Machine Learning Used by Over Half of Top Insurers Globally, Survey Shows

According to a global survey of insurance executives released in June 2017 by Earnix ^[1], a provider of analytics solutions for the financial services industry, there is wide adoption of Machine Learning by insurers across the globe, and expectation that machine learning will bring “significant” change to the industry over the next three to five years.

According to Wikipedia, machine learning gives “computers the ability to learn without being explicitly programmed.” Machine learning algorithms can learn from and make predictions on data, through building a model from sample inputs.

Over half (54%) of the almost 200 insurance executives surveyed said that their organization was using machine learning for predictive analytical modeling. Of those deploying the technology, 70% said they were using it for risk modeling; followed by demand models (45%) and fraud detection (36%).
Over half of the respondents (57%) said that Machine Learning has made their analytical models far more accurate, which has led to better risk assessments, and ultimately better decisions.
The survey found that the main barrier to wider adoption is a lack of knowledge and expertise within organizations. Eighty-two percent say their organization is relatively inexperienced with Machine Learning.

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As a caveat, according to U.S. research and advisory firm providing information technology related insights, Gartner, as of 2016 machine learning in general had become a buzzword that is at its peak of inflated expectations ^[2]. This is because finding patterns is hard; often not enough training data is available; and also because of the high expectations it often fails to deliver.

However, the insurance industry has a long and successful track record in collecting, cleansing, analyzing, and acting on data. That high-quality data can be used for training models. Furthermore, the industry has experienced statisticians (also known as actuaries) who can check the models for validity and executives who are comfortable relying on data and statistics for decision-making. Therefore, in our industry perhaps more than in any other, machine learning may be an useful enabling technology for discovering new patterns in existing data and/or accelerating the refinement of existing analytical models.

Download the survey report here “Machine Learning – Growing, Promising, Challenging ^[3]“.