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

Breaking Through the Challenges of Commercial Underwriting

Commercial lines underwriting sits at the heart of the insurance value chain, yet it remains one of the most complex and underserved areas in the industry. The ability to analyze and price risk effectively is what separates leading insurers from the rest. Historically, underwriting has been a blend of art and science—relying on technical models, exposure information, and risk appetite, but also on subjective judgment. This was once justified by limited data and technology. Today, however, the abundance of data and advanced computing power offers a new opportunity: to make commercial underwriting more scientific, data-driven, and efficient.

Despite this potential, commercial underwriting is still hampered by significant challenges:

• Fragmented, messy submissions: Underwriters spend over 40% of their time on non-underwriting tasks, such as rekeying data and generating documents, due to fractured submission processes.
• Lack of real-time management information: Without real-time visibility into portfolio concentration and trends, underwriters often prioritize the wrong opportunities, missing out on margin or growth.
• Manual, fragmented decisioning: Multiple handoffs across disparate systems (CRMs, spreadsheets, emails, third-party databases) increase the risk of error and slow down the process.
• Low-quality, siloed data: Data is often scattered and inconsistent, making it difficult to join and analyze in time to inform decisions.
• Slow speed of change: Adding new modeling factors or data sources is cumbersome, often taking months due to legacy system complexity.

The Opportunity: Embedding Data at the Core of Underwriting

The nature of risk is evolving—climate change, technology, and new economic realities demand new products, data sources, and rapid innovation. As startups and digital-native competitors gain ground, incumbent insurers must transform their underwriting processes by embedding data and AI at the core.

Embedding data at the core means treating data as a strategic growth lever, not just a set of tools. This approach delivers:

• Value: Improved risk selection, faster response times, higher conversion rates, and greater underwriter productivity.
• Speed: Shorter cycle times and accelerated product innovation.
• Quality: Better decision-making, improved loss ratios, and reduced risk of errors.

The Pathway to Modernizing Underwriting

A successful transformation requires a step-by-step approach:

1. Strategy: Identify Points of Leverage

Business, technology, and data teams must collaborate to pinpoint where data can accelerate and improve underwriting. Key questions include:

• How can data improve our underwriting processes?
• What decisions can be better informed with more or better data?
• Where can automation drive efficiency?

Five key data-centric plays in the underwriting journey:

1. Automated ingestion and triaging of submissions: Automatically read and prioritize broker submissions, triaging cases to the right underwriter based on historical data and portfolio needs.
2. Enhanced underwriting and decisioning: Present underwriters with decision-ready risks, enriched by AI-driven insights and contextual prompts.
3. Real-time portfolio steering and scenario management: Use real-time dashboards to monitor exposures, test scenarios, and adjust pricing or risk appetite dynamically.
4. Continuous improvement of decisioning and data ingestion: Rapidly incorporate new claims data and external sources, using AI to suggest updates to pricing or risk appetite.
5. New product innovation: Leverage claims and market data to develop and test new risk solutions, such as parametric or IoT-enabled products.

2. Capabilities: Build Infrastructure, Analytics, Operations, and Culture

Insurers must assess their maturity across four dimensions:

• Business operations: Identify where underwriters spend time on non-core tasks and streamline handoffs.
• Infrastructure and data: Ensure submission, pricing, and claims data are accessible and differentiated.
• Analytics: Make data reliable, accessible, and aligned across teams with clear definitions and dashboards.
• Team and culture: Foster data literacy and empower teams to use self-serve analytics and decision frameworks.

3. Pathway: Transition States to Maturity

Transformation is not a one-off project but a journey of continuous improvement:

• Set strategy: Reorient the organization around data, prioritizing key leverage points.
• Develop a focused minimum marketable product (MMP): Use a “steel-thread” approach—build a small, end-to-end slice of functionality to prove value quickly.
• Implement and iterate: Launch the first use case, gather feedback, and refine. Establish clear accountability and data governance.
• Scale and industrialize: Expand to additional use cases, embed continuous improvement, and build a culture of experimentation and learning.

Data-Centric Plays in Action: Industry Examples

• CFC: Automated underwriting and data enrichment enable rapid product development and instant binding quotes.
• Ki Insurance: Algorithmic risk evaluation via a broker portal, achieving significant growth and efficiency.
• AXA XL: A digital ecosystem consolidates enterprise data, enabling cross-selling and faster policy launches.
• Cytora: Digitizes and triages risks, demonstrating the power of intelligent, data-driven underwriting.
• Hyperexponential: A pricing decision intelligence platform handling billions in contracts annually.

The Role of Automation and AI in Risk Selection and Pricing

AI and automation are transforming risk selection and pricing by:

• Reducing underwriting leakage: AI analyzes vast datasets, combining historical claims, demographics, and external data to refine risk assessments.
• Lowering loss adjustment expenses: AI models predict reserves and analyze unstructured data, such as images, more efficiently.
• Detecting fraud: AI-powered image recognition and claims analysis quickly flag suspicious activity.
• Identifying underserved markets: Data-driven insights reveal new opportunities for growth and inclusion.

Building the Foundation for Continuous Improvement

To sustain transformation, insurers must:

• Modernize legacy systems: Move to cloud-native, composable architectures that enable rapid integration of new data sources and applications.
• Invest in data governance and quality: Establish robust frameworks to ensure data integrity and accessibility.
• Empower teams: Build multidisciplinary squads with the autonomy to experiment, iterate, and scale successful innovations.
• Foster a culture of learning: Encourage continuous feedback, upskilling, and cross-functional collaboration.

How Publicis Sapient Can Help

Few incumbents have fully realized the promise of data-driven underwriting. Many struggle to get started or become bogged down in large, slow-moving programs. Publicis Sapient brings deep expertise in strategy, data, and customer experience to help insurers:

• Rapidly assess opportunities and maturity
• Articulate a clear vision and strategy for change
• Build and scale end-to-end proofs of concept
• Navigate the journey from legacy to digital-native underwriting

The Future of Commercial Underwriting

The future belongs to insurers who embed data and AI at the core of their underwriting journey—delivering faster, smarter, and more resilient operations. By modernizing infrastructure, investing in analytics, and building a culture of continuous improvement, commercial insurers can unlock new value, drive growth, and secure their place in a rapidly evolving market.

Ready to transform your underwriting for the digital age? Connect with Publicis Sapient to start your journey.