Artificial intelligence (AI) is driving significant change across many industries, including the property and casualty (P&C) insurance sector. AI is reshaping how insurers handle claims, from streamlining processes to improving accuracy and customer satisfaction. As the race to adopt AI in claims management intensifies, organizations face a crucial decision: Should they develop a custom AI solution in-house, or should they opt for an existing third-party solution?

The appeal of building an in-house solution is understandable—it offers the potential for a system designed specifically for your organization's unique needs. However, the challenges and complexities of creating an AI platform for P&C claims must be carefully considered. Here’s a guide to help you navigate this decision, with a focus on how Milliman Nodal can serve as a solution provider.

Assembling the right team

Developing a robust AI platform for P&C claims management requires more than just a few data scientists. You’ll need a comprehensive team that includes product managers, software engineers, data engineers, claims experts, and operational specialists. This diverse group must work in unison to create a system that integrates with your existing workflows and meets the specific demands of P&C claims processing.

However, assembling and maintaining such a specialized team can be a significant challenge. The expertise required spans multiple disciplines, and the costs, time, and resources involved in recruiting and retaining top talent can be substantial. Additionally, even with the right team in place, the complexities of building a scalable, efficient AI solution from scratch are more demanding than anticipated. For many insurers, this hurdle may make the prospect of building an in-house solution less attractive than it initially seems.

Outsourcing your AI solution to a third party ensures that you have access to a fully assembled team of experts who specialize in AI and P&C claims. These teams are comprised of skilled professionals who understand the intricacies of AI development and claims processing. With their extensive experience, they have already navigated the common pitfalls and challenges associated with implementing AI in the insurance space.

By choosing a third-party solution, you not only save the time and effort required to build and train an internal team but also gain the confidence that your AI platform is being developed by professionals who know exactly what they’re doing. This allows your organization to focus on core operations while leveraging cutting-edge technology created and supported by industry experts.

Leveraging data effectively

The effectiveness of any AI solution in P&C claims hinges on the quality and quantity of data available. Insurers with large amounts of claims data might consider developing an in-house solution. However, even large organizations often face gaps or biases in their data, particularly when it comes to less common claims scenarios or emerging risks.

Some third-party vendors have access to extensive, anonymized, and aggregated data from a wide range of sources. This allows for the rapid training of AI models that can handle a diverse array of claims scenarios, providing a level of insight that may be challenging to achieve in-house.

Balancing customization and maintenance

An in-house solution offers the advantage of customization, enabling you to tailor the system to your specific P&C claims processes. However, customization is not a one-time effort—AI systems need to be continuously updated to stay relevant in the continually evolving insurance landscape.

With a third-party solution, you gain the benefits of ongoing enhancements, updates, and maintenance as part of the package. This ensures that the AI system remains aligned with industry changes and your organization's evolving needs.

Maintaining focus on core operations

Implementing an AI solution for P&C claims is just the beginning. Maintaining and improving the system requires a dedicated focus, particularly as claims patterns shift and new types of claims emerge. Regular model updates, bug fixes, and feature enhancements are essential to keep the system performing at its best.

Third-party solutions offer continuous support and improvements, freeing your organization to focus on core operations while ensuring that your AI platform stays cutting-edge.

Ensuring data security

Security is a paramount concern in the P&C insurance industry, particularly when dealing with sensitive claims data. In-house solutions offer control over data security, but third-party vendors must meet rigorous standards to ensure data protection.

When evaluating third-party options, it's essential to review their security credentials. Certifications such as System and Organization Controls (SOC) 2 Type II demonstrate a commitment to maintaining the highest level of data security.

Accelerating time to value

Developing an in-house AI solution can be a lengthy process, from assembling the team to operationalizing the platform. If your organization needs to capture value quickly, a third-party solution can significantly reduce the time to deployment and impact.

Evaluating costs

Cost is a significant factor in the build-versus-buy decision. While an in-house solution might seem more cost-effective at first glance, the full scope of expenses—including staffing, training, infrastructure, and ongoing maintenance—must be considered. Third-party solutions offer predictable costs that include support, performance improvements, and hosting, providing a clear financial picture.

Conclusion

The decision to build or buy an AI solution for P&C claims management is complex and multifaceted. While an in-house solution offers customization and control, the challenges of staffing, data, ongoing maintenance, and security cannot be overlooked. Third-party solutions provide a compelling alternative, offering expert teams, extensive data, continuous updates, and robust security measures—all with predictable costs and faster time to value. Navigating the AI journey in P&C claims requires careful consideration of these factors to make the best decisions for your organization.

About Nodal

Milliman’s Nodal is a predictive model for early claims intervention and cost reduction. Nodal uses advanced artificial intelligence (AI) technologies to identify high-cost and low-cost claims soon after reporting, allowing for efficient triage of claims and allocation of resources that maximize the use of staffing and cost containment strategies. Milliman’s team of actuaries, claims professionals, and data engineers has developed an end-to-end solution that is fully supported through implementation, deployment, and assessment.