How generative AI delivers value to insurance companies and their customers – IBM Blog

Insurers struggle to manage profitability while trying to grow their businesses and retain clients. They must comply with an increasing regulatory burden, and they compete with a broad range of financial services companies that offer investment products that have potential for better returns than traditional life insurance and annuity products. Although interest rates have increased at an unprecedented rate over the past year as central banks attempt to curb inflation, a significant part of insurers’ reserves are locked into low-yield investments, and their investment yields won’t improve for several years (as their portfolios turn over).

Large, well-established insurance companies have a reputation of being very conservative in their decision making, and they have been slow to adopt new technologies. They would rather be “fast followers” than leaders, even when presented with a compelling business case. This fear of the unknown can result in failed projects that negatively impact customer service and lead to losses.

IBM’s work with insurance clients, along with studies by IBM’s Institute of Business Value (IBV), show that insurer management decisions are driven by digital orchestration, core productivity and the need for flexible infrastructure. To align with key imperatives and transform their companies, insurers need to provide digital offerings to their customers, become more efficient, use data more intelligently, address cyber security concerns and have a resilient and stable offering.

To achieve these objectives, most insurance companies have focused on digital transformation, as well as IT core modernization enabled by hybrid cloud and multi-cloud infrastructure and platforms. This approach can accelerate speed to market by providing enhanced capabilities for the development of innovative products and services to help grow the business, and it can also improve the overall customer experience.

Role of generative AI in digital transformation and core modernization 

Whether used in routine IT infrastructure operations, customer-facing interactions, or back-office risk analysis, underwriting and claims processing, traditional AI and generative AI are key to core modernization and digital transformation initiatives.

Core modernization with AI

Most major insurance companies have determined that their mid- to long-term strategy is to migrate as much of their application portfolio as possible to the cloud.

When use of cloud is combined with generative AI and traditional AI capabilities, these technologies can have an enormous impact on business. The initial use of generative AI is often for making DevOps more productive. AIOps integrates multiple separate manual IT operations tools into a single, intelligent and automated IT operations platform. This enables IT operations and DevOps teams to respond more quickly (even proactively) to slowdowns and outages, thereby improving efficiency and productivity in operations.

A hybrid multicloud approach combined with best-in-class security and compliance control features (such as controls IBM Cloud® is enabling for regulated industries) offers a compelling value proposition to large insurers in all geographies. Several prominent companies in every geography are working with IBM on their core modernization journey.

Digital transformation with AI

Insurance companies are reducing cost and providing better customer experience by using automation, digitizing the business and encouraging customers to use self-service channels. With the advent of AI, companies are now implementing cognitive process automation that enables options for customer and agent self-service and assists in automating many other functions, such as IT help desk and employee HR capabilities.

The introduction of ChatGPT capabilities has generated a lot of interest in generative AI foundation models. Foundation models are pre-trained on unlabeled datasets and leverage self-supervised learning using neural networks. Foundation models are becoming an essential ingredient of new AI-based workflows, and IBM Watson® products have been using foundation models since 2020. IBM’s watsonx.ai™ foundation model library contains both IBM-built foundation models, as well as several open-source large language models (LLMs) from Hugging Face.

The supervised learning that is used to train AI requires a lot of human effort. It is difficult, requires intensive labeling and takes months of effort. On the other hand, self-supervised learning is computer powered, requires little labeling, and is quick, automated and efficient. IBM’s experience with foundation models indicates that there is between 10x and 100x decrease in labeling requirements and a 6x decrease in training time (versus the use of traditional AI training methods).

To achieve digital transformation with AI, insurance companies need to get a good understanding of structured and unstructured data, organize it, manage it in a secure manner (while complying with industry regulations) and enable instant access to the “right” data. This capability is fundamental to providing superior customer experience, attracting new customers, retaining existing customers and getting the deep insights that can lead to new innovative products. It also helps improve underwriting decisions, reduce fraud and control costs. Leading insurers in all geographies are implementing IBM’s data architectures and automation software on cloud.

Generative AI capabilities that enable today’s digital transformation can be placed in five domains:

1. Summarization: Transform text in large documents, voice conversations and recordings with domain-specific content into personalized overviews that capture key points (such as insurance contracts, policy and coverage documents, and responses on customer FAQs).
2. Classification: Read and classify written input with as few as zero examples (such as classifying claims requests, sorting customer complaints, analyzing customer sentiment, classifying risk during insurance underwriting and analyzing customer segmentation for insurance product development).
3. Generation: Generate text content for a specific purpose (for example, marketing campaigns with a focus on specific insurance products, blog posts and articles for various insurance-related topics, personalized customer email drafting support and code generation for use by insurance technology systems).
4. Extraction: Analyze and extract essential information from unstructured text (such as extracting information from insurance agent-filed reports, extracting medical diagnosis from physician or clinical reports for use in insurance underwriting and evaluating risk).
5. Question-answering: Create a question-answering feature grounded on specific data (for example, build policy and coverage-specific Q&A resource for customer service agents).

As insurance companies start using generative AI for digital transformation of their insurance business processes, there are many opportunities to unlock value.

IBM’s work with clients shows significant productivity gains when using generative AI, including improving HR processes to streamline tasks such as talent acquisition and managing employee performance; making customer care agents more productive by enabling them to focus on higher value interactions with customers (while digital channel virtual assistants using generative AI handle simpler inquiries); and saving time and effort in modernizing legacy code by using generative AI to help with code refactoring and conversion.