Call centers, emails, and FAQs on websites have long been the traditional methods of customer service used by the insurance industry. However, these channels make customers frustrated as they have to repeat the information across multiple engagements. Natural language processing, machine learning and automation, are leading conversational AI–enter intelligent assistants that are changing the insurance customer experience.
This article will discuss the shortcomings of standard customer service, what makes conversational AIs capable, real-world examples of insurance companies using chatbots, the strong of the two, the weak of the two and the future with AI reaching maturity. By blending empathy and efficiency, conversational AI in insurance, says DXC, promises better customer and employee experiences, increased cost savings and revenue opportunities.
Traditional Insurance Customer Service Shortcomings
Other industries have been ahead of insurance customer service. Insurers are trying to reach policyholders using traditional methods to do so quickly, being fast, personal and seamless, and policyholders expect these fast, personalized, seamless interactions. Common pain points include:
Long Wait Times. Long wait times are inevitable during peak calls, as the average for the skill set at call centers is 9 minutes to be put through to an agent. The high abandonment rates are a result of these delays.
Repeating Information. Customers become frustrated when they have to repeat their issue or repeat the same policy details to different channels and agents. Without context, the effort for the customer becomes higher.
Limited Service Availability. Online chat and call centers are open only for certain hours. You may expect a reply in 1–2 days by email. Customers can’t get information through any always-on channels.
Impersonal Experiences. Copy-and-paste email responses and scripted call center agents do not provide the level of service that is expected today. Most of the interactions are transactional instead of relational.
With the emergence of on-demand customer service in other sectors, policyholders now expect more from their insurers. Conversational AI achieves this in all its instances, such as 24/7 availability, personalization, and automated processes.
Conversational AI Capabilities for Insurance
Also known as chatbots, virtual assistants or digital agents, conversational AI allows text or voice-based interactions with human-like software. Backed by machine learning and natural language processing, these bots can understand the context to deliver seamless, helpful and personalized conversations.
Key capabilities of conversational AI for insurance include:
- Omnichannel Engagement. Seamlessly transition between channels like web chat, SMS and smart speakers without losing context.
- 24/7 Availability. Bots never sleep, ensuring customers get support anytime. This also reduces dependency on offshore call centers.
- Instant Answers. Common questions and policy details served through a knowledge base to remove hold times and email lags.
- Personalization. Remember customer data and conversation history to keep dialogues relevant.
- Process Automation. Take actions like changing a billing date or adding a vehicle without human involvement.
- Sentiment Detection. Identify customer emotions like frustration to elevate to a human agent when necessary.
With these attributes, conversational AI in insurance provides an always-on channel to improve experience while reducing operating costs.
Conversational AI Insurance Case Studies
DXC Technology is revolutionizing Insurance Business Process Services (BPS) with Conversational AI, enhancing efficiency, reducing costs, and improving customer satisfaction. DXC’s AI-driven solutions automate customer interactions, claims processing, and policy servicing, allowing insurers to scale their operations seamlessly.
Many top insurance providers have already implemented AI-driven bots to transform both customer and employee experiences:
Allstate. Allstate’s AI assistant Amelia handles 500,000 customer conversations per month. With a 50% containment rate, she automates routine transactions like making payments, freeing up human agents for more complex issues.
John Hancock. Life insurer John Hancock’s COIN bot helped boost life insurance sales by 10% in its first year by qualifying leads 24/7. COIN asks personalized questions to provide policy recommendations in minutes.
Geico. Geico’s AI-based virtual assistant Kate handles basic customer inquiries, integrating with policy systems to access customer data. Kate contains around 80% of inquiries that would have previously gone to a human agent.
Progressive. Auto-insurer Progressive launched their Flo bot to make purchasing policies easier. Flo walks customers through getting a quote, adding a vehicle and comparing plans through an engaging conversation.
Lemonade. Renter and home insurance startup Lemonade uses AI bots to power its entire claims process end-to-end. Customers file a claim via chat, and bots gather information and process payouts in seconds rather than weeks.
These real-world examples demonstrate how insurers are turning to conversational platforms over traditional methods to improve key metrics:
- Containment rate: % of inquiries resolved by bot alone.
- Customer satisfaction score (CSAT).
- Average handle time: time spent per inquiry.
- Loss ratio: payouts vs premiums collected.
The numbers don’t lie – conversational AI outperforms traditional customer service on key insurance metrics. But finding the right human vs. bot balance is key for long-term success.
Finding the Right Bot and Human Balance
While conversational AI unlocks immense potential, pure bot-only interactions have limitations in more complex situations:
- Trouble understanding vague customer questions.
- Difficulty detecting frustration or urgency.
- Inability to build emotional connections.
The goal of conversational AI in insurance should be to enhance, not replace, human agents. Below are best practices for combining automated and human touchpoints:
- Hand-off Triggers. Set rules like repeated questions, no responses, or high emotion to involve an agent automatically.
- Ongoing Optimization. Continuously improve the bot with real conversation data to address fail points.
- Agent Coaching. Help agents handle escalations better by providing them with full context from the bot conversation.
