Why have insurers been so slow to embrace machine learning when organizations in other sectors are busy collaborating with machines, and applying artificial intelligence, to create new business models, reduce risk, improve efficiency and drive new competitive advantages?
Essentially, machine learning refers to a set of algorithms that use historical data to predict current or future outcomes. For example, banks use machine learning algorithms to monitor for fraud or irregular activity on credit cards.
Today, machine learning has become a hot topic in many sectors fueled, in large part, by the increasing availability of data and low cost, scalable cloud computing, the availability of masses of unstructured data, and the ongoing drive for operational efficiency and cost management.
For the insurance sector, we see machine learning as a fundamental game-changer since most insurance companies today are focused on three main objectives: improving compliance, improving cost structures and improving competitiveness. Machine learning can form at least part of the answer to all three.
Improving compliance: Today’s machine learning algorithms, techniques and technologies can be used to review, analyze and assess information in pictures, videos and voice conversations. One immediate benefit, for example, is the ability to better monitor and understand interactions between customers and sales agents in order to improve controls over mis-selling of products.
Improving cost structures: With a significant portion of an insurer’s cost structure devoted to human resources, any shift towards automation should deliver significant cost savings. Using machine learning insurers could cut their claims processing time down from a number of months to just a matter of minutes.
Improving competitiveness: While reduced cost structures and improved efficiency can lead to competitive advantage, there are many other ways that machine learning can give insurers the competitive edge, including product, service and process innovation.
Insurers have been slow to adopt machine learning in large part due to a culture of not being ‘early adopters’ of new technologies and approaches. This risk-averse culture also dampens the organization’s willingness to experiment and fail in its quest to uncover new approaches.
Insurance organizations also suffer from a cultural challenge common in information-intensive sectors: data hoarding. Fortunately, many companies are now keenly focused on moving towards a ‘data-driven’ culture that rewards information sharing and collaboration and discourages hoarding.
The first thing insurers should realize is that this is not an arms race. The winners will be the ones that take a measured and scientific approach to building up their machine learning capabilities and capacities and – over time – find new ways to incorporate machine learning into ever-more aspects of their business.
Insurers may want to start small. Our experience and research suggest that – given the cultural and risk challenges facing the insurance sector – insurers will want to start by developing a ‘proof of concept’ model that can safely be tested and adapted in a risk-free environment.
Recognizing that machines excel at routine tasks and that algorithms learn over time, insurers will want to focus their early ‘proof of concept’ efforts on those processes or assessments that are widely understood and add low value. The more decisions the machine makes and the more data it analyzes, the more prepared it will be to take on more complex tasks and decisions. Later, business leaders can start to think about developing the business case for industrialization, along with appropriate governance, monitoring and system management.
At KPMG, we have worked with a number of insurers to develop their ‘proof of concept’ machine learning strategies over the past year. We can say with absolute certainty that the battle of machines in the insurance sector has already started, and those that remain on the sidelines will suffer as they stand by and watch competitors find new ways to harness machines to drive increased efficiency and value.