An index-type numerical financial time series data checking method

Abstract

An index-type numerical financial time-series data verification method, the index-type numerical financial time-series data verification method first reads and stores index-type numerical financial time-series data, that is, the data to be inspected; then judges the read The type of indicators in the data to be tested; for different types of indicators in the data to be tested, use the corresponding data inspection rules to perform inspection calculations; output the inspection calculation results. This application can automatically read and call the index-type numerical financial time series data to be tested, and perform multiple abnormality warning tests on the data, improve the efficiency of data review, expand the coverage of data review, reduce labor costs, and ensure data quality.

Description

technical field [0001] The invention belongs to the technical field of financial data management, and relates to an index type numerical financial time series data checking method. Background technique [0002] The index-type numerical financial time series refers to the sequence of the values ​​of one or several financial indicators arranged in the order of their occurrence time. It is one of the most commonly used data forms by various financial institutions and financial management departments. The original data quality management mainly relies on manual review, which has defects such as time-consuming and laborious, non-uniform review standards, and small review coverage. Traditional data quality management uses Excel and other tools to conduct audits by setting relevant formulas, but there are still shortcomings such as difficulties in data import, limited audit content, and difficult maintenance. At present, some data quality management is realized through self-develo...

AI Technical Summary

Problems solved by technology

The original data quality management mainly relies on manual review, which has defects such as time-consuming and labor-intensive, non-uniform review standards, and small review coverage

Traditional data quality management uses Excel and other tools to conduct audits by setting relevant formulas, but there are still shortcomings such as difficulties in data import, limited audit content, and difficult maintenance

At present, some data quality management is realized through self-developed systems, but there are deficiencies in the flexibility of verification rules, dynamic adaptability, richness of verification content, and readability of output results.

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