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
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.
More than 250 years ago, the challenge of making predictions from small data weighed strongly on presbyterian minister Reverend Bayes of Tunbridge Wells, England.
Looking to the easily banal Raffles of the 18th Century England he wondered what one's chances of winning them were. If five tickets out of ten bought won, then the chances of a win were quite simply 50%. But what if one bought a single ticket and it came out the winner? Were the chances of winning the Raffle really a 100%? It sounded far too simplistic to our dear Reverend who balanced scholarly and theological interests almost all his life. Ordained like his father and a man of keen intellect he was elected to the Royal Society in 1742.
In her article, “Decoding Consciousness”, which first appeared in the souvenir of Dr. Parnekar Life Management Foundation, Singapore, Dr. Sharda Bapat, Head of AI, Healthcare at AlgoAnalytics, explores and expounds her thoughts about machine learning and “artificial consciousness”. She envisions hope that the collective intelligence can be directed towards harmony and synchronized development of human intellect,physical and psychological realities. But cautious, that the challenge is to create appropriate technologies that will restore natural equilibrium. Read the full article here
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.