With the artificial intelligence (AI) technology that exists today, an AI-enabled single view of the customer provides insurers with an opportunity to improve the customer experience. In the previous blog article “The Importance of Having a Single View of Your Customer in 2019,” we discussed a few of ways this can be accomplished.
When William Shakespeare wrote “To be or not to be” for Prince Hamlet to speak and express his contemplation for embracing the universal truth; little did he know that he would be quoted in various different contexts for different types of effects. A coward soldier saying; “to flee or not to flee”; a conniving trader evaluating an unsuspecting customer; “to fleece or not to fleece”; the colonial masters strategizing their exit; “to free or not to free”. And as guessed by you; a data scientist upon stumbling on a couple of interesting variables; “to correlate or not to correlate”.
The first step in predictive modeling is defining the problem. Once done, historical data is identified, and the analytics team can now begin the actual work of model development. In this blog, we touch on the business factors that influence model development. If you find this interesting and want a deeper dive, you’ll have the opportunity to download our whitepaper that goes into more detail on this topic.
An interview with Jackie Vergne, Director of Customer Success
Most insurers’ view of their policyholders is in isolation - one policyholder at a time, with the possibility that more than one individual in a household may have different or multiple policies from the same insurer. As such, the premium impact of the household is larger than that of the individuals.
Today, customers expect a personalized, unique experience. Millennials not only expect a superior experience but also expect their service provider to know in advance about the kind of treatment they prefer to receive. A critical step in delivering a unique experience is to know what your existing customers think about you and your services.
Virtual assistants like Siri, Cortana and Alexa as well as other speech synthesis techniques have solved many customer use cases by offloading repetitive and mundane searches or activities. Customer-oriented businesses leverage this technique to provide better operational efficiency and improve customer experience. They can then run analytics over the voice/audio content to derive predictions.
If a life insurer wants to build a predictive model, how should they go about it? In this article, we explore the factors that need to be considered before beginning actual model development. We will do this by using the example of predictive models for improving persistency. (Improving persistency for a life insurer means increasing the volume of business they retain.)
Companies design application processes to provide the best possible experience for their customers. These processes rely on application and customer-originated events to function. These events and their outcome form the basis of the customer’s experience. Therefore, event-driven philosophy is an ideal way for companies to measure customer experience.
I recently saw a tweet from Mat Velloso - “If it is written in Python, it’s probably machine learning. If it is written in PowerPoint, it’s probably AI.” This quote is probably the most accurate summarization of what has happened in AI over the past couple of years. A few months back, The Economist shared the chart below that shows the number of CEOs who mentioned AI in their Earnings calls. Towards the end of 2017, even Vladimir Putin said: “The nation that leads in AI ‘will be the ruler of the world.” Beyond all this hype, there is a lot of real technology that is being built. So how is 2019 going to look for all of us in the insurance world?