- Hybrid Model. Maintain a mix of bots for high-frequency transactions and human specialists for claims and complex queries.
Conversational AI should take the burden off simple and repetitive tasks so agents can spend time on higher-value interactions. This ultimately provides a better employee experience in addition to customer experience.
The Pros and Cons of Conversational AI in Insurance
Replacing traditional methods with conversational AI provides many benefits but also comes with its own set of challenges:
Pros:
- 24/7 availability to customers.
- Instant answers to common questions.
- Higher containment rates improve operational efficiency.
- Personalized conversations drive engagement.
- Faster claims processing through automation.
- Improves customer satisfaction and retention.
Cons:
- Significant upfront development costs.
- Ongoing maintenance of bots.
- Risk of bad data leading to wrong answers.
- Customers may prefer the human touch for emotional conversations.
- AI regulation poses risks to ongoing usage.
Insurers must view conversational AI as a long-term investment, not a short-term cost play. When built the right way, bots become smarter over time through machine learning – recouping the initial spending through better business performance.
Beyond Customer Service: Conversational AI’s Expanding Role in Insurance
Much of the conversations around conversational AI in the insurance sector have been around customer service, but the scope of AI in insurance drives beyond what we might normally assume. Virtual assistants that are being driven by AI are impacting risk assessment, the detection of fraud, underwriting, and customer education and thus, conversational AI ceases to be merely a game changer for parts of the insurance ecosystem.
Enhancing Risk Assessment and Underwriting
Traditionally, underwriting and risk assessment have been time-intensive processes that rely on extensive manual data collection and analysis. Conversational AI is changing this by automating key parts of the process, improving efficiency, and increasing accuracy.
Dynamic Data Collection. AI-powered chatbots can engage customers in natural conversations to collect critical data points for risk assessment. Instead of lengthy forms, policyholders can answer intuitive questions, streamlining the application process.
Personalized Policy Recommendations. AI-driven assistants analyze historical data and real-time inputs to offer policy recommendations that best fit the customer’s needs, ensuring better coverage alignment.
Predictive Analytics Integration. By leveraging machine learning, conversational AI can assess an applicant’s risk profile based on behavioral data, prior claims history, and external sources, reducing underwriting errors.
Revolutionizing Fraud Detection and Prevention
An annual cost for the insurance industry is billions in fraudulent claims. Real-time analysis and pattern detection are the key roles that Conversational AI is currently playing in fighting and preventing fraud.
Anomaly Detection. One of the ways AI models utilize their knowledge to find inconsistencies in customer interactions can indicate fraud. For instance, speech or text-based discrepancies in a claimant’s statements can be used to trigger additional verification steps.
Behavioral Biometrics. The authenticity of the user is assessed based on typing speed, hesitation and sentiment by AI-powered chatbots. Red flags can come up if a customer suddenly deviates from his or her usual interaction patterns.
Automated Cross-Referencing. Conversational AI can instantly cross-check claims data against historical records, industry databases, and external sources, flagging potential duplicate claims or suspicious activity.
Empowering Customer Education and Financial Literacy
Insurance policies can be complex and difficult for customers to understand. Conversational AI is emerging as a valuable tool in educating policyholders about their coverage, policy terms, and financial planning.
Interactive Policy Explainers. AI-driven chatbots break down policy documents into easy-to-understand conversations, helping customers grasp key terms, coverage limits, and exclusions.
Guided Financial Planning. Virtual assistants provide insights into how different insurance products align with a customer’s financial goals, offering scenario-based recommendations.
Claims Guidance. AI chatbots walk customers through the claims process step-by-step, ensuring they understand requirements and timelines, which reduces confusion and improves overall satisfaction.
The Outlook for Insurance Conversational AI
The global insurance chatbot market is projected to expand from $467.4 million in 2022 to $4.5 billion by 2032, reflecting a compound annual growth rate (CAGR) of 25.6% between 2023 and 2032.
Regarding the adoption of conversational AI, a survey revealed that 77% of insurance companies are at various stages of integrating AI technologies into their operations. This indicates a significant trend among leading insurance providers to implement conversational AI initiatives.
Bots will eventually take up more and more complex issues without having to rely on underlying natural language processing and sentiment analysis capabilities that continue to mature. More than one day down the line, virtual AIs can one day play the role of personal advisors who proactively recommend tailor-made coverage options instead of reactively.
In fact, the human element will continue to play a role in insurance conversations, particularly when you have to deliver bad news or during emotionally charged situations. The objective is to map out new ways to use AI to help and not replace the role of agents.
Conclusion: Blending Digital and Human Experiences is the Future
The occasion of conversational AI in insurance customer experience is seismic. Combining – put simply – the automation of organizational efficiency with the human things of empathy and trust, insurers can deliver on customers’ rising demands without the cost or expense.
The high-frequency transactions will be handled by AI, while the agents will focus on mission-critical claims and advisory conversations. Only those companies that can effectively blend their bots and humans for intelligent customer engagement will lead the age of intelligent customer engagement.
