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.
Generative AI Use Cases in Insurance
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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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.