Advancements in artificial intelligence have exploded over the past year with the proliferation of generative AI (GenAI). Comparative to technological breakthroughs like the Internet and smart phone, GenAI is the most remarkable technology we have seen in decades. And as noted by Accenture1, it is a catalyst for reinvention, having the power to recreate every facet of an organization.
Estimates show GenAI’s market potential could reach $15 billion by 2025 solely in the insurance and finance industries.2 Per Statista, the generative AI market is poised for significant growth between 2023 and 2030. Approaching $45 billion at the end of 2023, the global GenAI market nearly doubled its size from 2022. This yearly expansion of nearly $20 billion is forecasted to continue through the decade.3 In the U.S., the generative AI market is expected to exceed $60 billion by 2030, quadrupling from its 2023 size of $16 billion.4
Applications for GenAI are emerging across the insurance industry, primarily in underwriting, customer service, claims, and IT, with widespread usage in document interrogation and summarization, modern code generation, and virtual assistance.
Per a December 2023 ReSource Pro report5, 60% of personal lines insurers are already experiencing cost reduction benefits in the area of fraud monitoring and detection from the use of GenAI. Additionally, the technology can play a substantial role in in the automation of many other business aspects, such as streamlining the creation of policy documents and supporting the development of underwriting and claims correspondence. Many personal lines carriers reported being already actively involved in some stage of GenAI transformation activity, with 67% in the planning or piloting phases.
ReSource Pro’s research6 of commercial lines insurers found that GenAI has elevated the role of all AI technologies in the commercial lines space due to its broad availability powered by large language models (LLM). By summarizing and analyzing quote submissions consisting of both unstructured and structured data, GenAI can swiftly provide underwriting insight. Commercial lines carriers reported GenAI transformation activity at higher levels than that of personal lines carriers with 72% already involved in either the planning /or piloting phases and 6% actually in production.
In an interview with Insurance Thought Leadership (ITL), John Sviokla, cofounder of GAI Insights, discussed the power and potential of GenAI’s capability in managing unstructured data. He explained how it can gather information from various formats and sources and then summarize it to seamlessly integrate into the workflows of agents, underwriters and claims professionals. "We’ve had 120 years of automation of the physical world, 67 years of automation on structured data and 18 months of automation of unstructured data. There’s lots of opportunity."7
With roughly 5 million customers, Jerri Insurance is an app-based, direct-to-consumer insurance brokerage that utilizes artificial intelligence to match users with insurance policies. According to Sviokla, the broker has been using GenAI for the past year to service customers with chatbots and text messaging. The results are impressive. Customer response rates have increased from just over 50% within a day to 100%, with most customer inquiries responded to within 30 seconds. They have reduced human intervention from 100% of issues down to only 11%. “They’re way more scalable. They can grow this business without having to grow the customer service function. ROI is about $4million a year.” 7
Insurtech Clearcover has launched a series of AI-driven solutions, including a Digital Statement Collection tool that utilizes LLMs to facilitate a conversational experience immediately following First Notice of Loss (FNOL). The claims intake tool gathers necessary incident information that is usually collected during adjuster/claimant follow-up calls. This results in reduced adjuster burden, a streamlined claims process, and expedited settlement. Clearcover’s Chief Product and Innovation Officer, Adam Fischer said, "By leveraging the power of generative AI, we're ushering in a transformational way for our customers to share information and self-serve throughout their insurance journeys."8
In a recent Majesco panel discussion around what’s next with GenAI, Bryan Falchuk, Managing Partner at Insurance Evolution Partners, pointed out that the insurance industry cannot sit back and take a wait-and-see approach before investing in the generative AI world. It’s just moving too fast. He advises insurers to experiment and find areas where they can get comfortable using the technology, starting with those that bolster employees’ expertise. “This approach supports the fact that we have huge retirement rates in this industry, as well people very separated with remote work, and the result is that we aren’t receiving adequate knowledge transfer. These tools will be super valuable.”9
Unlike other technologies, GenAI can profoundly influence every aspect of an organization. Harnessing its potential demands insurers fully embrace reinvention: rethinking talent, processes, and workflows – while managing the technology through a new capability being referred to as ‘responsible AI’.1 The insurance industry must transcend fragmented, siloed point solutions and incremental approaches. Instead, insurers should apply insights gained from previous digital transformation endeavors and rethink their business models, focusing on four key areas: the overarching AI strategy; digital, cloud-based infrastructure; investment allocation and application; work redesign and workforce reinvention.
Shift the focus from siloed use cases to those that prioritize business capabilities across the entire value chain, guided by objective assessment of business case, organizational readiness, and return on investment. Only pursue GenAI use cases with a clear strategic direction. Developing a comprehensive, enterprise-wide AI and automation strategy that incorporates GenAI ensures investments align with strategic priorities and evolving customer expectations.
Invest in modern, cloud-based infrastructure that can support and scale GenAI to ensure its full potential can be actualized. As Accenture reports: “GenAI requires a fundamentally different enterprise architecture in which data is more fluid, and unstructured and synthetic data become much more important.” Understand the new capability requirements and what it will take to build them. Since higher demands are placed on infrastructure, IT operating models will need to change.1
Concentrate GenAI resources in targeted capability domains. Per PwC, 50% to 70% of all insurer activities characterized by high volume and low variability can be described as ‘non-differentiating capabilities’ – table stakes and lights-on capabilities. Their research shows that GenAI will drive the most value in non-differentiating areas such as policy generation, claims, and customer service. Using GenAI in these business domains allows insurers the ability to experiment and learn without assuming undue risk.10
Redesign work and prepare workers for the GenAI world where as John Sviokla explains, “They’re conversing with a different sort of conversation partner, one with a hive intelligence and encyclopedic knowledge. And the conversation is codified into the knowledge base…This is much more about shared discovery, because as the individual is doing the work, their job is changing.” Organizational change management will become increasingly critical.7
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