Financial service risk prediction method and device

A risk prediction and financial technology, applied in the field of data processing, can solve problems such as unstable machine learning models, complex calculation processes, and no consideration of dependent variables

CN111815432AActive Publication Date: 2020-10-23INDUSTRIAL AND COMMERCIAL BANK OF CHINA
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2020-10-23

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Abstract

The embodiment of the invention provides a financial service risk prediction method and device. The method comprises the steps of receiving a financial service risk prediction request for a target user; selecting one of the plurality of financial service risk prediction models as a target financial service risk prediction model based on the financial service request type, wherein the financial service risk prediction model is obtained by training by using respective corresponding training sets, and the training sets are target data sets obtained by performing data binning on historical user financial information of multiple users by using a Spark system; and inputting the user financial information into the target financial service risk prediction model, and taking output as a financial service risk prediction result of the target user. The reliability, efficiency and automation degree of the binning process of the training data can be effectively improved, and then the accuracy, efficiency and automation degree of financial service risk prediction of financial institution users by applying the financial service risk prediction model obtained by binning data training can be effectively improved.
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Description

technical field

[0001] This application relates to the technical field of data processing, in particular to a financial service risk prediction method and device. Background technique

[0002] As financial institutions such as banks provide more and more types of financial services to the public, and financial services target more and more target groups, financial institutions need to provide financial services to users before providing certain financial services. Pre-judgment of possible risks in the service. At present, the way financial institutions conduct financial service risk prediction usually uses machine learning models for automatic prediction, but the machine learning models currently used by financial institutions usually require a large amount of historical data for training, and it takes a lot of time to apply these historical data. manpower for data mining and processing. However, the method of data processing by manpower is time-consuming and labor-intensi...

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Embodiment Construction

[0086] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments It is a part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

[0087] Data binning (also known as discrete binning) is a data preprocessing technique and an important data processing operation in data mining feature engineering. It is used to reduce the impact of minor observation errors, improve model stability, and reduce the risk of model overfitting. It is a method of grouping multiple continuous values ​​into a small...