and heightened regulatory oversight. The stakes for accuracy and efficiency have never been higher. Solutions like Policy Checking AI and Policy Data Extract AI can transform operational challenges and data quality concerns into efficiency advantages – ensuring program compliance, stronger carrier relationships, and accelerated growth. By embracing these technologies, MGAs/MGUs can better meet regulatory expectations, enhance business resilience, and position themselves for sustainable growth in an increasingly challenging market.