The past few years have brought about significant changes in the insurance industry. With the rise of digital-first consumers and advancements in technology, the industry has had to evolve to catch up with customer expectations. The adoption of newer technologies has enabled insurers to provide more personalized and on-demand solutions to customers, enhancing the overall customer experience. Generative AI, such as ChatGPT, is now transforming how insurers interact with their customers. From creating personalized content to running complex code, helping insurers streamline their operations and offer better services.

According to Gartner, by 2025, 75% of all insurance operations will be conducted using AI, ML, and IoT, compared to just 10% in 2019. If utilized to their fullest potential, these technologies can lower costs by 10-15% and grow revenue by 15-20%.

Thus, streamlining the bottom line is no longer enough. Insurers must rethink lead generation, agent interactions, and the way they service customers.

Today customers research protection offerings through web reviews, social media, etc., in conjunction with advice from an agent. The preferred journey has moved beyond traditional agent interactions and now includes mobile phones, AI assistants and embedded interactions as well. Thus, insurers need to rethink customer acquisition strategies and build seamless on-off early engagement experiences.

With 67% of consumers ready to switch insurers after one bad experience(2), it is time for insurers to go “fully phygital”, creating a crack-proof digital-to-physical experience, vital to entertain and retain today’s digital consumers.

The insurance buying journey has gone online for a majority, that go offline only to seek advice when making the final decision or for solving complex queries, thus necessitating providing an end-to-end phygital experience :

1. Research and Purchase Decision:

As a customer starts their journey by searching online, insurers should be ready with a user-friendly app and website with easy-to-find information about their offering along with a strong social media presence. The website can include virtual assistants to answer questions and offer online quote tools to allow customers to get a quick estimate of their premiums. Insurance content can be pushed via gamification, simplifying complex policies while educating the customers. Such game-based rewards can nudge them into sharing relevant information, referring products further, and receiving further communications and offers across platforms. (Exhibit 3)

As customers navigate through digital platforms, the agents should be notified about the customer’s requests so that they can seamlessly modify quotes, answer queries and revert by text or call with personalized options for the customer to finalize decisions while getting the right advice from the live agent.

2. Onboarding:

Once the policy is purchased, digital onboarding should be offered through a mobile app or website with document uploading functionalities, e-signatures, and digitally delivered welcome kits.

Insurance agents should be updated in real-time on the progress made by the customer on the onboarding front so that they can guide them through the process in case of roadblocks without asking preliminary questions when customers contact help desks or set up in-person meetings.

3. Policy Management:

During the course of the policy, customers can be provided with a portal to change or update coverage, pay premiums, explore other policies, etc.

Agents with the customers’ profiles and purchases can easily educate them on their purchased policies and plans or guide them through choosing appropriate ones.

4. Claims Processing:

Customers are provided with an option to file the FNOL online along with options to submit damage proof, track the status of claims, etc.

Agents can help in physical claims filing with personalized support.

Since a traditional insurance policy can get complex, agent involvement is necessary throughout the customer’s lifecycle. This necessitates a traditional-digital hybrid model, and only insurers able to balance digital and agent interactions will have an edge with future generations.

As insurers work on building a smooth omnichannel experience and adding new customer touchpoints, the focus should also be on exploiting these channels to expand the customer base and fulfill a larger set of the customer’s needs.

Today, most Americans across generations worry about buying life insurance or other high-ticket policies, as it is an important long-term financial safety net (Exhibit 4). This presents a unique opportunity for US insurers to incorporate bite-sized products into their product portfolio and nurture the relationship to bigger ticket products later.

With customers being 5x more likely to buy additional insurance from an initial bite-sized insurer4, these insurance policies lay the foundation of a trusting relationship, while also providing a perfect entry point for customers who can’t afford or fully understand the more comprehensive policies.

This “lead-gen insurance” can be sold through easily-accessible digital touchpoints, covering specialized insurance at smaller premiums to reach a larger customer base. Various firms have introduced bite-sized offerings including SNACK a Life Insurer in Singapore that enables customers to accumulate Term Life, Critical Illness and Personal Accident insurance coverage using public transportation, completing step goals, dining at select restaurants, and paying micro premiums with each trigger. Similarly, SmallTicket in Korea primarily sells pet life & health insurance, but also offers travel insurance on an hourly/trip basis, protecting riders against accidental deaths and injuries.

Such bite-sized products can help insurers reach today’s task-rich, time-poor consumers. Additionally, sales are further boosted by fulfilling needs unmet by traditional large-ticket insurance policies while also:

Targeting underserved-upcoming markets such as the younger generation who are starting out in their careers and don’t have a lot of disposable income to spend on insurance, but may be interested in affordable, targeted policies like rental insurance or travel insurance.

Upselling and cross-selling larger policies such as selling a customer a small travel insurance, and then using that relationship to offer them other types of insurance, like medical and life insurance.

Simplifying customer experience by offering an easy-to-purchase, low-on-T&C interface attracting more customers and building loyalty.

