Improve Life Insurance Underwriting: 3 Ways to Evolve Your Processes

As customer expectations change, it’s imperative that life insurance companies consider underwriting process improvement, including using artificial intelligence (AI) to enhance approaches. In this blog post, we examine three ways that life insurers can evolve their underwriting processes.

In brief:

• Generative AI dramatically reduces underwriting timelines, significantly cutting into the traditional underwriting cycle, which is 45 to 60 days. This directly improves “not taken rates” and customer satisfaction.
• Information collection acceleration, business process management evolution, and AI and machine learning integration each offer immediate return on investment (ROI) opportunities for life insurers.
• Modern generative AI-powered robotic process automation (RPA) and intelligent orchestration can enhance existing systems rather than requiring costly full-scale replacements.
• Unlike traditional ML models that require manual updates for new risk categories, generative AI can automatically recognize and adapt to emerging risk patterns without reprogramming.
• Generative AI enables dynamic, behavior-based customer experiences that adapt in real time to individual risk profiles and preferences.

In 2022, I wrote a blog post describing the long-standing inefficiencies that insurance underwriters face as a “recurring dream” — familiar, frustrating and stubbornly resistant to change.

I documented how business process automation, digital data collection, process improvement, and business process management can address the persistent — and costly — problem of slow turnaround times. I also discussed how artificial intelligence (AI) and machine learning (ML) could help.

Some insurers have begun to wake up from their recurring dreams. Global Growth Insights says 48 percent of insurers are using RPA for claims verification and premium renewals alone. And Cloud Insurance estimates that 68 percent of insurers are now using cloud technologies.

But none of those advances can match the impact of generative AI. While the bedrock principles of underwriting remain, generative AI has even greater potential for making bread-and-butter tasks such as case intake, risk analysis, premium setting, and payout determinations faster and more accurate.

At the same time, generative AI has introduced new methods that can take insurance underwriting to the next level — and it has added a lot of new terminology and concepts underwriters must master.

In this update to my 2022 blog post, I’ve woven generative AI updates into my original recommendations and expanded my section on traditional AI and ML to include new ways of meeting and exceeding your customers’ 21st-century expectations.

As you’ll see, these exciting new tools make it easier to cut into the 45 to 60 days typically required for underwriting while driving higher customer and agent satisfaction scores.

3 Ways Life Insurers Can Use AI for Underwriting Process Improvement

Let’s look at three newer tools and ideas for improving life insurance underwriting.

1. Collect Life Insurance Underwriting Information Easier and Faster

When insurers decide whether to issue a life, disability, or health policy for a potential client, they need data to make the best determination. We commonly refer to this information as pending requirements.

Some of the information comes from the applicant, and some of it comes from outside sources like healthcare providers. The type of information and level of detail needed can vary significantly from application to application.

Some factors life insurers use to decide what information they need to collect include:

• How much and what type of insurance the applicant wants
• The applicant’s age and sex
• How the applicant answers key questions
• Answers from service providers

The leading business problem that insurance companies face is the time required to collect this data. As the days drag on, industry research shows the “not taken rate” — the percentage of applicants who refuse to take a policy they applied for — begins to skyrocket.

This means the longer it takes to gather this information, the more likely your company and agent will waste their efforts while you incur expenses collecting the information you need to make the decision.

Historically, technology improvements have helped speed up life insurance underwriting, improved customer satisfaction and the policy-taken rate, and decreased insurers’ costs. Snail mail and phone calls gave way to email and enterprise application integration (EAI), which automated much of the information gathering, but didn’t help much with the process.

More successful companies dealt with their legacy technologies, heightened their focus on customer experience, and used data to identify kinks in processes and document the value of modernization. Now, large language models (LLMs) are giving more companies access to solutions that can supercharge all these processes.

For example, conversational AI interfaces can guide applicants through the process in real time, reducing errors and incomplete submissions. Using natural language processing (NLP), LLMs can convert handwritten documents (even from doctors and pharmacists!) into usable, AI-ready data. Generative AI can even predict when you need to gather additional data, making it easier to collect everything up front.

2. Improve Processes With Business Process Management Tools

Business process management (BPM) tools are not new. The Workflow Management Coalition (WfMC), with original members including IBM, Hewlett-Packard, Fujitsu, ICL, and Staffware, formed in 1993 as a consortium of software companies to define standards for the interoperability of workflow management systems.

Before disbanding in 2019, the WfMC said the following: “BPM is a discipline involving any combination of modeling, automation, execution, control, measurement, and optimization of business activity flows in support of enterprise goals, spanning systems, employees, customers, and partners within and beyond the enterprise boundaries.”

The roots of BPM solutions date back to the 1980s, with companies like FileNet and their system to route scanned documents. In the 2000s, the rise of EAI systems helped automate the communications between applications, but they often lacked process workflow maturity.

