A numerical financial data quality monitoring system and method

Abstract

The invention discloses a numerical type financial data quality monitoring system and method. The system comprises a data reading configuration module to be monitored, a data reading and standardization module, a monitoring model configuration module, a data monitoring rule self-generation and update module, a data monitoring rule manual configuration module, a scheduling module, a data monitoringcalculation module, a message pushing module and a feedback module. The numerical type financial data quality monitoring system and method provided by the invention adopts machine learning to carry out self-learning on monitoring rules, and combines the manually set data quality monitoring rules with the machine self-learning monitoring rules, so as to improve the quality, efficiency and coverageof data quality monitoring, and reduce the human cost at the same time.

Description

technical field [0001] The invention relates to the field of data quality monitoring, in particular to a numerical financial data quality monitoring system and method. Background technique [0002] The integrity and correctness of data is an important basis for the service provided by intelligent investment consultants based on big data technology and artificial intelligence technology. If there is a problem with the integrity and correctness of the data, it will lead to errors in important modules such as intelligent investment strategies and customer investment advice, which may lead to the loss of customer investment. The traditional data monitoring mode requires developers and testers to formulate and code different data quality monitoring rules for different data. However, in the face of massive financial market data and customer data, this monitoring model will consume a lot of manpower and development. time. At the same time, the artificially formulated monitoring r...

AI Technical Summary

Problems solved by technology

If there is a problem with the integrity and correctness of the data, it will lead to errors in important modules such as intelligent investment strategies and customer investment advice, which may lead to loss of customer investment

The traditional data monitoring mode requires developers and testers to formulate and code different data quality monitoring rules for different data. However, in the face of massive financial market data and customer data, this monitoring model will consume a lot of manpower and development. time

At the same time, the artificially formulated monitoring rules will also have the disadvantages of unreasonable rule formulation, false positives, and high false alarm rates.

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