A user purchase prediction method in a big data financial scene

Abstract

The invention belongs to the field of user purchase prediction in a financial scene. The invention particularly relates to a user purchase prediction method in a big data financial scene. The method comprises the steps that in a big data financial scene, historical consumption behavior data of a financial platform APP are preprocessed, a data set is divided, feature engineering is carried out, andan algorithm model is constructed to predict whether a client can purchase coupons on a credit card platform APP or not in the future ten days; according to the invention, model integration for improving feature correlation is utilized; whether a customer purchases coupons on a credit card platform APP or not in ten days in the future is accurately predicted, the credit card can be helped to actively capture user value information and consumption requirements through data accumulation and data driving while services and scenes are continuously expanded, the data value is brought into play, and more accurate services are provided for the user.

Description

technical field [0001] The present invention relates to the field of user purchase prediction in a financial scenario, in particular, to a user purchase prediction method in a big data financial scenario. Background technique [0002] User purchase prediction has always been an important and hot research field. How to accurately predict the future purchase behavior of users is of great significance for companies to capture user value information and consumer demand. [0003] On the one hand, under the current existing technology, how to predict whether users will buy products in financial scenarios has not yet proposed an effective solution. Most of the existing technologies include user mobile purchase prediction, high potential user mining, user reputation prediction, etc. However, with the rapid development of big data, user purchase prediction based on the financial background is very important, which can promote the continuous expansion of business and scenarios in fina...

AI Technical Summary

Problems solved by technology

[0003] On the one hand, under the current existing technology, how to predict whether users will buy products in financial scenarios has not yet proposed an effective solution. Most of the existing technologies include user mobile purchase prediction, high potential user mining, user reputation prediction, etc. However, with the rapid development of big data, user purchase prediction based on the financial background is very important, which can promote the continuous expansion of business and scenarios in financial credit cards, and jump out of the traditional business model;

[0004] On the other hand, the forecasting degree of existing purchase forecasting methods is not accurate enough, and further improvement is needed

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