Artificial intelligence in insurance: How to make insurance more personal, affordable and adaptable

When was the last time your insurance company realized you had a life change, like getting married or buying a house or having a baby, and designed a policy just for you? And presented it to you without being asked?

Without encroaching on personal privacy, this is the vision of insurance in the future: personalized, affordable and adaptable. Artificial intelligence (AI) can help insurance companies understand their customers in powerful new ways and be proactive — and competitive — about serving their needs.

Why AI?

You may have heard the terms analytics, advanced analytics, machine learning and AI. Let’s clarify:

  • Analytics is the ability to record and playback information. You can record the customer transactions and report the number of insurance services that a customer uses.
  • Analytics becomes advanced analytics when you write algorithms to search for hidden patterns. You can cluster customers based on which insurance services they use.
  • Machine learning is when the algorithm gets better with experience. The algorithm learns, from examples, to predict the insurance services that a customer will use.
  • AI is when a machine performs a task that human beings find interesting, useful and difficult to do. Your system is artificially intelligent if, for example, machine-learning algorithms infer a customer’s need and recommend a solution.

If you’re in insurance, here’s how to make sense of the terms analytics, advanced analytics, machine learning and AI. Click image to expand.

AI is often built from machine-learning algorithms, which owe their effectiveness to training data. The more high-quality data available for training, the smarter the machine will be. The amount of data available for training intelligent machines has exploded. According to an article on Forbes.com, by 2020 every human being on the planet will create about 1.7 megabytes of new information every second. According to IDC, information in enterprise data centers will grow 14-fold between 2012 and 2020.

For insurance companies used to traditional sources of customer information like transaction history, income and age, the importance of this new data may not be obvious. But using this data to get to know customers is key to creating value. Here’s what it looks like when insurance companies use AI to understand the customer journey and help along the way.

Here’s what it looks like when you apply industrialized AI in insurance. Click image to expand.

More affordable

Fraud is wasteful for insurance companies and expensive for customers. According to the McKinsey Global Institute, 10 percent of property and casualty claims costs are potentially fraudulent. AI can detect anomalies in insurance claims. It can monitor claims and patterns of customer interaction that suggest fraud. It can reduce the amount of resources wasted on fraudulent claims. Insurance that costs less to provide can become more affordable for customers.

More responsive

Responsive insurance companies require the right staff. AI can forecast customer demand so that insurance companies can handle spikes in service needs. It can help administrators plan staffing to best meet those needs. According to the McKinsey Global Institute, insurance companies can increase productivity 6 to 8 percent by using AI to optimize staff and resource availability. You anticipate demand, respond quickly and keep customers happy.

More personal

Insurance companies have a chance to personalize relationships with customers. Take, for example, customizing insurance risk based on individual driving behavior. AI can learn patterns of driver behavior, improve risk calculation and personalize policy terms in response. According to the McKinsey Global Institute, an insurer may drop risky driver behavior by 53 percent by personalizing risk calculation. Getting personal lowers risk for both insurer and customer.

More valuable

Personal service is most valuable when it is delivered in real time. AI allows insurance companies to automate policy decisions, which simplifies the interaction between customer and company. According to the McKinsey Global Institute, 39 percent of administrative activities can be completely automated with AI. An AI can learn to spot signs of life changes in customers such as a having a baby, make administrative decisions and respond, in real time, with insurance options that fit. You automate operations, respond to customers quickly and increase the value of your services.

Applied AI is a differentiator

If we see AI as just technology, it makes sense to adopt it according to standard systems engineering practices: Build an enterprise data infrastructure; ingest, clean and integrate all available data; implement basic analytics; build advanced analytics and AI solutions. This approach takes a while to get to ROI.

But AI can mean competitive advantage. When AI is seen as a differentiator, the attitude toward AI changes: Run if you can, walk if you must, crawl if you have to. Find an area of the business that you can make as smart as possible as quickly as possible. Identify the data stories (like detecting fraud or the next best service offer) that you think might make a real difference. Test your ideas using utilities and small experiments. Learn and adjust as you go.

It helps immensely to have a strong Analytics IQ — a sense for how to put smart machine technology to good public use. We’ve built a short assessment designed to show where you are and practical steps for improving. If you’re interested in applying AI in insurance and are looking for a place to start, take the Analytics IQ assessment.

See more of Jerry Overton’s thoughts in Wired Magazine: Welcome to the Age of AI-Based Super Assistants.


Jerry Overton is a data scientist and senior principal in DXC Technology’s Analytics group. He leads the strategy and development for DXCs Advanced Analytics, Artificial Intelligence and Internet of Things offerings.

Jerry is the author of the O’Reilly Media ebook, Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist. He teaches the Safari Live Online training course “Data Science at Enterprise Scale.” @JerryAOverton

RELATED LINKS

Managing the challenges of machine learning

The value of orthogonal data to insurers

Defining the Future of Digital Insurance

 

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