Gen AI: The Secret Weapon Insurers Need to Win in 2024

Introduction

The world of AI continues to evolve rapidly, and gen AI has sparked universal interest. This is certainly the case for the insurance industry, where generative AI is fundamentally reshaping everything from underwriting and risk assessment to claims processing and customer service. The transformative power of this technology holds enormous potential for companies seeking to lead innovation in insurance CRM. Amid an ever-evolving competitive landscape, staying ahead of the curve is essential to meet customer expectations and navigate emerging challenges.

The Role of Gen AI in Insurance

Generative AI, as its name suggests, generates content. Because its algorithms are designed to enable learning from data input, generative AI can produce original content, such as images, text, and even music, that is sometimes indistinguishable from content created by people. Models such as GPT-3.5 and GPT-4 present opportunities to radically improve insurance CRM operations. They have the potential to automate processes, enhance customer experiences, and streamline claims management, ultimately driving efficiency and effectiveness across the industry.

As the number of companies exploring gen AI multiplies, so will industry use cases. Recently, four patterns have started to emerge among insurers:

1. Summarize Policies and Documents: Generative AI can summarize policies, documents, and other unstructured forms of content, making information more accessible and digestible for both insurers and customers.

2. Synthesize Summarizations: By synthesizing summarizations, generative AI can create new content, enabling insurers to generate personalized policy recommendations and other relevant materials for customers.

3. Answer Questions: Leveraging what it has learned from summarizing and synthesizing, generative AI can answer questions, providing quick and accurate responses to inquiries from customers, agents, and other stakeholders.

4. Language Translation: Generative AI can translate between natural languages and even computer code, facilitating communication and collaboration across linguistic barriers.

Three Primary Objectives

Insurers are seeking to leverage these expanded patterns to address three main objectives:

1. Improve Experiences for Customers and Agents: By deploying generative AI virtual assistants or agents, insurance companies can reinvent their approach to providing customer service and creating new products. Generative AI makes it easier to deliver individualized and empathetic interactions, while also streamlining processes for insurance agents.

2. Heighten Productivity and Efficiency: Generative AI can be deployed alongside insurance industry knowledge workers to enhance productivity and efficiency. By summarizing and synthesizing large volumes of content, such as call transcripts and legal paperwork, generative AI can dramatically reduce processing times and improve overall operational efficiency.

3. Manage Compliance and Mitigate Risks: In a highly regulated industry like insurance, compliance and risk management are paramount. Generative AI can automate compliance monitoring, detect fraud, and generate training materials to ensure that staff remain current on the latest regulations.

Three Steps to Help Insurers Get Started

To drive better business outcomes, insurers must effectively integrate gen AI into their existing technology infrastructure and processes. Gen AI is a tool within a broader set of techniques and technologies. Accordingly, insurers should improve existing processes and optimize them in parallel to achieve the maximum benefits of generative AI. The big win often involves combining multiple AI technologies to address different aspects of a project, such as semantic searching or language capabilities.

For insurers who are not yet using gen AI, these three initial steps are recommended:

1. Establish a Multidisciplinary Team: Form a team comprising businesspeople, IT specialists, and data scientists to embark on the generative AI journey. This team should focus on adapting generative AI solutions to the unique requirements of the organization.

2. Identify the Operating Model: Determine the operating model that best fits the organization, ensuring that the technology can be experimented with safely, successfully, and at scale.

3. Develop Expertise and Capabilities: Begin with low-barrier use cases and gradually fine-tune models based on domain knowledge and data sources. Building expertise in generative AI will be crucial for long-term success in leveraging this technology effectively.

Risks and Human Oversight

While gen AI is valuable for identifying risks that humans overlook, the technology itself carries associated risks. These involve elements such as intellectual property, corporate-level reputation and bias, and information security. To mitigate such risks, insurers must embrace accountability and have control procedures and compliance frameworks in place. Responsible AI methods that include human oversight are essential for ensuring ethical and nondiscriminatory generative AI models.

Conclusion

Generative AI has the power to transform the insurance sector by increasing operational effectiveness, opening up new innovation opportunities, and deepening customer relationships. But it is essential that insurers proceed mindfully, with the right guardrails in place to manage the risks associated with the technology. Firms that adopt responsible AI practices and avail themselves of leading industry insights will be well positioned to seize opportunities as they arise amid the evolving AI landscape. As we move forward into 2024 and beyond, gen AI will undoubtedly emerge as a secret weapon for insurers looking to gain a competitive edge and drive lasting success in the digital age.