Using machine learning and cognitive computing (which I defined in my previous blog post) is an efficient and advanced way for organizations across verticals to improve processes and save time, manpower and ultimately, money. With traditional computing, processes are programmed and operated based on sets of rules. Cognitive systems learn continuously and are not dependent on rules and programming. Machine learning takes vast quantities of data – millions and millions of pages of structured, searchable, straight-forward data stored in databases; and unstructured data, such as photos or audio files; and turns them into actionable insights. These insights and recommendations result in new offerings and services, to increase customer engagement and satisfaction.

Machine learning is already up and running in a variety of ways in the banking and financial sectors, healthcare, retail, publishing and social media, robot locomotion, gaming and more. Google, Facebook and others benefit from pushing relevant advertisements based on users’ past search behavior. Machine learning can also handle multi-dimensional and multi-variety data in dynamic or uncertain environments. It offers tools for continuous quality improvement in large and complex process environments.

Shaking Up the Insurance Industry

Today’s insurance companies face external pressure from consumers and internal pressure to lower costs, without delaying payouts. The solution is to adopt the most advanced technologies to keep themselves ahead of their competitors and delight their customers.

Actuaries and statisticians have used historical data to recognize patterns in claims and predict future losses for over 100 years…The level of sophistication and tools has changed over time and I look at machine learning and AI as transformative for the way we try to solve the same problems, while also gaining insights from places where traditional methods fail.

– George Argesanu, Global Head of Advanced Analytics, Personal Insurance, AIG.

Disruption is in the air. Cognitive computing via machine learning is clearly the next logical step toward developing the kind of products and services that will enable insurance companies to stay relevant and financially viable in the twenty-first century.

There is growing potential for use of machine learning to boost the insurance industry in many areas, particularly customer care and improved service, or efficiency and cost-cutting. When machine learning executes in place of a human, the process generates significantly fewer errors and is mind-bendingly faster than what any human is capable of.

For example, IBM’s Watson™ can read 800 million pages per second. Watson is IBM’s cognitive system that can “think” or “reason” like the human brain: processing information, drawing conclusions and learning from its experiences.

For additional information, please check out my NEW white paper: Learning about Machine Learning. It examines the upcoming challenges, as well as some opportunities that will likely result, and how insurers can maximize those opportunities.

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