Insurance and Machine Learning

Risk and Insurance are words that go in sync with each other- one cannot live without the other. The insurance industry has always been dependent on various factors, the most important being statistics to know their customer’s and the market demand. Machine Learning(ML) and Artificial Intelligence (AI) has brought in a lot change in the way Insurance strategies are now being structured.
Besides predicting risks, ML and AI have now helped the Insurance industry to monitor, analyze not just their own strategies at one given time period, but keep a constant look out for changes that affect their customers, thereby keeping their policies attractive and thus halting retention.
To get that competitive edge, Insurers need to engage with Machine Learning (ML) to improve not just their business strategies but also tailor the insurance purchases to customer’s unique needs to bring in customer satisfaction.Continue reading

The Role of a Data scientist

A Data Scientist today is in much demand. But what does it take to become a Data Scientist? The kind of skill set and abilities required are not very different from what say a computer engineer would do- coding, programming, analyzing etc. However, a major winner is being ruthlessly analytical and data driven.

Learning and researching about the new Machine Learning models “out there in the market” makes the work of a Data Scientist different from the traditional software developer.  One needs to understand the problems of the clients, research on which model/s to be employed, research, and design and tweak Machine Learning models.

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Text Analytics: Taking search to the next level

Can Text Analytics really help reduce manual overload? Let’s take a look back when we started sharing interesting links, bookmarks, etc. During the early 2000’ there were many “tagging sites” that emerged. Tagging helped in collating our searched links online rather than just in a browser and also helped share the saved links with a single tag. It was soon realized that the one word tagged for different people meant different things and thus context and meaning to that tag was required and with that relevant sub-tags became a part of the search criteria.

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