The industry is on the verge of a seismic, tech-driven shift. A focus on four areas can position carriers to embrace this change.
Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. His digital personal assistant orders him an autonomous vehicle for a meeting across town. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into “active” mode. Scott’s personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott’s assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a “pay-as-you-live” basis, will increase by 2 percent for this quarter. The additional amounts are automatically debited from his bank account.
While this scenario may seem beyond the horizon, such integrated user stories will emerge across all lines of insurance with increasing frequency over the next decade. In fact, all the technologies required above already exist, and many are available to consumers. With the new wave of deep learning techniques, such as convolutional neural networks, artificial intelligence (AI) has the potential to live up to its promise of mimicking the perception, reasoning, learning, and problem solving of the human mind (Exhibit 1). In this evolution, insurance will shift from its current state of “detect and repair” to “predict and prevent,” transforming every aspect of the industry in the process. The pace of change will also accelerate as brokers, consumers, financial intermediaries, insurers, and suppliers become more adept at using advanced technologies to enhance decision making and productivity, lower costs, and optimize the customer experience.
Artificial intelligence can deliver on industry expectations through machine learning and deep learning.
As AI becomes more deeply integrated in the industry, carriers must position themselves to respond to the changing business landscape. Insurance executives must understand the factors that will contribute to this change and how AI will reshape claims, distribution, and underwriting and pricing. With this understanding, they can start to build the skills and talent, embrace the emerging technologies and create the culture and perspective needed to be successful players in the insurance industry of the future.
of physical robotics
Smart contracts enabled by blockchain instantaneously authorize payments from a customer’s financial account. Meanwhile, contract processing and payment verification are eliminated or streamlined, reducing customer acquisition costs for insurers. The purchase of commercial insurance is similarly expedited as the combination of drones, IoT and other available data provides sufficient information for AI-based cognitive models to proactively generate a bindable quote.
Highly dynamic, usage-based insurance (UBI) products proliferate and are tailored to the behavior of individual consumers. Insurance transitions from a “purchase and annual renewal” model to a continuous cycle, as product offerings constantly adapt to an individual’s behavioral patterns. Furthermore, products are disaggregated substantially into microcoverage elements (for example, phone battery insurance, flight delay insurance, different coverage for a washer and dryer within the home) that consumers can customize to their particular needs, with the ability to instantaneously compare prices from various carriers for their individualized baskets of insurance products. New products emerge to cover the shifting nature of living arrangements and travel. UBI becomes the norm as physical assets are shared across multiple parties, with a pay-by-mile or pay-by-ride model for car sharing and pay-by-stay insurance for home-sharing services, such as Airbnb.
The role of insurance agents has changed dramatically by 2030. The number of agents is reduced substantially as active agents retire and remaining agents rely heavily on technology to increase productivity. The role of agents transitions to process facilitators and product educators. The agent of the future can sell nearly all types of coverage and adds value by helping clients manage their portfolios of coverage across experiences, health, life, mobility, personal property, and residential. Agents use smart personal assistants to optimize their tasks as well as AI-enabled bots to find potential deals for clients. These tools help agents to support a substantially larger client base while making customer interactions (a mix of in-person, virtual and digital) shorter and more meaningful, given that each interaction will be tailored to the exact current and future needs of each individual client.
Regulators review AI-enabled, machine learning — based models, a task that requires a transparent method for determining traceability of a score (similar to the rating factor derivations used today with regression-based coefficients). To verify that data usage is appropriate for marketing and underwriting, regulators assess a combination of model inputs.
They also develop test policies for providers when determining rates in online plans to ensure the algorithm results are within approved bounds. Public policy considerations limit access to certain sensitive and predictive data (such as health and genetic information) that would decrease underwriting and pricing flexibility and increase antiselection risk in some segments.
Price remains central in consumer decision making, but carriers innovate to diminish competition purely on price. Sophisticated proprietary platforms connect customers and insurers and offer customers differentiated experiences, features and value. In some segments, price competition intensifies, and razor-thin margins are the norm, while in other segments, unique insurance offerings enable margin expansion and differentiation. In jurisdictions where change is embraced, the pace of pricing innovation is rapid. Pricing is available in real time based on usage and a dynamic, data-rich assessment of risk, empowering consumers to make decisions about how their actions influence coverage, insurability, and pricing.
for accelerating changes
1. Get smart on AI-related technologies and trends
Although the tectonic shifts in the industry will be tech-focused, addressing them is not the domain of the IT team. Instead, board members and customer-experience teams should invest the time and resources to build a deep understanding of these AI-related technologies.
Part of this effort will require exploring hypothesis-driven scenarios in order to understand and highlight where and when disruption might occur—and what it means for certain business lines. For example, insurers are unlikely to gain much insights from limited-scale IoT pilot projects in discrete parts of the business. Instead, they must proceed with purpose and an understanding of how their organization might participate in the IoT ecosystem at scale.
Pilots and proof-of-concept (POC) projects should be designed to test not just how a technology works but also how successful the carrier might be operating in a particular role within a data- or IoT-based ecosystem.
2. Develop and begin implementation of a coherent strategic plan
Building on the insights from AI explorations, carriers must decide how to use technology to support their business strategy. The senior leadership team’s long-term strategic plan will require a multiyear transformation that touches operations, talent and technology. Some carriers are already beginning to take innovative approaches such as starting their own venture-capital arms, acquiring promising insurtech companies and forging partnerships with leading academic institutions. Insurers should develop a perspective on areas they want to invest in to meet or beat the market and what strategic approach — for example, forming a new entity or building in-house strategic capabilities — is best suited for their organization.
This plan should address all four dimensions involved in any large-scale, analytics-based initiative — everything from data to people to culture (Exhibit 2). The plan should outline a road map of AI-based pilots and POC and detail which parts of the organization will require investments in skill building or focused change management. Most important, a detailed schedule of milestones and checkpoints is essential to allow the organization to determine, on a regular basis, how the plan should be modified to address any shifts in the evolution of AI technologies and significant changes or disruptions within the industry.
There are four core elements in defining a successful artificial-intelligence strategy.
In addition to being able to understand and implement AI technologies, carriers also need to develop strategic responses tocoming macrolevel changes. As many lines shift toward a “predict and prevent” methodology, carriers will need to rethink their customer engagement and branding, product design, and core earnings.
Mckinsey Global Institute