Index type numerical value type financial time series data checking method

Abstract

The invention discloses an index type numerical value type financial time series data checking method. The index type numerical value type financial time series data checking method comprises the following steps: firstly, reading and storing index type numerical value type financial time series data, namely to-be-checked data; then, judging the index type in the read to-be-inspected data; for different types of indexes in the to-be-inspected data, performing inspection calculation by using corresponding data inspection rules; and outputting a test calculation result. According to the invention, the to-be-checked index type numerical financial time series data can be automatically read and called, and multiple abnormal early warning checking is carried out on the data, so that the data checking efficiency is improved, the data checking coverage is expanded, the labor cost is reduced, and the data quality is guaranteed.

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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