Statistical models that are produced by Machine Learning are far more accurate. The machine considers all the underlying interactions in a way that a human could not manually construct. This is a well-documented benefit of the technique. As such, 57% of the surveyed companies cite greater analytical accuracy as the primary benefit being realized through the use of Machine Learning.
Most companies reported that the most significant promise and expected benefits of Machine Learning are greater automation, productivity and cost savings. Yet, only 12% of the surveyed companies are realizing this benefit today.
What’s standing in the way of the full potential?
There are barriers preventing many insurance firms from achieving the full potential of Machine Learning.
The biggest barrier is the knowledge gap.
Despite growing adoption, 82% of survey respondents described themselves as still beginners (29%) or having intermediate knowledge (52%) when it comes to Machine Learning.
Larger companies are further along in the learning curve, according to the survey, with 22% describing themselves as beginners versus 33% of smaller firms.
Another barrier to greater use of Machine Learning is the lack of an experienced hiring pool. It is extremely difficult to attract analytic talent to insurance companies.
One of the reasons for this is that very few universities are actually teaching courses relevant to Machine Learning, due to the newness of these techniques.
Machine Learning focuses on a series of statistical modeling techniques, many of which are geared toward predictive modeling. This modeling relies on computer programing to develop, test and refine algorithms with the least amount of human intervention.
While the algorithms themselves might not be very simple, the overall application of Machine Learning is relatively straight forward.
Earnix provides an advanced analytics platform designed for the financial services industry, which integrates real-time decision-making capabilities into the business process, delivering significant results. Earnix’s modeling, algorithms, and Machine Learning capabilities automate the rapid deployment of customer-centric offers by considering variables such as price, product features, and distribution channels, in order to optimize KPIs such as revenue, profit, sales volume, and customer satisfaction.
The company’s business consulting teams are comprised of analytics experts and data scientists with deep domain experience in financial services. They are trusted advisors who work closely with clients to define and develop models that are tailored to the client’s unique needs.