Machine Learning is Necessary for Today’s Insurers
Let’s imagine a fictional millennial named “Nick Loehanson.” He’s 29 years old (they grow up so fast), newly married and works as a project manager for a tech company. His life is typical for his generation – he’s been ordering his groceries, electronics, clothing and pretty much anything else you can think of online for years. He only goes to an actual store on vacations, because he’s ultra-busy. Nick almost always banks online and passively relies on his bank to offer him tailored and simple solutions for his banking needs.
When Nick wanted to take out homeowners and life insurance, he didn’t feel like he received optimal service. He was able to initially fill out a claim digitally, but then had to wait weeks for underwriting approval and then wait again for what seemed like an eternity to get paid out when his apartment flooded a year later.
It’s a New Generation
Insurance companies are facing a new generation of customers who have grown up in a world dominated by Facebook, Netflix and Amazon. They are used to instant, digital service and personalized offers, and they expect the same kind of unique experience from their insurance companies as they get from Google. In fact, almost a fifth of millennials say they would buy insurance from Google.
An IBM Institute for Business Value survey makes it clear that insureds around the globe aren’t happy:
- 32% of respondents switched insurers in the previous two years
- 41% of them switched their insurer due to slow reaction to their changing needs
- Only 37% of respondents said they fully trust their insurer
- Less than half (42%) of respondents said their insurer can be counted on to provide good service
Insurance companies, some of whom are still relying on antiquated legacy systems, are seeking to satisfy their customers’ high expectations by using new technologies to improve performance, while also constantly looking for new ways to lower costs and remain competitive.
Machine learning and cognitive computing are increasingly becoming attractive options for achieving these goals. What do these popular buzzwords really mean, though?
The main aim of machine learning is to enable computers to automatically learn without human intervention and without being explicitly programmed. To adjust actions, by learning and improving from experience.
Machine Learning is an application of AI, based on the idea that we should be able to provide machines access to data and let them “learn” for themselves. For example, Amazon uses machine learning algorithms to learn about its customers’ behavior patterns on the site and then offer suggestions about other products they may want to buy.
When exposed to new data, these computer programs can learn, develop and change without outside/human intervention. Machine learning allows computers to find insights without being programmed on where to look for a specific piece of information. Instead, insights are discovered using algorithms that iteratively learn from analyzing massive quantities of data.
Cognitive computing uses machine learning algorithms to make computers more user-friendly, with interfaces that are more focused on what users want. It uses self-learning systems that utilize data mining, pattern recognition and natural language processing to mimic the way the human brain works. Cognitive computing aims to create automated IT systems that can solve problems without human assistance.
It takes signals about what the user is trying to do and provides an appropriate response based on the user’s context, which it understands. Cognitive computing looks at the user’s location – at home, in the car, etc., and contextualizes the potential range of responses, making them more personalized.
For additional information, please stay tuned for our NEW white paper on this topic. It will examine the upcoming challenges, as well as some opportunities that will likely result, and how insurers can maximize those opportunities.Share this blog post