Building Artificial Intelligence Models into the Insurance Value Chain
How can insurance companies take advantage of emerging AI technologies?
The recently concluded InsureTech Connect (ITC) conference showcased numerous insurance solutions providers with a wide variety of value propositions aimed at making the age-old insurance business faster, better, and cheaper to operate. Artificial intelligence (AI), including new iterations like Generative AI (GenAI) and Large Language Models (LLMs), featured prominently in many of these value propositions.
Insurance companies are actively embracing AI technologies to enhance their business, operations, and systems. This was evident during the LIMRA annual conference in October, where AI dominated discussions among insurance executives. In contrast, during the 2022 annual conference, AI was hardly mentioned. While AI has been around for some time, the recent surge in interest can be attributed to the emergence of GenAI and LLMs, and in particular the popularity of ChatGPT, which is built on a large language model.
Insurance is a highly regulated sector that relies heavily on data. Any usage of AI models in the insurance industry is subject to audits and compliance requirements. Furthermore, the evolution of AI models is dependent on their training, specifically their machine learning (ML) capabilities, which in turn rely on the dataset used for training. Considering these constraints, it becomes crucial for insurance companies to prioritize data and begin by formulating data strategies tailored to their specific needs.
To maximize the benefits of AI, it is recommended that insurers recognize the pivotal role data plays and take the first step by developing company-specific strategies. ITC showcased numerous tools and solutions built on AI technologies, offering insurers the opportunity to enhance their value chains. By organizing data effectively, ensuring its quality, implementing governance measures, and operating securely with sound management principles, insurance companies can successfully leverage the power of AI technologies.
Cloud computing also plays a significant role in this context, as it offers a flexible and scalable framework for executing data strategies and evolving AI/ML models. The accompanying figure provides a high-level overview of building AI models leading to its utilization in insurance use cases.
Cloud providers have created foundational models for AI training. These models provide a comprehensive set of tools and services that enable organizations to harness the power of AI without the need for significant upfront investments in infrastructure and expertise. This approach allows companies to concentrate on developing and deploying AI models to drive innovation and achieve their business objectives. Typical components included in a foundational model are infrastructure, storage, data management, and pre-built AI frameworks and libraries.
By prioritizing data strategies, followed by execution in a cloud environment, and then training AI models, insurance companies can achieve sustained benefits across the insurance value chain. AI models can be trained for use cases in product development, underwriting, predictive analytics, claims, fraud detection, customer and user experience (CX/UX), operational efficiency, risk management, and more.
Celent offers a wealth of materials and best practices to assist insurance companies in navigating the realms of data, cloud, and AI. These valuable resources can be accessed at celent.com.