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Since past couple of years, Agents have been a crucial part of the financial sector, primarily focusing on the Auto Insurance sector, whose key responsibilities are centered around finding new prospective customers and maintaining a relationship with existing customers. But with every other company streamlining their business processes with the latest Technology, Insurance Industry is not too far behind. Currently, Insurance Industry has dived and started exploring the online space. Prospective customers can now get online insurance quotes, chat with an online robot and even purchase an Insurance policy online. Digitalization, Automation, and Streamlining are key buzzwords in every type of business sector. Given the above trends, Insurance Agents seem to be an unnecessary expense. In this paper, we propose an Artificial-Intelligence driven approach that eliminates the need for a human Insurance Agent that will ultimately reduce the overall cost for the end customer. As part of our contribution to the above problem statement, we have proposed a Software Application where four Statistical Models are deployed. These Models are tasked with determining prospective customers who will likely buy an Insurance Policy, identifying customers who are likely to cancel a policy so that we can provide them with something better, identifying customers submitting fraudulent insurance claims and finally a Recommendation System Model to recommend updates to current policy to existing policy of Customers. In our Experimentation Results, we identified a cluster of customers who were most likely to buy a product using an Unsupervised Statistical Machine Learning model.
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