During tumultuous times, insurance companies are often the first place a person turns when they need a hand. In this age of COVID-19, the demands on insurers have been amplified, as they act as the first line of support for a huge number of people who suddenly need help mitigating the effects of missed debt payments, cancelled travel plans, health care, and more.
Even governments are calling on insurers to step up and fill coverage gaps. This is a challenging time where insurers are going beyond the contract for their customers. For example, several Canadian auto insurers have reduced their monthly fees, both in recognition of the fact that we are driving less and to help people through this financially difficult time. Even so, insurance companies themselves are vulnerable to the economic effects of this global pandemic.
Now, more than ever, access to smart, up-to-the-moment data will make or break the ability of the insurance sector to adapt and pivot their strategy and products as quickly as the changing state of the external world. Luckily, the insurance sector already has a special appreciation for the importance of in-depth data.
But it’s about more than just having the data. It’s about the way we process it to gain useful insights in a climate where circumstances change every day. This is nothing new for the insurance industry, where innovations like Artificial Intelligence (AI) are already revolutionizing this process.
AI gives us the ability to transform raw information into organized, actionable data at rapid-fire speeds. At Apply, we look at AI as three precise data analysis tools working together for one purpose. This technology processes raw data incredibly quickly. It also makes smart selections about the right data to input and process out in order to make useful predictions. Finally, it turns data into actionable insights that translate to real-world benefits. In short, these factors combine into accelerated learning- transforming our experiences into actionable insights at a rapid-fire pace.
AI offers insurance providers with the tools they need to make predictions and to customize their coverage both for the benefit of their business model and their customers. But AI isn’t a one size fits all solution. Before you put AI to work for your company, you first need to make some strategic choices about the best way this technology can meet your unique needs.
A Personal Coach for every Customer Interaction
A real-time cheat sheet for your customer service team.
Over the course of one hour, your customer service team encounters an entire spectrum of personalities, demands and nuanced requests for support. What if you could give each team member a personal coach who could help them steer these interactions towards the best possible outcome?
An AI-enabled technology called sentiment analysis is doing just that. Built on a foundation of Natural Language Processing (or NLP), sentiment analysis is already in use in many call centers. Sentiment analysis engines ‘listen’ to what a customer is saying over the phone or through text typed into a chatbox.
Imagine it this way: A caller dials into an insurance claim center about an irregular claim. They’re irate, and hard to deal with. Before sentiment analysis, the call center agent would need to put that customer on hold, go speak to their manager and explain the situation, and then return with a response- and it’s very possible the whole process would need to be repeated several times before the matter was resolved.
Now, with sentiment analysis, you have an AI tool involved straight from the beginning of a call, like a real-time coach. And it can provide second by second feedback on what to say to calm the customer down, what the company can actually offer, and how to avoid accidentally agreeing to additional liability. This means shorter call times, faster problem resolution, and outcomes that are always consistent with the company’s business model.
Data Analysis at Lightning Speeds
Let the machine do the learning, and spend your time making decisions instead.
All companies look at historical patterns in order to hypothesize what the future may hold. In insurance, actuaries pick five or ten data points, and develop predictions based on these metrics. This is a good strategy for making informed decisions, but its potential is limited by the time and capacity of a team.
AI-driven machine learning is already changing the way insurers analyze claims. Not only does this technology work incredibly quickly, but it’s capable of analyzing not just five or ten data points, but hundreds of thousands of data points at the same same time. This breadth of data means that your AI actuary can pin-point claim trends almost as soon as they are submitted.
Imagine it this way: An insurer puts AI to work sorting through claim information for every single make, model, and year of vehicles covered under its policies. The information the AI comes back with points to one startling trend- the majority of transmissions in the 2010 Model R manufactured by Automaker A are experiencing faults at the 200,000 km point.
This info can be put to good use. The insurer can reach out to all owners of this 2010 Model R and let them know that they’re at a higher risk of transmission failure. The insurer can also offer an insurance coverage upgrade that will safeguard these customers ahead of time by paying for a transmission replacement if the need comes up in the future.
This lightning-fast data processing can easily be customized to meet the demands of any industry or sector. Really, the only limit here is the data you collect and how you approach the results you receive on the other side.
Insurance Done Ethically
Making it easy to be good.
As we see in the example above, enhanced smart data and in-depth analysis tools open up new avenues for companies to act in a more proactive and ethical way when it comes to customer service.
AI reveals a clearer picture of the way products are serving customers- and where they’re missing the mark. At the same time, this data-crunching tool gives companies a more precise view of their benefit and loss ratios. Together, these insights let companies make smarter decisions that balance their business goals with tailored customer service and product offerings.
One example of a way an insurance provider is putting AI-driven data to work for the benefit of its customers is Manulife Vitality. Customers are asked to self-report on metrics like their weight, how often they exercise, and whether or not they smoke. Afterward, they are told their ‘physical age’, along with actionable suggestions on how to improve their overall health. Finally, Manulife rewards its customers for healthier behaviours, based on self-reporting along with data gathered from wearable tech like the Apple Watch. This reward takes the form of points, which then can be cashed in to reduce the cost of their plan.
This is a perfect demonstration of the way AI can translate to a better experience and financial savings for your customers, while also reducing claims and protecting your business model.
Much like the early days of the Internet, we’re just beginning to realize exactly what we can do with AI. As we adjust to this global pandemic and the aftershocks to come, including AI in your strategic planning strategy isn’t an option- it’s a necessity. And like the Apples and Microsofts of the Internet revolution, transitioning into the AI space early sets your organization apart as a technology trailblazer.
In this time of unprecedented change, putting data to smart uses has never been more important. Reach out to us today at email@example.com.
Part 1 of a 2 part series on AI for insurance and other high volume data businesses. To check out Part 2, please click here.