Generative AI represents a monumental leap forward in Artificial Intelligence (AI). Its ability to engage in extended conversations, process complex text and image inputs, and provide reasoned and creative answers demonstrates human-level performance on professional and academic benchmarks. While Generative AI may not universally surpass human capabilities, it exemplifies the tremendous potential of AI in augmenting and enhancing our cognitive abilities.
The integration of AI in the insurance industry ushers in a new era of possibilities. Insurance companies can leverage Generative AI’s advanced capabilities to navigate economic uncertainty, effectively manage inflation, reduce operational costs and cater to evolving customer preferences for digital and self-service models. By harnessing the power of Generative AI, carriers can streamline their operations, transform customer engagement and explore cross-selling opportunities, all while delivering exceptional experiences tailored to individual policyholders.
Furthermore, industry trends indicate a growing focus on operational efficiencies and the adoption of AI in insurance technology investments. According to Gartner, 40 percent of insurance CIOs plan to increase their AI investments in 2023.1 These strategic investments aim to assist insurance professionals in accomplishing tasks more efficiently, freeing up time to focus on value creation.
Let's dive into some specific use cases for Generative AI in insurance.
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Customization and Product Mapping
Generative AI’s powerful contextualization and summarization capabilities can enable unparalleled personalization of insurance products. By capturing outliers and boundary conditions, such as incorporating outliers in mortality calculations for life insurance, Generative AI can offer an elevated level of contextual understanding.
It can access large and varied data sets in diverse fields, such as legal, medical, historical and demographic data, at speed and scale. Data is enriched with information from individual interactions, allowing the mapping of customers' lifestyles, risk profiles and behavioral patterns to help design tailored plans with personalized insurance quotes.
The Large Language Model (LLM) becomes more effective as more information is captured, products are tweaked and market gaps are identified (as they emerge), thus shrinking product development from months to weeks.
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Sales & Marketing
Integrating Generative AI with the sales platform can enable the extraction of high-quality leads and improve conversion ratios. Carriers can offer modular coverage, allowing customers to purchase separate insurance covers for different aspects, such as device, battery and screen for a mobile phone.
By identifying unique customer needs, the AI-powered recommendation engine can help design targeted solutions and align products with the right distribution channels.
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Underwriting
Generative AI can accelerate risk engineering processes – for instance, read images and charts – to help design more intelligent algorithms and provide richer insights for underwriting. Image analytics can enhance risk assessments for manufacturing / construction industries by analyzing heavy machinery, equipment positioning and worker risk exposure.
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Policy Servicing
Conversational AI improves policy servicing by providing quick and accurate information and recommendations. Interactive AI engages with customers, understands their requirements and intent, and offers clarifications and relevant suggestions. This technology benefits business-to-consumer products, such as personal lines, auto and home insurance.
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Claims Processing
According to the Federal Bureau of Investigation, the annual cost of insurance fraud is estimated to be more than USD 40 Billion.2 AI in insurance claims represents an opportunity to radically re-imagine outcomes. Generative AI integrated with fraud, medical and other systems can reduce turnaround time and help prevent fraudulent claims. It can simplify claims processing through natural language understanding and provide informed insights by analyzing multiple data sets quickly.
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Compliance and Reporting
Automated monitoring and alerts provided by Generative AI strengthen compliance and reporting. Insurers can stay up-to-date with regulatory changes and manage compliance complexities better, reducing risks associated with non-compliance.
Accelerating Growth with Generative AI
With Generative AI as an underlying technology, insurers can significantly elevate their decision-making capabilities. By embracing AI, these companies can streamline non-essential tasks, effectively harness vast amounts of data and ultimately provide customers with a distinctive experience. Gartner predicts that by 2027, chatbots and conversational AI will be the primary customer service channel for about 25 percent of organizations.3 In response to this trend, insurers are proactively integrating language models into their operations, enabling targeted functionalities that drive growth and foster a culture of innovation.
Contact us to know how WNS can help your business harness the power of Generative AI.
References:
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Gartner
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Federal Bureau of Investigation
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Gartner
About WNS Triange:
WNS Triange powers business growth and innovation for 200+ global companies with Artificial Intelligence (AI), Analytics, Data and Research. Driven by a specialized team of over 6000 analysts, data scientists and domain experts, WNS Triange helps translate data into actionable insights for impactful decision-making. Built on the pillars of consulting (Triange Consult), future-ready platforms (Triange Nxt), and domain and technology (Triange CoE), WNS Triange seamlessly blends strategy, industry-specific nuances, AI and Machine Learning (ML) operations, and intelligent cloud platforms.
Driving a futuristic edge are WNS Triange’s modular cloud-based platforms and solutions leveraging advanced AI and ML to provide end-to-end integration and processing of data to actionable insights. WNS Triange leverages the combined strength of WNS’ domain expertise, co-creation labs, strategic partnerships and outcome-based engagement models.