As BPM solutions evolved in the 2000s, developers added new functions to support process modeling, reporting and analytics. BPM solutions began to use a central rules engine maintained by business users who could visually create and manage process flows and decision points. That advance provided a central hub for handling complex underwriting processes and managing workflows to ensure agents handle their tasks. AI capabilities enabled dynamic, responsive workflows rather than static process maps.

Additionally, BPM solutions began using tools such as application programming interfaces (APIs), RPA, low-code applications, and ML/AI models to automate integrations with internal and external applications. For example, RPA helped carriers with legacy environments where agents can only access data manually.

However, today’s generative AI-infused RPA goes beyond simple rule-based automation. Intelligent bots can access legacy systems and perform complex manual tasks without APIs, and smart orchestration layers provide centralized control over data and workflows without replacing whole systems.

Meanwhile, advanced journey analytics now provide real-time insights into where and why customers drop off during underwriting, enabling carriers to make immediate adjustments to improve conversion rates.

These suites’ reporting and analytics allow carriers to monitor the underwriting process to better understand efficiencies, inefficiencies, and throughput. Modern BPM solutions provide a robust collection of tools to help make the underwriting process more efficient and profitable for carriers today. This data provides insights into the process and highlights potential areas that may impact the customer experience.

3. Speed Up Underwriting by Using AI, Generative AI, and ML

To reiterate, all these advances aim to accelerate the underwriting process. In traditional AI/ML solutions, AI and ML would work together to sift through large amounts of data, predict what information was needed next, and generate intelligent recommendations.

However, these solutions had limitations. For example, while AI and ML previously required separate models for each task, generative AI can now do many tasks, using various types of data (such as handwritten notes, images and audio), within a single model. Generative AI’s human-friendly capabilities, such as NLP, conversational APIs, and intuitive prompts, also make generative AI models easier to work with for more employees.

More significantly, previous ML models were not good at complex reasoning or understanding nuanced relationships between data. Generative AI and LLMs, on the other hand, demonstrate more sophisticated relationships between data and what they mean.

For example, traditional AI and ML might be able to differentiate between “complex” and “standard” cases, as you have defined those terms, and route them to the appropriate underwriting teams. But what if new risk categories arise, such as a study about the relationship between alcohol consumption and cancer?

With traditional ML, you would have to analyze the new study, program relevant portions into the model, and ensure that it still yielded accurate risk analyses. If the potential client stated that he “enjoys two or three drinks a week” before and that was considered “standard,” a generative AI model may recognize that information and move his case to the “complex” category, provide a recommendation, or even make the adjustment itself.

While this example does not rule out the need for human oversight, it does show how generative AI and ML can work together to provide faster and more accurate information.

Using a Generative AI-Powered Digital Journey for an Improved Customer Experience

Despite introducing new tools and processes, we will continue to see lengthy underwriting processes for more complex or high-value applications, especially as insurers and their customers continue to face the financial and human impacts of unforeseen natural disasters.

As a result, communication between applicants and agents is increasingly important. A digital customer journey is a path for keeping these lines of communication open and flowing, from the first time a customer encounters a product through product purchase and, ultimately, filing a claim.

With life insurance, especially, this journey is often very long, making it important for carriers to provide efficient, easy-to-access, and accurate touchpoints. A digital journey provides multiple channel options (for example, phone apps, web, phone, email, text, and so on) with modern graphical interfaces and smooth, easy access — and generative AI makes all these tools more powerful.

That’s because generative AI now takes digital customer journeys beyond static mapping. Modern systems can adapt in real time based on customer behavior, risk level, and channel preferences to create truly personalized experiences.

More dynamic mapping helps the industry adapt to the current evolution in customer demographics as younger, more technologically skilled consumers start families and enter the life insurance market.

These consumers have different demands for more tailored projects, responsive and proactive interactions, and accessibility from more devices. The behavioral insights that generative AI delivers can inform agent outreach strategies and automatically trigger appropriate communications based on where customers are in their journey.

What’s Next for Life Insurance Underwriting

Life insurers are analyzing the causes of underwriting inefficiency and looking to stop it. Despite its traditional resistance to change, the insurance industry is starting to see value in modernizing the underwriting process, especially with generative AI tools.

Successful insurers will blend these intelligent AI tools with foundational insurance principles and human insights to create a modern, responsive underwriting experience.

One challenge is the need to focus on the ethical dimensions of generative AI. This requires greater focus on governance, risk management, and human-centered design that protects customer data, ensures regulatory compliance, and augments — rather than replaces — human underwriters.

However, the tools for transformation are here, and the change is already underway. Insurance companies must focus on these areas to accelerate the underwriting process, keep up with evolving customer expectations, and stay ahead in this competitive industry. Generative AI and other modern technologies aren’t simply automating old problems — they’re reimagining how carriers approach risk, speed and service delivery in the modern era.

Are you ready to explore how artificial intelligence can fit into your business but aren’t sure where to start? Our AI experts can guide you through the entire process, from planning to implementation. Talk to an expert