Bank outlet site selection recommendation method based on multi-source data driving

Abstract

The invention relates to the field of data mining and big data application, in particular to a bank outlet site selection recommendation method based on multi-source data driving, which comprises thefollowing steps of: obtaining mobile signaling data and bank outlet peripheral data which comprise local bus data and network crawling data; Extracting five characteristics of visitor flow, user value, traffic jam index, number of surrounding competition websites and average visitor income from the target bank website according to the acquired data; Utilizing the constructed features to train a plurality of site selection classification models, and constructing a bank outlet site selection recommendation model based on probability-based multi-model fusion; And by using the constructed bank outlet site selection recommendation model, recommending the optimal bank outlet site selection distribution of the area according to the characteristics of the target area, so that the purpose of intelligent site selection of the bank outlet is achieved, the problems of inaccurate site selection range and high labor cost caused by traditional site selection are solved, and better economic benefits and social benefits are obtained.

Description

technical field [0001] The invention relates to the fields of data mining and big data, in particular to a multi-source data-driven site selection and recommendation method for bank outlets. Background technique [0002] With the development of society and economy, the business demands of major commercial banks and the accumulation of users are increasing day by day. The location of bank outlets is directly related to the development and interests of banks. Therefore, the site selection of bank outlets is of great significance to the development and future planning of banks. [0003] The traditional bank site selection methods mainly include manual research, expert scoring, and consulting professional consulting companies. These methods first collect data on a large scale through manpower, including the flow of people within the location, the number of bank competition outlets, traffic flow, etc., and finally combine these data to display the results in a data visualization...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the deficiencies of the above problems, the present invention uses mobile signaling big data and surrounding data of bank outlets, which includes local bus data and network crawling data, to provide a bank outlet location recommendation method driven by multi-source data, which can effectively Solve the problems of long time and high research cost of traditional methods, realize the purpose of automatic bank location selection, and be fast, accurate and effective

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