Driving sales through data by utilizing AI to target ‘ideal’ customer segments (millennials, price-conscious individuals, travelers, etc.) through optimal channels (social media, mobile apps, direct marketing, or through bundling with other products and services).

Embedded insurance isn’t a new concept, but a plethora of new use cases have sprouted over the years. Today customers are looking to simplify all aspects of living.  Thus, purchasing insurance needs to be simpler than purchasing a pair of jeans from Amazon.

Examples of embedded insurance include:

i. Accident insurance with a full-day ski pass

ii. Dental insurance with dental kits

iii. Family life insurance or child life coverage while buying a crib

Though traditionally sold through direct or digital channels, and recently through third-party sites, the sales cycle for complex embedded insurance products should be reimagined by seamlessly embedding Agents into the buying experience. This way the conversation about protection is only natural. Agents can be guided by cross-sell and upsell recommendation engines that tailor the conversation with the customer based on their past behavior.

For example, an agent can initiate a conversation about health insurance with a customer purchasing travel insurance from a travel website. However, agent intervention is not needed for simpler products such as mobile screen insurance.

Over the years, customers have gotten accustomed to embedded P&C insurance. They now seem ready to accept it beyond P&C (Exhibit 5). Embedded insurance is estimated to make up 40% of total insurance sales by 2040(5).

Additionally, with the help of AI, insurers can further improve embedded services e.g., through dynamic pricing. Here, policies are priced based on real-time market changes and the customer’s risk tolerance, shifting from the traditional set-in-stone rule-based engines. This way insurers can maximize conversion probability i.e., quote optimal rates in seconds at the point of decision.

A well-thought-out embedded insurance experience can benefit all parties:

AI alone cannot reliably generate leads, convert them, or drive customer satisfaction since customers themselves continue to entrust advisors when making major life decisions such as buying the right insurance policy. Thus, the AI’s speed, consistency and cost-effectiveness should augment the agent’s relationships, experience and empathy to create a truly harmonized AI-gent, helping shorten turn-around time and increase customer satisfaction.

As social platforms change the way people communicate, customers now expect a quicker, more efficient resolution to their queries and to interact with an entity familiar with their journey to date. Customers are getting impatient and slow response time can be the deal breaker when making a policy-related decision. (Exhibit 7 & 8)

Enhancing customer experiences can drive growth of 4-8%. With the introduction of generative AI, insurers can now boost customer satisfaction through personalized responses to queries and assisting agents and customer care personnel quickly search large databases when solving complex claims or answering policy-related questions.

With an estimated savings of $80B by 20269, integration of AI into the customer service segment can:

Provide 24/7 Support: Using Chatbots, messaging apps and voice chat assistants that are mobile optimized

Reduce response times: External databases can be utilized to respond swiftly to complex queries, hold meaningful discussions, and determine when human intervention is necessary

Provide personalized recommendations: Using data from past queries and search history on channels customers interact with daily

Solve unstructured asks and queries: With OCR and NLP techniques in place free text communications can be structured and simplified

Improve reach: Through multilingual capabilities

Manage Frauds: Fueled with machine learning and deep learning, AI solutions can identify recurring patterns of unusual customer behavior and run background checks before onboarding or accepting applications

Automate time-consuming tasks: NLP can automate pre-call, in-call, and post-call activities like after-call documentation, agent coaching, and summarization

Boost Marketing efforts: Response rates on various platforms and interaction analytics can identify preferences, common pain points and opportunities which can boost customer retention and build an ideal customer profile

Generative AI is one such capability that can support multiple use cases throughout the journey.

Digital Experience: Today, the customer’s experience is largely dependent on the skills of marketers and agents. In a world of generative AI, traditional methods of customer segmentation and personas are no longer needed. Generative AI can target customers more precisely based on the information available and produce personalized messages on a case-to-case basis, handle email exchanges with customers along with generating website content, blog posts, etc.

Generative AI can also provide real-time feedback to agents on calls, summarize discussions, suggest next steps, and more.

Personalized Insurance Plans: Tailored fit policies can be designed without agent involvement using Generative AI and NLP tools that can consider lifestyle, employment, and health data extracted from policies, claims, conversation history, etc. Such policies that can cover customers unique asks to lead to greater satisfaction and retention

Underwriting: Vast amounts of data can be analyzed to generate personalized risk assessments based on a customer’s individual characteristics and circumstances. This also helps insurers identify risk factors that may not be apparent through traditional methods. Insurers can now also offer dynamic pricing based on driving data, claims history, etc.

Engagement and Retention: Predictive models can be used to anticipate the customer’s future needs and preferences. This can help insurers offer tailored coverage options that meet a customer’s unique circumstances, resulting in a more relevant experience, and boosting loyalty and stickiness.

Additionally, data from IoT devices, such as wearables and health monitoring systems, can be used to create personalized life insurance products that consider the customer’s specific health risks and needs. ML algorithms can also be used to analyze customers’ health records and family medical history to create customized life insurance policies that cover specific risks.