PUBLISHED DATE: 2025-08-13 23:21:37

A Digital Future for Insurance: Embedding Data at the Core of Commercial Lines Underwriting

Breaking Through the Challenges of Commercial Underwriting

Underwriting is the heart of insurance. The ability to effectively analyze and price risks distinguishes good underwriters from great ones, and profitable insurers from unprofitable ones.

Underwriting is part art and part science. Underwriters blend technical models, exposure information, risk appetite, and strategy to come to a decision. The artistry is much more pronounced in commercial lines than personal lines, where risks are more diversified and complex, with more bespoke pricing models.

In an ideal world, underwriters would spend most of their time understanding and underwriting risk, and collaborating with colleagues to find new solutions to underwrite and price better. Yet the reality is quite different. Multiple studies indicate that insurers spend more than 40 percent of their time performing non-underwriting activities: rekeying data, generating documents, and supporting sales or marketing.

Why? The current underwriting process is fragmented, inefficient, relies on poor-quality data, and is slow to change. Here are five common challenges we’ve seen:

  1. Fractured Submissions:
    • Insurers receive messy, unstructured submissions from multiple sources that need to be manually rekeyed and categorized by an underwriter or offshore support.
    • Underwriters will often not input submissions they know will be rejected. While this may be a sensible use of time, it muddles the process and data received.
  2. Lack of Real-Time Management Information (MI) for Efficient Prioritization:
    • Underwriters often lack real-time MI on their portfolio, limiting visibility into concentration across sector, geography, and client, as well as into how conversion rates might have changed over time.
    • As a result, underwriters tend to prioritize simpler or “louder” requests, rather than those where they may win margin or want to expand their footprint. This results in inconsistent prioritization, often disconnected from the aggregate risk appetite.
  3. Fragmented Decisioning:
    • The decisioning process is often manual and highly customized, with multiple handoffs. Models are not linked together, and external data must be manually retrieved and rekeyed, increasing the risk of human error.
    • For example, an insurer may rekey a submission into at least six systems: CRM, Excel-based rating models, flood forms, third-party data systems, emails to the broker, and, eventually, the policy admin system.
  4. Low-Quality and Siloed Data:
    • Insurers have lots of data, but it is often of poor quality and siloed across various systems, spreadsheets, and documents, inhibiting proper use.
    • For example, claims data may be stored separately from underwriting data, so actuaries struggle to access claims history to adjust pricing models.
  5. Slow Speed of Change:
    • It can often take months to add a new rating factor to an existing product and even longer to add a new data source to an existing product.
    • This is driven by the complexity of running large monolithic systems that are hard to test and change, and large change stack setups that make it hard to test and learn.

The Opportunity: Embedding Data at the Core of Underwriting

The underwriting process needs to be transformed. For context, Lloyd’s of London only introduced electronic trading in 2016. Meanwhile, commercial insurer incumbents and startups are evolving and taking share. Key players are investing in new underwriting solutions and data platforms, improving their use of data and risk selection, and finding new models for underwriting.

The nature of risks is also changing. While economic concerns dominated 10 years ago, technology, extreme weather, and climate change prevail today. Carbon transition, AI products, and increased natural catastrophe prevalence all require new product innovation, new data sources, and an efficient pace to seize the space.

In the face of legacy underwriting processes, the changing nature of risk, and an evolving competitive landscape, commercial incumbents face a clear impetus to transform the underwriting process and embed data at its core.

By embedding data at its core, we mean viewing data as a strategic lever of growth (rather than just a set of tools) and developing a clear view of the strategy and capabilities needed to support it.

Better use of data can offer improved value, speed, and quality:

The Pathway to Modernizing Underwriting

There are three key steps to placing data at the core of underwriting:

  1. Strategy: The points of leverage for data to accelerate and improve underwriting.
  2. Capabilities: The infrastructure, analytics, operations, and people needed to support this.
  3. Pathway: The key transition states to maturity.

(This builds on Reforge’s Scaling Data Framework, which is targeted at startups, but we build on this with practical examples for insurers and how we’ve seen this brought to life for large organizations.)

Strategy: Points of Leverage

To start, business, technology, and data teams need to come together to identify the points of leverage that data can offer across the underwriting process. Some key questions to ask:

There are five key points of leverage across the underwriting journey:

  1. Automated ingestion and triaging of submissions
  2. Enhanced underwriting and decisioning
  3. Real-time portfolio steering and scenario management
  4. Continuous improvement in pricing
  5. New product innovation

Automated Ingestion and Triaging of Submissions:

Enhanced Underwriting and Decisioning:

Real-Time Portfolio Steering and Scenario Management:

Continuous Improvement in Pricing:

New Product Innovation:

Identifying the unique set of points of leverage, which will differ for each insurer depending on their overall business roadmap and strategy, helps align both business and technology teams around a common vision and outcomes.

