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Calculation method and device for feature data of lending user in financial scene

A technology of user characteristics and user data, applied in the field of big data, can solve the problems that the characteristics cannot be counted relatively completely, manual processing is time-consuming and laborious, and achieve the effect of reducing labor and time costs and reducing financial risks

Inactive Publication Date: 2021-01-26
北京泛钛客科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, risk control in the field of Internet finance usually requires risk control data experts to manually analyze, design and clean to generate characteristic variables, but manual processing is time-consuming and laborious, and manual experience may also prevent the characteristics from being statistically complete

Method used

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  • Calculation method and device for feature data of lending user in financial scene
  • Calculation method and device for feature data of lending user in financial scene
  • Calculation method and device for feature data of lending user in financial scene

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Embodiment Construction

[0036] It should be noted that, in the case of no conflict, the implementation modes in the present application and the features in each implementation mode can be combined with each other.

[0037] Hereinafter, the present application will be described in detail with reference to the accompanying drawings and in combination with embodiments.

[0038] This application is aimed at financial scenarios (such as banks) to calculate the feature data of deposit and loan users (such as users with deposit and loan time series records), and is divided into four parts, including data initialization, field expansion, data grouping, and feature calculation. Each part is independent of each other and interrelated. The modular structure is flexible, which facilitates the combination of modules, and has high reusability and maintainability.

[0039] The calculation method of the deposit and loan user characteristic data provided by this application is as follows: figure 1 shown, including: ...

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Abstract

The invention discloses a calculation method and device for feature data of a lending user in a financial scene. The method comprises the steps of grouping debit and credit user data according to a debit and credit user identifier to form a first data block; performing field expansion on the first data block to form an expanded first data block; grouping the expanded first data blocks according tothe state of the deposit and loan user and / or the behavior occurrence time of the deposit and loan user to obtain second data blocks; and calculating deposit and loan user characteristic data according to the second data block and the expanded first data block, wherein the debit and credit user data comprises a debit and credit user identifier, a debit and credit user state, debit and credit userbehavior occurrence time and data related to debit and credit user behaviors. According to the technical scheme, high-efficiency and comprehensive deposit and loan user feature data can be generatedthrough engineering.

Description

technical field [0001] This application relates to the field of big data, and in particular to a method and device for calculating characteristic data of deposit and loan users in financial scenarios. Background technique [0002] In the financial field (such as banking, insurance, trust, etc.), risk control is a very important task in the financial system. Risk control requires feature extraction from a large amount of raw data. With the development of Internet finance, risk control becomes more and more important. At present, risk control in the field of Internet finance usually requires risk control data experts to manually analyze, design and clean to generate characteristic variables, but manual processing is time-consuming and laborious, and manual experience may also make the characteristics unable to be statistically complete. Therefore, it is necessary to use big data analysis to complete the feature extraction work, especially to process the feature data of depos...

Claims

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Application Information

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IPC IPC(8): G06Q40/02G06Q10/10G06N20/00
CPCG06Q10/10G06N20/00G06Q40/03
Inventor 崔润邦未伟吴航宇邓江贾宁亢延哲
Owner 北京泛钛客科技有限公司
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