AI’s Critical Role in the Emerging Standard of Insurance Customer Care

Meeting claimants expectations requires insurers to rethink how they approach customer service—how AI can help improve communications and accelerate claims handling.

(Image credit: Daniel Reche.)

Loss of or damage to property creates stress and often strong emotions, so when a customer calls, ensuring a positive experience requires empathy and immediate answers. Whether filing a claim, seeking policy information or renewing a policy, customers expect service on-demand. Meeting those expectations requires insurers to rethink how they approach customer service and how artificial intelligence (AI) can help improve communications and accelerate claims handling.

Since call center operators are a customer’s first point of contact, having a sound contact strategy is key. How can operators add value to the process and solidify customer relationships? Employing AI and machine learning as part of your contact strategy will help operators to know customers better and ensure a customer-centric focus. In addition, better, more insightful customer data helps insurers tailor their marketing efforts to specific customer groups instead of using a one-size-fits-all approach. AI can also improve data quality across the enterprise, enhancing data governance and privacy by controlling access and preventing data breaches.

Uncovering Unknown Facts About Customers

AI and machine learning can uncover unknown facts about customers by tapping external data and looking for similarities in internal data to produce a holistic customer profile. AI can analyze calls and identify call patterns that tell operators what types of customers are likely to call with specific questions or requests. For example, if a customer calls with a policy question, there may be a 60 percent chance the same customer will reach out again to the call center or through other channels for information on related topics. Having this information enables operators to proactively provide information, which can reduce call volume and save customer time.

Insurers can address high call handling times for low-value calls using AI to redirect, minimize or prevent these calls to improve efficiency and reduce costs. In most contact centers 40-60 percent of all calls are inquiries that can be handled using chatbots or conversational AI solutions like Alexa or Google Assistant. For customers who lack the time or desire to get information via phone, pushing out content through various social channels or secure messaging platforms may be more appealing. However, it is important that all channels be connected end-to-end.

One global property and casualty insurer applied an analytics platform informed by AI to improve the quality of its customer experience, help supervisors monitor call quality and help call center operators better understand customer sentiment during insurance claims calls. This resulted in 85-90 percent call dialogue accuracy and a 35-40 percent reduction in supervisor review time.

Understanding cost versus value will also help improve your contact strategy by measuring the returns on the amount of time, money and effort spent on calls. AI can provide transparency into how much of your call center’s time is spent addressing customer needs, adding value to customer interactions or creating value for your organization.

One organization measured the quality of its call center claims process by reviewing dozens of phone calls each month and providing general feedback to the operators. To create a more valuable review process, the company implemented an AI-driven, real-time call transcription, call listening and call analysis using IBM Watson. Watson listens to every call, analyzes them against quality parameters and measures customer sentiment. Real-time, context-sensitive suggestions, personalized for the individual caller, appear on the operator’s dashboard to help guide the call. In addition, Watson’s feedback is used for personal training to help improve operator performance, boost morale and reduce attrition.

During the underwriting process, AI and machine learning can identify and evaluate past historical positions, capture and summarize relevant information and simplify research. Using AI to automate underwriting audits will catch discrepancies inherent in human decision-making that can significantly impact the bottom line. Predicting claim likelihood through AI and machine learning enables insurers to revise ratings or prove the worthiness of a risk at a certain price point. AI-driven drones and visual recognition can help manage claims and greatly improve the outcome for everyone by expediting the settlement process, improving customer satisfaction, and reducing costs.

Managing the Cost of Modernizing Technology Infrastructure

It can be daunting to look at the cost of modernizing technology infrastructure in a traditional industry such as insurance, since it’s not unusual for call center infrastructure to be decades old. AI does not require replacing infrastructure end-to-end; however, some upgrades are necessary to ensure your technology can support the intelligence needed to provide insights that enable proactive assistance. Rather than replacing an entire contact center system, infusing AI and real-time analytics can provide dashboards with information operators need to more effectively converse with customers and solve problems efficiently.

While AI is not the solution to every problem, it is a powerful tool that can elevate your call center processes, provide deep insights into your customer base, improve efficiency across the organization, and enhance customer experience and satisfaction.

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Ajish Gopan // Ajish Gopan serves as business partner and commercial lead for Cognizant’s Digital Business, AI & Analytics business. In his work with insurance carrier clients, Gopan consults and takes accountability as a trusted partner to help them achieve transformational business outcomes leveraging data and insights. He has an impressive record of success in his engagements, often with some of the world’s largest insurers, by leveraging a combination of deep industry knowledge, proven consulting artifacts, analytics tools and platforms and judiciously leveraging a partnership ecosystem.

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