Capability: Infrastructure, Analytics, Operations, and People

Driven by these strategic opportunities, insurers should evaluate the maturity of their current underwriting and data capabilities. Here are some guiding questions to ask across four key dimensions:

Business Operations (Processes across underwriting and other functions, e.g., finance, risk):

Infrastructure and Data (Tools and architecture to manage underwriting and use of data):

Analytics (Dashboards and analysis used to generate insights across the company):

Team (Maturity of understanding and usage of data across the organization):

Pathway: The Transition States to Maturity

The above process will highlight the major gaps in capabilities, as well as force some difficult conversations around the priorities and sequencing of change. In some companies, the infrastructure and data activities may be ahead of the teams’ ability to make use of it (for instance, because a new enterprise data platform has just landed). At this point, the right step is to refocus on the problems appropriate for the current needs and maturity of the business.

Initially, the focus should be on ensuring trustworthy and available operational data. Underwriters need a common set of metrics they can trust for day-to-day operations that is readily available to everyone. Efficiency and morale deteriorate very quickly when commercial and finance teams start disagreeing over definitions of written premiums or retention.

From an underwriting perspective, this goes hand in hand with the automatic ingestion (and validation) of submissions and a single portfolio view of risks.

In practice, this means establishing KPI dashboards at both the company and team level, data dictionaries to ensure metric definition and alignment, powered by a robust data warehouse with the most commonly used areas of data.

Next, insurers should look to drive data into the decision-making process across every stage of the underwriting journey. Once teams have a trusted foundation of data, the key is to ensure that 1) the right data is in place for decision-ready risks, and 2) a clear decision-making framework is in place across teams. Owners need to be established for lower-level metrics (e.g., number of hours to a decision, percentage of retention) to drive accountability, with organizational realignment around value streams/journeys where appropriate.

At this stage, underwriters should begin to have automated workflows that present them with decision-ready risks and have the ability to test scenarios based on forecast data.

In practice, this means building self-service data products to enable more exploration and analysis, powered by data lakes and customer data platforms. A robust data governance framework and investment in data quality automation also become key at this stage to enable scale.

Finally, insurers should look to industrialize a culture of continuous improvement. As underwriting teams begin to make quality, data-driven decisions day to day, the focus shifts to driving continuous change and improvement at scale. This can start to take place in parallel with previous stages but requires a robust data foundation, decision framework, and processes to truly work at scale.

At this stage, there should be a closed loop between actuaries, underwriters, and commercial teams to improve pricing, tweak capacity, and develop new products. Underwriting journeys become further augmented by generative AI-based recommendations and automations.

This means building out machine learning operations and experimentation platforms to test and learn across the broader journey (e.g., marketing campaigns, journey flow, and retention prompts). Data should largely be self-serviced in near-real time across the organization.

Examples of Data-Centric Plays in Commercial Insurance

How Publicis Sapient Can Help

In practice, very few incumbents have managed to successfully transform their underwriting process and use of data. In many cases, insurers will acknowledge the need for change but struggle to get started. Others get bogged down in big programs that fail to deliver (continuous) value.

Publicis Sapient is well-placed to support insurers who want to explore this transition. We’ve worked with insurers and other major financial services providers across the full lifecycle of transformation, putting data at the core of their businesses.

Our team of strategy, data, and customer experience experts can help insurers navigate a clear path for transformation. We can rapidly assess the opportunities available, supported by customer research and technology vendor insight, articulate a clear vision and strategy for change, and build functioning end-to-end proofs of concept in a matter of months.

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About Publicis Sapient

Publicis Sapient is a digital transformation partner helping established organizations get digitally enabled, both in the way they work and the way they serve their customers. We help unlock value through a start-up mindset and modern methods, fusing strategy, consulting, and customer experience with agile engineering and problem-solving creativity. As digital pioneers with 20,000 people and 53 offices around the globe, our experience spanning technology, data sciences, consulting, and customer obsession – combined with our culture of curiosity and relentlessness – enables us to accelerate our clients’ businesses through designing the products and services their customers truly value.

Publicis Sapient is the digital business transformation hub of Publicis Groupe. For more information, visit publicissapient.com

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