By Ayo Mseka
The possibilities appear endless when it comes to the many uses of generative artificial intelligence in the insurance industry—from enhancing productivity to reducing fraud to improving customer service. We asked an expert what generative AI tools and solutions P&C insurers are implementing today, and what real-world impacts they are having.
P&C insurers are strategically deploying generative AI tools to transform their operations, according to Raghav Maheshwari, senior client partner in the global P&C insurance business at EXL, a global analytics and digital solutions company serving a range of industries. Key areas they are focusing on include:
- Agent Assist: AI-powered agent-assist tools revolutionize customer interactions by providing real-time, context-aware guidance to representatives. These tools offer procedural recommendations based on customer intent, sentiment, and historical data, enhancing agent performance, and driving exceptional customer experiences.
- Underwriting Assist: This tool enables better risk selection by uncovering insights within complex submissions and portfolios. “It moves beyond data extraction to summarize, analyze, and interpret documents. Large language models, fine-tuned on insurance data and integrated with business logic, aid underwriters in making faster, more informed decisions,” he said.
- Claims Assist: Generative AI streamlines claims handling with a claims co-pilot tool that boosts adjuster productivity. “Key features include claim information gathering, notes summarization, medical document review, invoice analysis, and open Q&A for deeper insights. This reduces overall claims cycle time and improves efficiency.” Maheshwari said.
“Generative AI empowers P&C insurers to achieve unprecedented levels of efficiency, insight, and customer-centricity. As these technologies evolve, we anticipate a future where AI drives continuous innovation, proactively mitigates risks, and delivers truly frictionless insurance experiences,” Maheshwari added.
Implementation challenges
P&C insurers are discovering that putting generative AI into practice is far from effortless, Maheshwari pointed out. There's significant behind-the-scenes work involved, including data management, workflow orchestration, domain contextualization, AI Ops expertise, and more. Selecting the right large language model (LLM) is only a fraction of the effort, he said. “True success lies in the less glamorous but essential bookends of implementation.
Major challenges include:
- Companies are understandably cautious about the data security and regulatory implications of generative AI. Data privacy breaches, the potential for hallucinated outputs with associated liability, and copyright issues surrounding pre-trained models are significant concerns. “This emphasizes the need for responsible AI practices and carefully implemented guardrails,” he said.
- Generative AI models rely on large, high-quality datasets. Inconsistent or incomplete data, along with inherent biases that might exist in the data, can lead to inaccurate predictions, and perpetuate unfair outcomes. “Insurers must invest in data cleansing, enrichment, and bias-mitigation strategies,” he said.
- Integrating AI solutions seamlessly with existing legacy systems can be complex. Effective change management and IT collaboration are crucial for scalable deployment, Maheshwari pointed out.
- Some generative AI models function as "black boxes," making it hard to explain how they arrive at decisions. This can hinder trust from both regulators and end-users. Insurers must prioritize models with explainability features or develop methods to interpret model outputs.
- Successful implementation often requires cultural and process shifts within insurance organizations. “Building data literacy, training staff on new tools, and promoting trust in AI outputs are key change-management considerations,” he said.
Areas with the most traction
Maheshwari said that while all the generative AI tools mentioned so far hold vast potential, certain categories have gained particularly strong traction in the P&C insurance industry. Here are some of these categories:
Agent Assist and Conversational AI: These tools directly address a major focus area for insurers: customer experience. By empowering agents with real-time guidance, and offering always-on, intelligent self-service through chatbots and procedural guidance, insurers drastically improve responsiveness and customer satisfaction, he explained.
Assist Solutions
- Claims Assist: The traditionally cumbersome, domain-heavy claims process is ripe for disruption with generative AI. These tools offer summaries, next-best-actions, and procedural guidance to drastically reduce adjudication time. “Some insurers are already seeing the benefits of this solution in production environments,” Maheshwari said.
- Underwriting Assist: Augmenting underwriters' decision-making processes with AI-derived insights from complex data sets offers a competitive edge. Faster, more accurate risk evaluation translates into improved pricing and reduced losses.
Benefits of generative AI
Generative AI tools are proving their value in real-world P&C insurance deployments, Maheshwari said, and he shared a few of the benefits observed by practitioners across key areas:
Claims Assist:
- Productivity boost: A 60% improvement in agents’ claim handling average handling time (AHT) translates into faster claims processing and increased capacity to manage caseloads, he said.
- Enhanced customer experience: More accurate, less repetitive interactions improve customer satisfaction while reducing frustration.
- Quality assurance: A 100% audit coverage ensures adherence to guidelines, minimizes errors, and improves overall claims handling consistency, Maheshwari said.
Agent Assist:
- There is a 30% decrease in cost to serve, which demonstrates significant operational efficiency gains, freeing up resources and reducing expenses.
- Real-time guidance and procedural support empower agents to deliver superior service, leading to higher satisfaction scores.
- By ensuring that all interactions adhere to standards, insurers reduce the risk of non-compliance and associated penalties, Maheshwari said.
Underwriting Assist:
- Intelligent lead prioritization enables underwriters to focus on the most promising opportunities, maximizing potential conversion rates.
- AI-powered insights lead to more accurate risk assessments, minimizing potential losses and improving the overall health of the insurance portfolio.
- A 3x-4x return on investment demonstrates the financial power of AI-enhanced underwriting, justifying technology adoption and offering significant long-term profitability gains.“These are representative examples from production deployments,” Maheshwari pointed out. “The specific benefits realized by individual insurers will depend on various factors, including the scale of implementation, the complexity of their existing processes, and the maturity of their data.”
Advantages to clients
The implementation of generative AI tools for the P&C insurance industry is driving positive transformations both for insurers and their clients, Maheshwari said. Among the benefits to clients:
- AI-powered automation reduces wait times across service requests, claim submissions, and policy inquiries, leading to faster resolutions and greater convenience.
- Clients receive tailored recommendations, relevant policy information, and proactive insights based on their individual needs and preferences.
- Chatbots and virtual assistants empower clients to seek answers, file claims, and check the status of requests on their own terms, improving accessibility and flexibility.
Impact of AI on P&C insurers
Among the benefits to insurers:
- Improved efficiency, reduced operational costs, and better risk selection contribute to increased bottom-line profitability.
- The ability to handle increased service volume and more complex requests positions insurers for growth without proportional increases in human resources. “Generative AI solutions also open doors to reaching out untapped markets,” Maheshwari said.
- Generative AI encourages a shift towards data-informed insights across underwriting, pricing and product development, leading to more strategic business decisions.
“These impacts mark the early stages of AI-driven change within the insurance industry,” Maheshwari added. “As generative AI models evolve, both clients and insurers will benefit from increasingly seamless, personalized, and intelligent interactions, reshaping the entire insurance value chain.”
Ayo Mseka has more than 30 years of experience reporting on the financial services industry. She formerly served as editor-in-chief of NAIFA’s Advisor Today magazine. Contact her at [emailprotected].
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