Index type numerical type financial time series data intelligent auditing system

Abstract

An index type financial time series data intelligent auditing system comprises a submitting mechanism, a -N-level auditing mechanism and a final auditing mechanism, the submitting mechanism is used for reading index type numerical value type financial time series data, verifying the financial time series data by adopting a corresponding data verification rule configured by a final verification institution based on different index type numerical value type financial time series data types, and pushing a verification result to a corresponding verification institution; and the auditing mechanismis used for verifying and confirming the real reliability of the data step by step, and pushing the inspection result upwards step by step after the data is verified and confirmed to be real and reliable step by step until the final inspection result is pushed to the final auditing mechanism. According to the invention, intelligent inspection and auditing of the to-be-inspected data are achieved,and meanwhile, authority management of data auditing users of different levels is also achieved.

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 review system. Background technique [0002] High-quality financial data is a necessary support for the "dual-pillar" regulatory framework of monetary policy and macro-prudential policy, and also a strong guarantee for the decision-making of individuals, enterprises, financial institutions, and government agencies. [0003] 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...

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, readability of output results, and controllability of institutional management.

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