Science, Applied: Three Ways AI and ML are Advancing the Insurance Industry
From maximizing advertisement relevance to customizing user experience, the benefits of applied sciences and advanced data analytics have become more apparent as industries adopt data-driven approaches to create new competitive advantages. Our previous post from the “Science, Applied” blog series explored how Artificial Intelligence (AI) and Machine Learning (ML) are being leveraged by media and entertainment companies. In this post, we focus on companies in the insurance industry that are implementing applications of data science to deliver efficient, risk-adjusted solutions by detecting fraudulent activity and providing a personalized customer experience. The best place to start is by looking at some of the technological trends being used by insurance companies today.
Growing Trends in the Insurance Industry
Customer Experience & Coverage Personalization
With access to a customer’s behavioral, geographic, social, and account data, AI-enabled chatbots can provide seamless automated buying experiences. These bots are quickly becoming the industry standard. According to a 2020 MIT Technology Review survey of 1,004 business leaders, customer service (via chatbots) is the leading application of AI being deployed today. The study shows that 73% of respondents indicated that by 2022, it will still be the leading use of AI in companies.
Behavioral-Based Policy Pricing
In the auto insurance industry, we are seeing ubiquitous IoT sensors provide personalized data to pricing platforms, allowing safer drivers to be rewarded by paying less for auto insurance (known as usage-based insurance). These techniques have expanded beyond auto insurance, and we are now seeing health & dental insurance companies also use IoT sensors that provide people who maintain a healthier lifestyle with a lower rate for insurance. A recent article highlighted dental insurance company Beam Digital for their use of IoT technologies. This company provides a smart toothbrush to every customer and monitors their oral health, while using this information to support a dental insurance plan. Beam sends the customer notices and encouragement if their brushing habits are falling short of the required standard. The company hopes this will result in improved dental hygiene and reduced premiums
Faster, Customized Claims Settlement
Online interfaces and computer-vision enabled virtual claims adjusters now make it streamlined and more efficient to settle and pay claims following an accident, while simultaneously decreasing the likelihood of fraud. Customers are now also able to select their preferred provider's premiums that will be used to pay their claims (known as peer-to-peer/P2P insurance). Data science applications have enabled the required higher-fidelity predictions based on events, in real-time, using large datasets rather than samples to make the best guess.
Industry Leaders That Are Adopting AI/ML
With advancements in AI/ML applications, more insurance companies are now actively leveraging preexisting data to increase the depth of understanding they have of their customers. Companies like State Farm, Liberty Mutual, Allstate, and Progressive are among a few of the industry leaders that are adopting AI and ML applications into their business model.
Greg Firestone, Vice President of Data Science at Allstate Insurance, explained in a recent interview why his company began leveraging anti-fraud technologies to mitigate fraudulent claims. “It's very hard to measure sometimes, but it's happening,” Firestone said. “The best prevention is really being aggressive: using AI and data to find fraud. Data is your friend in this regard. Fraud is a problem that impacts all insurance companies, and we need to focus on it and make sure the fraudsters realize that we're not easy marks.” The company leverages an AI-based solution to monitor and flag suspicious claims, however, they understand that keeping an eye on future fraud trends will still require a human touch. Large insurance companies process thousands of claims daily, making it impossible for a team of human analysts to thoroughly review each instance for fraudulent activity. Thus, many insurance companies are leveraging advanced AI systems to automate this process, which allows them to reserve their teams for claims the AI-based solution has flagged as suspicious.
Liberty Mutual Insurance
Last year, in an official press release, Liberty Mutual announced a strategic relationship with Groundspeed Analytics, Inc. to cut the time to extract submission data by 50% through the use of Artificial Intelligence (AI). “Properly evaluating customer submission documents is one of the most critical aspects of the underwriting process, and current methods don’t take advantage of the value locked in these documents.” By leveraging the available data in submission documents in a “data first” approach, Groundspeed is helping Liberty Mutual to make better risk selections, improve time-to-quote, and deliver better customer service.
In recent news, Progressive Insurance is reportedly leveraging Machine Learning algorithms for predictive analytics based on data collected from customer drivers. Progressive claims that “its telematics (integration of telecommunications and IT to operate remote devices over a network) mobile app, Snapshot, has collected 14 billion miles of driving data.” By feeding the labeled data which connects accidents with the accordant driving data, the insurer could identify a pattern and predict a new customer’s likelihood of causing accidents by simply gathering hours of their driving data. This data collection process could encourage the drivers to monitor and optimize their driving habits, and possibly decrease their number of accidents. As for the insurance company, increasing further data science capabilities allows them to gather a better outlook on the possible return and risk.
Customer Acquisition Through Predictive Analytics
Traditionally, insurance agents have relied on relationship-selling supported by lead generation tools. Today, new tools exist to help insurance carriers start to predict customer needs for insurance products. These tools use predictive analytics to look for “active signals” of customer intent and then tie in relevant insurance products. For example, knowing that a construction company has just won a large contract is a good signal that they might want additional umbrella insurance. Also, knowing that a business has just secured its first institutional round of funding is a good signal that the firm needs Directors & Officers insurance. Broadly speaking, algorithms use these predictive signals to look for specific events, or business life cycle activities (e.g.starting a business), to offer new and relevant insurance products that fit each customer’s needs. Moreover, algorithms can be used to identify other related businesses that have similar characteristics (e.g. revenue size, industry type, location) to an insurance company’s existing customer base.
Leveraging AI and ML capabilities for gathering and analyzing social, historical, and behavioral data allows companies to gain a more accurate understanding of their customers and provide better products and services. The three industry leaders mentioned in this article are just a few of many companies harnessing the power of applied science capabilities to better understand their customers and their data. Through more precise risk prediction, personalized customer policies, and automated settlements, both insurance providers and customers are able to benefit from the impact of science applied to technologies in the insurance industry.