Artificial Intelligence in Insurance – Three Trends That Matter

Change is here, more is coming. The insurance market is dominated by massive national brands and legacy product lines that haven’t substantially evolved in decades. Sound familiar?

People are already placing bets. Insurance is an industry that venture capitalists consider so ripe for disruption that the founders of Lemonade, a New York-based insurtech company, raised one of the largest seed rounds in history simply by talking.

It’s not just the venture crowd. Warren Buffett has gone on the record saying that the coming of autonomous vehicles will hurt premiums for Berkshire-owned Geico.

There’s good data suggesting this is true. Buffett may have been referring to a 2015 KPMG report which predicts that “radically safer” vehicles, including driverless technology, will shrink the auto insurance industry by a whopping 60% over the next 25 years. And auto insurance is more than 40% of the insurance industry. But aren’t there massive upsides for insurance carriers resulting from business process automation?

(For readers with a strong interest in other financial applications of AI, please refer to our full article on machine learning applications in finance.)

Artificial Intelligence in Insurance – Insights Up Front:

Trends that business leaders should know about. In this article we look at three key ways that AI will drive savings for insurance carriers, brokers and policyholders, plugging into existing transformations within the insurance industry:

  1. Behavioral Policy Pricing: Ubiquitous Internet of Things (IoT) sensors will provide personalized data to pricing platforms, allowing safer drivers to pay less for auto insurance (known as usage-based insurance) and people with healthier lifestyles to pay less for health insurance
  2. Customer Experience & Coverage Personalization: AI will enable a seamless automated buying experience, using chatbots that can pull on customers’ geographic and social data for personalized interactions. Carriers will also allow users to customize coverage for specific items and events (known as on-demand insurance)
  3. Faster, Customized Claims Settlement: Online interfaces and virtual claims adjusters will make it more efficient to settle and pay claims following an accident, while simultaneously decreasing the likelihood of fraud. Customers will also be able to select whose premiums will be used to pay their claims (known as peer-to-peer (P2P) insurance).

Insurance as a global marketplace tends to be associated with public distrust (one Australian poll ranked sex workers as more trusted than the insurance industry), and this may present unique challenges to technology innovations – through AI or otherwise.

Therefore, a key concern introducing new technologies will be in convincing the public that automation isn’t simply a Trojan horse for denying their claims — a worry that 60% of consumers have expressed about purchasing coverage via chatbot, according to a recent survey by Vertafore.

Three Current AI Application Trends in Insurance / Insurtech:

We’ll take a look at all three major AI insurance trends one by one, examining at the current state of the technology, the changes underway, and the potential resulting shifts in the industry. We’ll begin with “behavioral pricing”:

1 – Behavioral Premium Pricing: IoT Sensors Move Insurance From Proxy To Source Data

IoT data is opening a slew of  are three key ways that IoT data will enable personalized insurance pricing:

  • Pay What You Risk: Telematic and wearable sensor data enables lower premiums for less risky behavior, including driving less and exercising more
  • Bundle Policy and Loss Prevention Hardware: Smart home companies will offer policy discounts to users of sensorized loss prevention technology, enabling cross-selling of devices and insurance
  • Verify and Settle Claims: IoT data markets will enable carriers’ faster access to verified risk management information, rather than relying on costly assessments and audits

Hypothesis: IoT disrupts insurance the same way that data science has been disrupting finance: moving analysis from proxy to source data.

In the old world: Financial models were once dependent upon statistical sampling of past performance to forecast future outcomes.

Today: Data science has enabled predictions based on real events, in real time, using large datasets rather than samples to make the best guess.

In the old world: Insurance carriers relied on risk pools constructed using statistical sampling.

Today: IoT sensors allow insurance carriers to price coverage based on real events, in real time, using data linked to individuals rather than samples of data linked to groups.

Big picture: In each industry we are moving from proxy data (about categories) to source data (about individuals).

See a pattern? Whether the asset is a stock portfolio or an ‘09 Honda Civic, a bond or a cargo ship, the shift in how the value of the asset is forecasted is driven by the type of data that technology can offer analysts.

Here’s an example: Usage-based or pay-per-mile car insurance demonstrates this logic. Telematics sensors allows real-time tracking of an underlying asset (cars) allowing for the roll-out of a new product line in the related insurance market (auto insurance) by personalizing the risk of the event being insured (a car accident).

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