Insurance Analytics: Application of analytics / machine learning techniques in insurance industry

Learn : Application of analytics in the insurance industry. Agenda will be
-Customer lifecycle in insurance business
-Fundamental of insurance business
-Application of various analytics techniques along with the customer life cycle
1. Use of predictive analytics / supervised machine learning algorithms
2. Use of collaborative filtering etc.
Take a look at my other publications
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[@PersistentPureheart]

This was absolutely essential and very valuable. All concepts were properly curated and presented. Thank you so much. If you could also do the same with other domains such as capital markets, derivatives, global banking, etc. that would be beneficial for so many IT aspirants as well as those preparing for interviews for a change in domain and perspective. Once again, thank you!

[@vetribull8318]

Thanks a lot bro

[@Karma_would_return]

real domain knowledge and IT integration explained the analytical really well.
It made me your committed subscriber 👍 and would refer it each time When I get stuck....

[@ankursharma3135]

A very nice approach

[@prateekbharti6635]

Thank you

[@shaikbanu2308]

Very helpfull video..

[@klal1085]

Great presentation, there are very few videos which explains real business cases of analytics, kudos to you Gopal!!

[@theREstd]

Hello Sir, nice video but I am not sure how will you classify good performing agents in the first step?
What will be the dependent variable? Let's say based on the historical data we created a feature saying performance where it has 3 categories good,average,bad and we try to classify agents based on this feature. But how can we determine what is good and bad what are the parameters to be considered while creating this feature? Thanks again for the video and it will be of a great help for me if you can solve my query.

[@gopalprasadmalakar12]

Sravan, It is rather simple. Those agents who are performing (in other words selling sufficient insurance product) as per expectations are good. Those who are near boundry line of targets are average and you have poor performers, who are very far from target (at times even at zero). Usually you develop model by taking just good and bad and keep average out of model building exercise (very much the way you keep intermediate out of modeling exercise). I hope it helps. Regards

[@bhuvaneshsampat]

Excellent presentation....

[@karthikg108]

Thank you, sir.