Will the next round of competition in the insurance sector be fought—and won—by machine learning? It would seem so, with a handful of your peers already starting to arm themselves with the skills, capabilities and technologies to start winning the early battles. Are you ready to compete in this new environment?
Insurance executives can be excused for having ignored the potential of machine learning until today. Truth be told, the idea almost seems like something out of a 1980s sci-fi movie: Computers learn from mankind’s mistakes and adapt to become smarter, more efficient and more predictable than their human creators.
But this is no Isaac Asimov yarn; machine learning is a reality. And many organizations around the world are already taking full advantage of their machines to create new business models, reduce risk, dramatically improve efficiency and drive new competitive advantages. The big question is why insurers have been so slow to start collaborating with the machines.
Smart machines
Essentially, machine learning refers to a set of algorithms that use historical data to predict current or future outcomes. Most of us use machine-learning processes every day. Spam filters, for example, use historical data to decide whether e-mails should be delivered or quarantined. Banks use machine-learning algorithms to monitor for fraud or irregular activity on credit cards. Netflix uses machine-learning to serve up recommendations to users based on their viewing history and recommendations.
In fact, organizations and academics have been working away at defining, designing and improving machine-learning models and approaches for decades. The concept was originally floated back in the 1950s but—with no access to digitized historical data and few commercial applications immediately evident—much of the development of machine learning was largely left to academics and technology geeks. For decades, few business leaders gave the idea
much thought.
Machine learning brings with it a whole new vocabulary. Terms, such as feature engineering, dimensionality reduction, supervised and unsupervised learning, to name a few. As with all new movements, the ability of an organization to bridge the two worlds of data science-led machine learning and business is where the value will be generated.
Driven by data
Much has changed. Today, machine learning has become a hot topic in many business sectors fueled, in large part, by the increasing availability of data and low-cost scalable cloud computing. For the past decade or so, businesses and organizations have been feverishly digitizing their data and records—building up mountains of historical data on customers, transactions, products and channels. And now they are setting their minds toward putting it to good use.
The emergence of big data has also done much to propel machine learning up the business agenda. Indeed, the availability of masses of unstructured data—everything from weather readings through to social-media posts—has not only provided new data for organizations to comb through, it has also allowed businesses to start asking different questions from different data sets in order to achieve
differentiated insights.
The ongoing drive for operational efficiency and improved cost management has also catalyzed renewed interest in machine learning. Organizations of all types and stripes are looking for opportunities to be more productive, more innovative and more efficient than
their competitors.
Many now wonder whether machine learning can do for information-intensive industries what automation did for manual-intensive ones.
A new playing field
For the insurance sector, we see machine learning as a fundamental game-changer. The reality is that most insurance organizations today are focused on three main objectives: improving compliance, improving cost structures and improving competitiveness. It is not difficult to envision how machine learning will form (at least part of) the answer to all three.
Improving compliance: Today’s machine-learning algorithms, techniques and technologies can be used on much more than just hard data, like facts and figures. They can also be used to review, analyze and assess information in pictures, videos and voice conversations. Insurers could, for example, use machine-learning algorithms to better monitor and understand interactions between customers and sales agents in order to improve their controls over the misspelling of products.
Improving cost structures: With a significant portion of an insurer’s cost structure devoted to human resources, any shift toward automation should deliver significant cost savings. Our experience working with insurers suggests that—by using machines instead of humans—insurers could cut their claims processing time down from a number of months to just a matter of minutes. What is more, machine learning is often more accurate than humans—meaning that insurers could also cut down the number of denials that result in appeals they may ultimately need to pay out.
Improving competitiveness: While reduced cost structures and improved efficiency can certainly lead to competitive advantage, there are many other ways that machine learning can give insurers the competitive edge. Many insurance customers, for example, may be willing to pay a premium for a product that guarantees frictionless claim payout without the hassle of having to make a call to the claims team. Others may find that they can enhance customer loyalty by simplifying reenrollment processes and client onboarding processes to just a handful of questions.
All hail the machines!
AT KPMG, we have worked with a number of insurers to develop their “proof of concept” machine-learning strategies over the past year. And we can say with absolute certainty that the battle of machines in the insurance sector has already started. The only other certainty is that those that remain on the sidelines will likely suffer the most as they stand by and watch their competitors find new ways to harness machines to drive increasing levels of efficiency and value.
The bottom line is that the machines have arrived. Insurance executives should be welcoming them with open arms.
The article was taken from KPMG’s publication, entitled Frontiers in Finance: For decision-makers in financial services, written by Gary Richardson of KPMG in the UK.
R.G. Manabat & Co., a Philippine partnership and a member-firm of the KPMG network of independent firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved.
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