Intelligent Insurance Broker

Abstract

When considering the insurance industry, it is built on the collection of three major parties. Those are customers, insurance brokers and insurance companies. In here insurance brokers act as a vital role and they build a connection in-between customers and insurance companies. So their role is much important to provide successful services from insurance companies to customers. If we talk about the current situation of this process, most of the time insurance companies are unable to provide best insurance policy to their customer. Because they have not clear idea about customer requirements and still do not conduct much analysis to check the relationship in-between customer requirements and corresponding insurance policies. So the objective of this project is to implement an improved insurance brokering system by better matching insurance policies with the customer requirements. When considering the objective of the project we identified there are two ways to achieve our project goal. Those are data mining process and extract rules from expertise process. According to the timeline we selected extract rules from expertise process as a best way to accomplish our project goal. We also used weka data classification tool to do the analysis part. This thesis basically contains four major parts. Introduction section describes the problem, proposed solution and scope of the project. Related research projects are described in the background section. Design chapter is used to describe overall architecture and data analysis process. All the implementations are described in implementation section. Here, we have also added results and evaluation section apart from other four sections and that presents test cases and evaluation of this project results.