Big data continues to change the insurance landscape. Using analytics and predictive modeling, data savvy insurers are gaining new insights into their policyholders, loss ratios and overall profitability. Then there are those insurers that gave gathered so much information in the age of big data that they’re offering innovative products like usage-based insurance – insurance based on actual activity. But according to Information Management, insurers won’t be stopping there.
The next step, according to Information Management, is insurers will leverage machine learning to dig even deeper into current policy holders and potential customers. So, what exactly is machine learning and how does it work?
Machine learning is an algorithm that can learn from data without relying on rules-based programming. It is a subset of computer science and artificial intelligence that explores the study and construction of algorithms, instead of programmed instructions in order to make predictions on data. At a very simple level, machine learning is like teaching computers to learn the way humans do – by examining data or information, understanding what that data is telling us and then classifying and learning from the successes or failures that have been presented.
Machine learning algorithms can predict data on the fly and are capable of learning from trillions of observations, one by one. It is best used with data that has a high number of attributes and a large number of observations.
And with so much data being generated every day, from smartphones to IoT, it’s no secret that computers have plenty of information to examine, understand, classify and learn from. The human brain cannot process all the data that is available today, but with lightning speed and foolproof binary logic, a computer certainly can.
The easiest way to put it is that machine learning is the science of getting computers to act without being explicitly programmed, meaning there is no human interaction whatsoever.
It’s easy to see why insurers would want to leverage machine learning, but how can they?
To leverage machine learning, insurers need an all-inclusive, big data analytics platform. The platform should be built to handle not one, but rather many steps from data acquisition and preparation to data analysis and operationalization, in real-time. The platform should also be able to dissect and analyze vast amounts of data – not just some.
The platform should apply machine-learning algorithms for segmentation, classification, recommendation, regression and forecasting. Insurers should then be able to create reports and dashboards to visualize the results and collaborate across the organization.
By combining copious amounts and variety of data, machine learning can provide insurers with data that is uniquely tailored around each policyholder. For example the platform can predict every policy holder’s policy life expectancy, likelihood to renew, likelihood to file a claim, etc. Since policy holders are not static, different combinations of analytics are constantly updated to incorporate their changing circumstances as well as changes in external and market conditions.
To learn more about machine learning and advanced analytics, download the data sheet below.
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