How Machine Learning has affected the Insurance Industry

By Jose M.

Machine learning is a new type of “artificial intelligence” that gives computers an ability to learn, without being programmed. In our day in age, this is essential to all rapid technical advancements we are experiencing. One of the industries artificial intelligence is affecting is the insurance industry.

There can be two types of machine learning. One is supervised, and the other is unsupervised. The difference between these is whether there is user interacts with the process in the beginning or whether the machine is let freely put into use with data that is provided to find patterns and relationships. Supervised machine learning is “trained”, meaning it will be able to lead to an accurate conclusion based on data that is provided, while unsupervised machine learning will be given data, and will establish patterns and common occurrences in the data provided. (Mccrea, N)

The benefits of machine learning in the insurance industry is the ability to make a process faster, save money by doing so (in the aspects of research and finding opportunity costs), free humans from the automation process therefore improving their customer retention.

H2O.ai is a type of machine learning that helps the insurance industry. H2O is an open source machine learning platform, that helps predict churn, pricing, and fraud. It is used by various industries, one of which is the insurance industry. Amongst its users are Transamerica, Progressive, and Zurich North America. The predictive capabilities and machine intelligence make the software desirable and adaptable. (Noyes, K., 2016) One of the tools that H20 uses at Zurich is Hadoop, which works in correlation with the AI. It sets up the production and holds raw data until needed in a data lake. This helps pulling data feed and making them into sets to move forward into the second step. The second step being, the risk analysis models both testing and running. Hadoop, helps with the cluster of data allowing it to reach the analytical team and models much quicker than most traditional systems. (Vaughan, J., 2016)

Its application to businesses are the following: reduction of costs, improvement of efficiency, and the advantage gain against competition. The reduction of costs come from the shift from human resources to automation. It could cut down the processing time from months to days or even minutes. Competitive advantage is gained through creating a new innovative in the processes, service, and products (Reader, G 2015). Machine learning in this industry can also help prevent fraud, claims process will be more efficient, and losses will be caught and prevented furthermore than they are now. (Lloyd-Jones, T., 2016)

Some of the challenges machine learning faces in the insurance industry are, not enough research and development, a fear job loss. It compensates for it by using method of fusion of machine learning along with statistical and actuarial methods. Once research has been done through a fusion of methods, the data in the base of machine learning and data complexity of insurance have worked together, there can be a prediction resulting from the machine learning. The machine learning will get its predictions from the insurance data will exceed the predications current methods can do. (Leibensperger, M., 2010) Only the nonemotion parts/automated parts of an industry can machine learning help in, there was a challenge between having the system automated as possible but also having human interactions, but with software like H2O.ai, they say their goal is bring back the necessities of human interactions such as, love, emotions, and everything else that can’t be automated. The loss of jobs is one of the many challenges around AI because it is what may make some companies hesitant to adapting machine learning in their environment. Another challenge presented from AI, is training its users to effectively use output results to make the correct business decisions (Vaughan, J., 2016)

Overall with new technologies emerging, a business must adapt and use an AI in the near future so they can stay ahead or keep up with the competition around it. It is the new way of doing business in a new age. “Data will be a key factor that sets businesses apart competitively in the coming years, and success will depend increasingly on artificial intelligence and machine learning.”(Noyes,K., 2016 )

Reference Page
Leibensperger, M. (2010). Analytics 2.0. Canadian Underwriter, 77(12), 50-52. Retrieved
February 13, 2017.
Lloyd-Jones, T. (2016, December 09). Machine Learning and Artificial Intelligence in
Insurance. Retrieved February 13, 2017, from http://blogs.lexisnexis.com/insurance-insights/2016/06/machine-learning-artificial-intelligence-insurance/
Mccrea, N. (n.d.). An Introduction to Machine Learning Theory and Its Applications: A Visual
Tutorial with Examples. Retrieved February 12, 2017, from https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
Noyes, K. (2016, February 24). Artificial intelligence ‘frees us up to be humans again,’ H2O.ai
chief says. Retrieved February 13, 2017, from http://www.pcworld.com/article/3036767/application-development/ai-frees-us-up-to-be-humans-again-h2oai-chief-says.htm
Reader, G. (2015, July 22). How Machine Learning is changing the game for insurers. Retrieved
February 12, 2017, from https://home.kpmg.com/xx/en/home/insights/2015/07/how-machine-learning-fs.html
Vaughan, J. (2016). Machine learning tools pose educational challenges for users. Retrieved
February 13, 2017, from http://searchbusinessanalytics.techtarget.com/feature/Machine-learning-tools-pose-educational-challenges

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