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Bank risk analysis library platform based on big data, building method and readable medium

A risk analysis and big data technology, applied in the field of bank risk analysis library platform based on big data, can solve problems such as inability to operate, low data quality, complex table structure, etc., to reduce ground risks, improve data quality, shield effect of change

Inactive Publication Date: 2020-10-20
睿智合创(北京)科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The problem of solution 1 is that the timeliness and processing efficiency of data acquisition are insufficient, there are many docking source systems, the table structure is complex, and the data quality is not high, which makes it difficult to meet the needs of real-time risk analysis and complex correlation analysis
The problem in the second solution is to promote the platform construction from a technical perspective, lack of long-term considerations when building applications, lack of data management and control, data mining and modeling and analysis capabilities, and build an "advanced" platform that cannot operate

Method used

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  • Bank risk analysis library platform based on big data, building method and readable medium
  • Bank risk analysis library platform based on big data, building method and readable medium
  • Bank risk analysis library platform based on big data, building method and readable medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0039] This embodiment discloses as figure 1 A method for building a bank risk analysis library platform based on big data is shown, and the method includes the following steps:

[0040] S1 Sort out the database table structure of the source system, and design the data application scheme based on the existing data;

[0041] S2 Design the derived layer based on the application requirements, and establish a standard layer based on the application requirements of the derived layer, so as to realize the connection from the source system to the derived layer;

[0042] S3 sorts out the relational mapping table and the processing logic of the derived layer;

[0043] S4 generates data table creation sql, data mapping sql and index processing sql through the metadata management module;

[0044] S5 generates data verification rules sql through metadata classification and modeling, blood relationship and impact analysis, performs rule verification on necessary fields, and performs data...

Embodiment 2

[0055] This embodiment discloses a bank risk analysis library platform based on big data, including a file storage area, a temporary data area, a post source data area, a theme data area, an application data area and an archive data area. image 3 It is a data flow diagram, and the specific description is as follows:

[0056] (1) From the source system to the file storage area: perform batch data synchronization, and the source system will periodically unload the newly added or time-point data to the file storage area on the file server;

[0057] Data acquisition, including real-time and batch methods. The real-time method is for ETL tools to obtain incremental data through the database data exchange component, perform standardized processing and post source integration, and store it in the post source data area; the batch method is for the source system to regularly uninstall new or point-in-time data for file storage on the file server In the area, the data files on the fil...

Embodiment 3

[0065] This embodiment discloses as image 3 The construction method shown mainly includes the following steps:

[0066] (1) Sort out the database table structure of the source system, and clarify the range, frequency and method of data that can be collected. Currently, the range of data that generally needs to be collected is as follows:

[0067]

[0068]

[0069] (2) Design data application schemes based on existing data, including data query, reporting, correlation analysis and data mining, model verification and optimization, risk warning, etc., and design derivative layers based on application requirements.

[0070] (3) Based on the application requirements of the derived layer, establish a standard layer to realize the connection from the source system to the derived layer. The general standard layer construction table is as follows:

[0071]

[0072]

[0073] (4) Sorting out the relational mapping table: By establishing a data mapping relational table and ...

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Abstract

The invention relates to the technical field of financial risk control, in particular to a bank risk analysis library platform based on big data, a building method and a readable medium. According tothe invention, a big data platform is used as support, an ETL tool, metadata management and a data management and control technology are introduced to model underlying data, therefore, a powerful andstable data analysis system is quickly established, the convenience and timeliness of data acquisition are improved, and the data quality is improved, so that the requirements of data modeling and analysis are met, fraud risks and credit risks are prevented, the data value is converted into productivity in business application, an effective closed loop of data value utilization is constructed, andthe conversion from data support to data application is truly realized. The big data technology is introduced, real-time data processing and calculation are supported, the processing time is shortened by 50% or above compared with a traditional database mode, the data quality is improved, and modification of an application layer caused when a new system is put online is shielded.

Description

technical field [0001] The invention relates to the technical field of financial risk control, in particular to a bank risk analysis library platform based on big data, a construction method and a readable medium. Background technique [0002] With the rapid development of Internet business and the advent of the big data era, traditional banks have put forward new requirements for data collection, storage, application, analysis, management and control, and capacity expansion. At the same time, a large amount of data has been accumulated within the bank, and the types of data are increasingly rich and the scale of data is expanding. Although the data warehouse system has been developed in commercial banks for more than ten years, due to the weak processing capacity of a single node, it is very costly to improve the hardware capacity through Scale-up vertical expansion, and the performance improvement is limited. After completing the data statistical analysis work, it is also...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q40/02G06F16/182G06F16/16G06F16/14
CPCG06Q40/02G06F16/14G06F16/16G06F16/182
Inventor 王玉海曾杰张志宏陈绍佳
Owner 睿智合创(北京)科技有限公司