Strategies to Successfully Harness AI’s Impact on the Insurance Industry

From product automation to pre-emptive wearable technology, the insurance game is changing!

The insurance industry is currently undergoing an important evolution. Over the next five years, artificial intelligence will affect the whole value chain from marketing and distribution, underwriting and pricing, to claims management — driving new customer experiences, products, and operating models.

Due to the advantages of being able to deploy sizable compute power and best talent at sizable datasets, large incumbents can turn A.I. to their advantage to counter the disruptive digital-native innovators.

This piece explores the six major opportunities in the market today for players to harness A.I. to delight their customers and boost profits:

1. Delighting customers with slick digital experiences served by A.I. engines — such as personalized communications including frequency, timing, format, content, and channel

2. Automating insurance products through agile underwriting and automated claims management — such as Lemonade’s app-based claims that ensure reimbursements within days

3. Behavior-based dynamic underwriting leveraging more datasets — such as using non-traditional demographic, behavioral, attitudinal, psychographic, telemetry datasets

4. Proactive and pre-emptive life coaching — such as using wearable tech as well as non-traditional datasets to help customers manage their risk and make better life decisions

5. Bundling products into a holistic, personalized and cost-effective offering to address an individual’s overall risk — such as using the individual’s opted-in health data gathered for their health insurance to better price and package their car insurance

6. Rethinking products to drive new profits in seemingly commoditized markets — such as usage-based insurance for the ‘as-a-service’ markets like car insurance with Uber, home insurance with AirBnB

Let’s analyze the five attributes of success for deploying A.I. within an insurance company:

1. Technical teams are empowered to become citizen data scientists — democratizing the value of A.I.

2. Strong and active sponsorship from forward-leaning executives in your business — unlocking the blockers to progress across your organization

3. Stable and dedicated cross-functional teams working in an agile way are empowered to test and learn — and fail too sometimes

4. Non-traditional data sourced and integrated — such as telematics and IoT

5. Inter-business unit and cross-sector collaboration where possible

Strategies to Harness the Transformation

The insurance industry is next amidst a span of consumer services adopting A.I. to drive better customer experiences, optimize operations, empower its employees, and enhance its product offerings.

As an industry, it has been historically slow to adopt AI, largely due to a number of structural barriers, ranging from third-party agents masking customer interactions to annual renewals lowering the impetus for a digital customer relationship.

These barriers are being overcome with new insurance models, changing customer expectations, and systems that allow companies to drive impact via digital assets based on behavioral data and derived from extensive external datasets. The following key strategies are empowering forward-leaning insurers to disrupt the value chain with A.I.

Delighting Customers in the Digital Age

The majority of insurance processes are two or three decades old. As the industry transitions from providing a product to providing a service, new applications that focus on customer experience are emerging.

Digital communication channels, like chatbots, create a new medium to engage with customers. Embedding A.I. ensures that customers are engaged with the right message at the right time, creating a personal and intuitive customer experience.

Automated Insurance: Low-touch, Low-friction Products

This digital disruption has led to customers increasingly expecting low-touch, low-friction products, i.e., underwriting and claims-handling, that’s instant and non-intrusive.

New entrants, such as home and renters insurer Lemonade, offer a specialized service — its straightforward app-based claims process reimburses users within days, and underwriting occurs within minutes. In order to compete, existing insurance companies must develop new processes that use analytics to support agile underwriting, claims handling, and customer interaction.

From Pre-set Pricing to Dynamic Underwriting

Actuaries use static population averages to determine risk, which means individuals fall into one of relatively few price bands. What’s more, manually analyzing complex medical data to evaluate risk is a long, manual, and deeply imperfect process. A.I. helps break away from actuarial tables and draws upon additional data sources such as census or events pertaining to one individual in order to provide underwriting decisions and pricing that reflects the specificity, nuance, and diversity in human life.

Insurers Become Virtual Life Coaches

The role of the insurance company is shifting from reactive claims handling in the aftermath of an event, towards preemptive, continuous, and proactive life-decision support. While change facilitates more meaningful interactions with customers, it stems from the increasing breadth of data streams — from medical data to wearable tech. These insights derived from this data make for better risk assessment. Just imagine insurer integration into Alexa to guide consumers away from risky behavior, and rewards for positive or low-risk actions.

Bundling & Cross-selling to Lower the Individual’s Overall Risk

Consolidating data from different business lines in the organization allows insurers to apply A.I. systems to derive holistic insight and recommendations. By creating a holistic viewpoint of the customer, insurers can offer more customized products at a better cost. For example, a person that takes medication on time — an insight gathered based on prescription data — may be a more careful driver. This insight can improve the quality of motor underwriting because the insurer can better assess the risk of the individual, thereby creating an impact for both consumers and insurers.

A.I. as a Muscle to Capture Profits in New Product Offerings

While A.I. is a key tool in the race to compete in increasingly commoditized traditional motor, life, and health insurance lines of business, A.I. is even more important for capturing the profits in newly emerging offerings. Significant profits await in new lines of business where a dynamic data-driven approach is essential to cover and price new risk.

• Usage-based insurance — The gig economy created a market for a time-limited, single unit, on-demand coverage (called ‘usage-based insurance’). Uber, WeWork, Airbnb, and other players driving the ‘as-a-service’ business model revolution are well poised to capture profits within the new usage-based insurance value chain. A.I. applied to measure risk, pricing, and underwriting policies can serve as a muscle for incumbents to compete against digital and A.I. native horizontal entrants.

• Cybersecurity insurance — Cybercrime is expected to cost the world $6 trillion per year by 2021. In keeping with this, cybersecurity insurance is predicted to grow from $3.9bn in premiums in 2017 to $23.15bn in 2025. A dynamic data-driven risk analytics approach is essential as threats are constantly changing, and protocols and security measures to handle them evolve by the day.

• Parametric insurance — Consumers find parametric insurance popular, given its simplicity where pre-fixed terms confirmed by publicly available information avoids a claims process. Parametric insurance is also a win for insurance companies where eliminating the claims process is one example of reduced complexity. A.I. is the muscle allowing an insurance company to decipher and quantify risk correlations to rapidly build, test, and scale products matching consumer demand.

Become the Insurer of Tomorrow:

Your business can keep up with the pace of change by building on initial data and A.I. foundations to capture a larger segment of the customer value chain and further enhance margin and market share going forward.

There are two ways that you can implement this change:

Data Partnerships and Ecosystems

Connected device partnerships: introducing telematics data into A.I. systems provides stronger risk assessment and drives more valuable dynamic pricing.

Cross-sell partner services to add customer value: deepen existing customer touchpoints and create new revenue streams by cross-selling services or products from partner organizations that add value — E.G., offering tour packages for customers purchasing travel insurance.

How is your insurance provider streamlining services and increasing their efficiency with the latest available technologies?