Demystifying GenAI: Transforming the Insurance Landscape

In today’s ever-evolving technological landscape, the emergence of generative AI (GenAI) stands as a pivotal moment. Demystifying GenAI is a complex task as some view it as a mere trend, while others recognise it as a revolutionary force.

In the history of AI, we now have two distinct eras: pre-GPT and post-GPT.

We now live in the post-GPT era of AI. In just six months since ChatGPT’s release, AI developments have dominated headlines. Companies like Anthropic and DeepMind have introduced new large language models (LLMs). Meta and Google have also revealed their own LLMs.

For the insurance industry, GenAI represents more than just an upgrade from traditional AI; it signifies a transformative shift that reaches every corner of the business.

GenAI vs. Traditional AI: A Paradigm Shift

In contrast to traditional AI, GenAI democratises access to artificial intelligence. It simplifies the interface to a degree where anyone, regardless of their programming expertise and data science skills, can harness its power. This shift is akin to the introduction of the iPhone and its app store, ushering in a new era of applications and services. While traditional AI focused on optimising existing models, GenAI reimagines the entire operational framework.

Leading Insurers: Embracing Transformation

Forward-thinking insurers are adopting two distinct approaches to leverage GenAI. The first involves focusing on game-changing applications like knowledge assistants and coding assistants, streamlining day-to-day operations significantly. These applications, still in their infancy, promise profound impacts as they evolve. The second approach entails rethinking entire verticals, such as automating the claims process from start to finish. This end-to-end transformation simplifies processes, enhances customer experiences, and drastically reduces costs. Now that these new base models and capabilities can be implemented in a safe and secure way, where customer and proprietary data is not shared outside of the company, the use cases and limit of the value creation possibilities have no bounds.

The Impact of GenAI: Beyond Cost Reductions

While cost savings are a significant driver, GenAI’s impact extends far beyond financial gains. By automating mundane tasks, employees can redirect their focus to more value-adding activities, especially in sales and distribution. GenAI also opens doors to hyper-personalised policies, improving customer satisfaction and retention rates. Tools like sentiment analysers empower agents, ensuring empathetic customer care and paving the way for increased cross-selling opportunities, as well as ensuring new and ever-changing regulatory and compliance changes are adhered to and reportable using the right kind of traceability and explainability.

Building GenAI-Powered Applications: A Fusion of Functions

Fundamentally, GenAI operates on four key building blocks: search, summary, content, and code. These elements, when combined strategically, form powerful applications. For instance, a knowledge assistant combines search and summary functions, providing a user-friendly experience by simplifying complex information.

While the search function looks up specific terms and conditions of insurance policies across sources, the summary function condenses this information into more concise or simpler language. The content function ranges from the generation of responses to customer inquiries to claims reports and policy explanations. The fourth building block, the code function, involves activities such as the translation of natural language to SQL queries or the documentation of code in natural language.

As a simple example of how these building blocks complement one another, you can think of the knowledge assistant as a combination of the search and summary functions. First, it retrieves relevant information from various sources before synthesising the information into simple terms, ready to share with customers. The knowledge assistant provides a user-friendly experience, delivering valuable insights and clarifying complex information.

Other GenAI use cases that we see our insurance customers exploring:

1. Data Augmentation: Generative AI creates synthetic data, enhancing predictive models and ensuring customer privacy by mimicking original data without personal information.
2. Content Creation: Retrieval Augmented Generative (RAG) Models on a base LLM model like Llama, GPT, etc. AI tools like ChatGPT speed up processes by generating:

• Policy Documents
• Tailored Marketing Materials
• Customer Communications
• Product Descriptions

1. Risk Assessment and Premium Calculation: Generative AI simulates risk scenarios, leveraging historical data to estimate future risks. This refines predictive models, aiding in accurate premium calculations.
2. Fraud Detection: Generative AI generates examples for training fraud detection models, identifying fraudulent claims promptly, and saving costs for insurance companies.
3. Customer Profiling: Generative AI develops synthetic customer profiles, enabling customer segmentation, behaviour prediction, and personalised marketing without privacy breaches.
4. Claims Processing: Generative AI streamlines claims management.

• Automating Responses for Basic Inquiries
• Evaluating Damage and Estimating Repair Costs
• Providing Efficient Customer Communication

Generative AI revolutionises insurance by optimising processes, enhancing accuracy, and ensuring seamless customer experiences.

Advice for Insurance Leaders: A Multilayered Approach

To harness the full potential of GenAI, insurance leaders must embrace a multi-layered operating model. This model should include a clear strategy, proactive planning for skill shifts, and robust partnerships. By setting up a cohesive approach, insurers can avoid disjointed applications, focus their resources effectively, and guide their organisations towards a transformative trajectory.

In the realm of insurance, GenAI isn’t merely a technological advancement; it’s a fundamental reshaping of how the industry operates. As insurers continue to explore and implement GenAI applications, the possibilities for efficiency, customer satisfaction, and innovation are boundless.

We at esynergy strongly believe that GenAI is only one part of the solution needed to solve a business problem. Enterprises need a robust platform that enables them to leverage GenAI in reliable and compliant solutions that can be scaled and adopted enterprise wide with the ability to ensure that data ethics and use are fully under your control.