A huge amount of data is generally referred as Big Data. It is
enormous in size, diverse variety and has highest velocity of data arrival.
This huge information is useless unless the data is examined to uncover the new
correlations, customer experiences and other useful information that can help
the organization to take more informed business decisions. Big data is widely
applied in all sectors like healthcare, insurance, finance and many more.
Big data in insurance
sector is one of the most promising. Traditional marketing system of insurance
is offline based sales business. They generally sell the insurance policies by
calling and visiting the customers. This fixed marketing system also achieved
good results in past time. But currently many new private insurances companies
also have entered into the marketplace which gives healthier competition. On
other hand, eagerness of people to pay for the insurance service is also
increased. Therefore, understanding the need and purchase plan of clients is
extremely essential for insurance companies to raise the sales volume.
Big data technology
supports the insurance companies’ transformations. Due to lack of principle and
innovation of traditional marketing, badly structured insurance data, unclear
customers purchasing characteristics leads to imbalanced data, which brings the
difficulty of classification of user and insurance product recommendation.
Decision making task is difficult with imbalanced data
distribution. To solve this problem, we usually use few resampling methods
which will construct the balanced training datasets. This will improve the
performance of predictive model.
Main purpose of this
paper is to identify the potential customer with help of big data technology.
This paper does not only provide good strategy for identifying the potential
client but also act as good reference for classification problems.
We propose supervised learning algorithm call ensemble
decision tree to analysis the potential customer and their major
This paper is organized as follows. Section II introduces the
current research status of machine learning; Section III puts forward the
classification model and intelligent recommendation algorithm based on XGBoost
algorithm for insurance business data, and analyzes its efficiency; Section IV gives
you experiment and result. Section V puts forward the analysis result. Section
VI Conclusion and future work.