Reimagining risk: how AI is transforming commercial insurance

Discover how GenAI's scaling in consumer markets paves the way for commercial insurance to enhance efficiency and value.

Jonathan Kamp, Senior Manager, Ernst & Young LLP contributed to the article.

In brief

• GenAI’s rise in the consumer space signals a transformative chance for insurers to lead.
• Strategic investment in core operations like underwriting can greatly enhance value.
• Rapid tech advances make immediate action crucial for insurers to unlock potential.

In the face of proliferating and more severe risks, businesses need stronger risk management practices and innovative insurance solutions. But many commercial carriers struggle with outdated risk assessment and pricing methods. Generative artificial intelligence (GenAI) will revolutionize the future of commercial insurance, enabling carriers and producers to deliver what their clients need and helping to bridge the large and growing protection gap.

In pricing and portfolio management, GenAI and cognitive analysis can evaluate and price risks within an overall book of business.

The combination of AI-driven workflows and human expertise is well suited to drive value and boost underwriting performance, as early adopters are already demonstrating with breakthrough innovations. Underwriters will act as AI experts and skilled prompt engineers, with deep knowledge of large language models (LLMs) function and the ability to interpret AI-generated insights and apply them appropriately in different contexts. Commercial underwriting organizations in the future will feature new roles, including AI underwriting champions, specialists and technologists.

AI will enable collaboration in traditionally siloed roles.

Producers and underwriters will use GenAI tools to streamline communications (e.g., clarifying appetite, requesting missing information). Eliminating time-consuming (and error-prone) administrative activities will create space to focus on analytical tasks and complex submissions.

Product managers will incorporate AI and predictive analytics against larger data sets to observe product performance and market trends in near real time, opening up new possibilities to refine product offerings for both mass and niche market needs.

Pricing actuaries will focus on data science and predictive analytics, buttressed by more powerful tools, richer data and increased computing power, which will transform the role.

Getting started on the journey 

Carriers ready to embrace the AI-fueled future can act on multiple fronts.

1. Set the right strategic course: Success starts with a clear vision that defines the business objectives for transformation. Is the priority to differentiate via product innovation or by offering richer service propositions to producers? What is the primary benefit of increased efficiency – lower cost of sales or increased focus on value-adding activities? Early adopters have shortened the path to successful deployments by launching proofs of concepts and pilots designed to capture key learnings, build momentum through quick wins and establish Agile, test-and-learn capabilities for further development.

2. Prepare the talent and culture: Despite the increasing prominence of advanced technology, talent acquisition and retention will be the hallmark of tomorrow’s most effective product, actuarial and underwriting operations. GenAI will be a powerful training and coaching tool, building on the traditional apprenticeship model to accelerate learning and development through scenario modeling, risk assessment simulations, real-time feedback and personalized training modules. Indeed, some leading carriers are already using AI and related technology to train their employees and mitigate against the loss of institutional knowledge. 

3. Build the technology foundation: To make the most of GenAI, carriers will need to prepare or upgrade their technology infrastructure. The goal must be to create an environment that can support the vision of real-time underwriting. These modernized capabilities require new infrastructure and core systems, as well as GenAI platforms and tools. The key elements include:

• A comprehensive data strategy, including clear data definitions to support data quality, is critical to training GenAI models effectively.
• A modular infrastructure to support smart, seamless and secure connectivity with third parties or ecosystem partners will be critical. Flexible integration models will require sophisticated use of APIs and microservices to facilitate seamless and secure data exchange.
• Underwriting workbenches underpinned with GenAI can supercharge the underwriting process, providing underwriters with a single-pane view and offering breakthrough gains in efficiency.
• An updated IT operating model that reflects the fact that the use of GenAI, machine learning and continuous data and model management are all standard practices.

4. Solve for compliance and security: In the age of AI, the regulatory stakes continue to rise with authorities seeking stronger safeguards for customer data, as well as transparency and auditability of both AI inputs and outputs. Governance principles can also be embedded directly into AI algorithms and workflows to help protect against data leaks, breaches and cyber threats. Ethical and responsible use of AI is the overarching goal.