Get Better Benefits Insights When You Combine Analytics and Generative AI

Most benefits carriers’ data might as well be gathering (digital) dust. 

The advanced analytics tools needed to turn the data into actionable, meaningful strategy just aren’t implemented in most of today’s insurance companies. 

Fortunately, AI and the machine learning technology known as “generative AI” offers an incredible solution.

Used in conjunction with generative AI, data analytics enables efficient, effective approaches to numerous benefits insurance operations — from customer service to fraud detection.

The parallel development of AI and analytics

AI’s core concepts are about as old as early machine-facilitated data analysis, owing their origin to Alan Turing’s pivotal 1950s research. Moreover, AI’s progression paralleled analytics’ development in numerous ways: Not long after Frank Rosenblatt developed the first rudimentary neural network in 1957, critical algorithms for both modern analytics and ML emerged in the 1960s, such as decision trees and k-nearest neighbors.

AI research and development saw peaks and valleys, as did analytics, but their parallel-track development continued: For example, neural network development stalled in the 1970s but was resurgent throughout the next decade — and in 1989, a future Gartner analyst coined the term “business intelligence” to characterize data analysis’s tangible value as a corporate asset. 

That term (like “analytics”) wouldn’t truly become a buzzword until the 2000s and 2010s, which roughly matches when businesses in various industries, including insurance, started exploring AI and ML. These days, data analytics and AI are often symbiotic.

From descriptive to generative

Gartner defines four levels of analytics: prescriptive (what), diagnostic (why), predictive (what happens next), and prescriptive (what could we do next). 

These tiers also align with AI’s evolution, but the technology can leverage data on two additional levels. A cognitive AI solution monitors a process (e.g., a pending claim) and responds autonomously or semi-autonomously (with approval or rejection), informed by ultra-fast analysis of relevant historical data. Next comes generative AI, where models use natural language processing (NLP) and a vast swath of training data to generate critical insights in clear language.

4 Group carrier use cases for analytics and generative AI

While not a complete list, the following are areas where data analytics and AI can (and, for some carriers, are) improving key processes:

1. Chatbots

Analytics-derived insights into insurance end users’ medical history, past claims, and more ensure chatbots have full, accurate employee profiles. Meanwhile, NLP ensures the chatbot’s responses provide a reassuring experience, and while more than two-thirds of inquiries are resolved entirely by a chatbot, the AI can instantly escalate to the human call center if needed.

2. Claims management

The combination of analytics and generative AI makes claims submission more intuitive for employee end-users and processing more efficient for carriers. Eligibility and any applicable payouts are calculated and initiated near-instantaneously, and if the AI-driven claims interface is directly integrated with absence management, it can trigger related processes, like short-term disability and leave types.

3. Fraud detection

Generative AI coupled with analytics swiftly identifies anomalies in new claims based on instant comparisons to claims history. Obvious malfeasance can be stopped in its tracks — or, if there’s a question of mistake versus fraud, the AI pauses processing so human adjusters and analysts can peruse the claim.

4. Demand forecasting

The ability to analyze employee participation in open enrollment and project interest in new voluntary benefits ensures carriers construct ideal group plans. Meanwhile, generative AI can simplify enrollment for employees and provide personalized recommendations for voluntary add-ons.

Avoiding common AI adoption challenges

To circumvent challenges other industries sometimes face with large-scale AI adoption, group insurers can:

  • Discourage outdated attitudes regarding analytics and AI. At the same time, ensure these tools always support and optimize human operations (among customers and within carrier operations).
  • Don’t let any existing departmental data silos stay isolated. Break them down with effective data integration, which both analytics and AI tools can support. This will help your organization’s various segments become better aligned and more efficient.
  • Don’t rush implementation: Roll out AI-driven advanced analytics initiatives in phases; e.g., pilot them in claims before moving on to billing and other business units. Scale up gradually as successful usage improves.

Embrace ecosystems for easier access to advanced AI and analytics solutions

Using the ecosystem approach allows insurers to find insurtechs and other innovative vendors offering the most cutting-edge data, analytics, and AI solutions. As these technologies improve, they’ll do even more to help all segments of carrier operations run smoothly and in harmony.

Time is running out for legacy approaches to group insurance. EIS Group and PwC experts tackle this in our webinar: What Makes the Insurer of the Future? We examine how recent disruptions to traditional models have changed benefits insurance for good, and how the ecosystem model is critical for group carriers to move forward and thrive.

Watch now: What Makes the Insurer of the Future?


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