By: Emma Sheard
Insurance Nexus, a division of FC Business Intelligence, recently spoke with both George Argesanu and Monika Schulze in the lead up to the Insurance Analytics EU Summit. We discussed how machine learning is currently being adopted, the next logical steps for insurance, and the hurdles that must be overcome for the successful implementation of this type of AI.
Monika Schulze, Global Head of Marketing at Zurich Insurance sees the opportunity for machine learning to revolutionise the insurance industry: "The old way of working can be modernised and be made more efficient, but it’s also possible to find new products and services. How do we get from paying out when something happens to helping customers predict when and how something might happen?"
The world itself is changing, and the insurance industry is undergoing dramatic disruption in the form of autonomous cars, the Internet of Things, wearables, sensors and the quantified self. Insurance companies must be innovative in their product development and service provision to meet the ever-changing needs of consumers.
George Argesanu, Global Head of Advanced Analytics, Personal Insurance at AIG adds that the "one thing I am the most excited about is the dynamic aspect. With telematics, machine learning will enable us to ‘see’ and hopefully prevent an accident before it happens by recognizing the patterns in the driving behaviour, traffic and road conditions. It is like Minority Report but with the precogs replaced by machine learning and AI, and much sooner than 2054."
Whilst machine learning is a natural partner for the growing capability born from these new technologies, it also has the power to transform the traditional operations of insurance.
Argesanu illustrates the scope of the opportunity: "I think there is tremendous potential for our industry to use machine learning to do things faster and smarter. There’s not going to be a big bang followed by a new order of the universe, but slowly and surely, we are getting to a more accurate pricing of risk."
Schulze suggests that "[for] the insurance business as a whole, one of the focal points is fraud mitigation. That’s where I see insurance applying machine learning to improve the P&L -then claims management which is also very important. It is a much faster process and it is easier to reduce errors by using machine learning to process large amounts of data."
Whilst there are many exciting opportunities within machine learning, insurance companies must be pragmatic when developing a plan for its implementation. They must balance a variety of considerations, and target the areas where machine learning represents the biggest value.