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3 Use Cases for AI in Insurance That Will Revolutionize the Industry

Nov 6, 2023

Earlier this year, Eric Anderson, President of Aon, appeared before the U.S. Senate Committee on the Budget and discussed an existential threat to the insurance industry: the financial impact and overexposure to risk from climate change.

During his testimony and supplemental report, Anderson enlightened the gathered senators about the $313 billion near-average economic losses in 2022—only 42% of which was covered by insurance—and the crossroads faced by insurers, both large and small.

Their report particularly highlighted the growing exclusions for climate-related coverage triggers, the abandonment of risk-overexposed regions, and the avoidance of net-zero transition that could offset future risk. However, Anderson also emphasized a better path for the industry, one we envision will be driven by innovation.

That’s where artificial intelligence enters the chat. This technology has the potential to stabilize insurance prediction and business models not only for climate change but economic turbulence, cyberthreats, and other disruptive factors.

Here’s how insurance companies are using AI to stay profitable, efficient, and responsive in our all-too-dynamic world.

Eye in the Sky and AI: Your Fraud Detector

Hurricane Ian had an extreme economic impact, causing between $50 to $55 billion in insured loss across Florida, the Carolinas, and Virginia. Though this category 5 hurricane was one of the costliest on record, it by no means will be the last to inflict catastrophic damage on coastal communities. As extreme weather events barrage nations around the globe, there will be a rise in weather-related claims.

The insurance industry has not gone unaffected. Smaller insurers have been bankrupted by massive payouts and in response, some companies have pulled out of high-risk regions or elevated the property insurance premiums consumers pay. This trend decreases the number of people willing to pool their risk, which not only reduces the sources of shareholder profits but also places a greater burden on the remaining insured.

Rather than jettisoning business interests in high-risk regions, some insurers are mitigating that risk through AI-powered solutions, specifically when detecting insurance fraud. Insurance Journal outlined an emerging strategy using historical aerial imagery and drone footage to make regular and accurate property assessments. Companies can quickly review data using artificial intelligence to determine whether the damage described in fraud cases is the result of natural disasters or different, older circumstances.

Though insurers will need to team up with certified drone pilots and determine if their use falls under the FAA’s Code of Federal Regulations (CRF) Part 107 rules, there is a chance to cut down fraudulent claims along the way.

Teaching Algorithms to Fight Fraud & Data Breaches

The industry is increasingly offering cyber insurance, but how are insurers protecting their businesses and assets? Hackers will try to open any door, locked or otherwise, if they’re convinced there’s a wealth of information on the other side, and the industry has proven a profitable gateway to consumer data.

MAPFRE Insurance, a Massachusetts based insurance company, revealed the driver’s license numbers and vehicle information of more than 260,000 consumers. By pretending to request a quote, scammers were able to obtain the information they wanted by simply providing the name, address, and date of birth of people, whether they were customers or not.

How do you balance customer experience with security protocols? Artificial intelligence can prevent insurance companies from sacrificing one for the other, when used correctly.

For starters, AI-powered cybersecurity platforms can help insurers to cover and defend the extensive attack surface created by internal systems, customer-facing websites, mobile apps, and other digital assets. This allows organizations to offer a robust multichannel customer solution by hardening points of contact throughout the customer journey rather than narrowing their digital presence. With AI, these tools can analyze millions of events, almost in a heartbeat, to identify established and evolving threats and adapt better strategies to defuse these dynamic issues before they do damage.

Insurance companies can also use machine learning models as a further fraud preventative. By creating in-depth user profiles from behavioral data, insurers can proactively identify when specific consumers are deviating from their normal browsing, transactional, and device habits. For instance, if an existing policy holder is amending their life insurance, making an unprecedented person a beneficiary, while using an unfamiliar device, cybersecurity systems can flag the transaction to prevent cybercriminals from achieving their deception.

On-Demand Pricing Models: Paying Only When Products Are Used

Consumers are always looking for new ways to pay less, especially after years of post-pandemic inflation. Even as consumer prices are leveling off, people still feel the strain and are eager for relief. That sentiment is reflected in the KFF Survey of Consumer Experiences with Health Insurance with 55% of marketplace plan and 46% of employer sponsored insurance (ESI) policyholders rate their insurance negatively from a premium standpoint.

Achieving balanced pricing that satisfies both sides of the transaction has been a tricky situation, but artificial intelligence has the potential to decipher the code for insurers. Part of the solution stems from on-demand and usage-based insurance pricing models. The idea of customers being able to personalize their insurance, adjusting policies to the circumstances of their usage is already being explored in automotive insurance.

Why should consumers have to pay for insurance when their car is safely parked within their garage or driveway? Or why should they pay the same rates on a sunny afternoon in the suburbs as they should on a rain-soaked morning during rush hour? Or if they’re safe drivers? Progressive’s Snapshot® program uses predictive analytics to gauge a customer’s risk at a given moment and charge prices accordingly. As a result, people who drive less often or in safer areas will no longer overpay for their coverage.

Automotive insurance isn’t the only potential benefactor of this model. Let’s return to climate change. With global temperatures rising and life-threatening heatwaves now a common occurrence, there’s an emerging use of on-demand pricing: single-day heat stroke insurance.

Two major Japanese insurance companies offered these micro short-term policies dedicated to covering treatments for heatstroke (intravenous drip and similar treatments) for workers or individuals spending their days in sweltering heat. Though unlikely to become a market on its own, this supplemental insurance could help to protect consumers and generate additional profits for insurers.

Staying Receptive to New AI Use Cases in Insurance

Artificial intelligence is evolving in incredible and unprecedented ways. For most of 2022, generative AI still felt like a distant possibility, but now it has proliferated across industries. For insurances companies to take full advantage of this technology, leaders need to remain attuned to the latest innovations and build a foundation where they can quickly implement game-changing AI uses cases.

The trick is to create resiliency and protect today’s assets for tomorrow and beyond. The above Aon report captured the spirit of the moment with this quote: “Through the adoption of effective adaptation strategies and better disaster management and warning systems, we can better protect the communities in which we live and work.” With artificial intelligence, we can reduce the pain of change, mitigating risk and positioning customers and stakeholders for a more secure future.

Want to learn more about how AI in insurance can transform the industry and your business? Take a look at how w3r Consulting is empowering insurers to maximize their profitability, coverage, and security.

 

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