Analysis of Key Points in Data Cleaning and Migration Implementation for Financial Institution System Upgrades

Wenxin Li

doi:10.54097/005wpd83

Authors

Wenxin Li School of Business, University of New South Wales, Sydney, Australia

DOI:

https://doi.org/10.54097/005wpd83

Keywords:

Financial institutions, System upgrade, Data migration, Data cleaning, Consistency verification

Abstract

The quality of historical data migration during the upgrade of financial institutions will directly affect the stable operation of the new system after it is released. The new-generation core system of Guizhou Rural Credit Cooperative and the centralised trading system of Guolian Minsheng Securities are chosen as the subjects of this study. The former had more than 90 million current accounts and over 25 million fixed-term accounts. After 520 days of hard work and 22 hours, 64 system switches have been set up to complete the core data migration without any errors. The latter had connected 23 different systems, combined the data of about 1.92 million brokerage customers, and merged information from 74 branch offices. On the first day after the switch, 640,000 transactions had reached 9.476 billion yuan. The five contents of the implementation plan for the migration project are as follows: data cleaning, migration plan, consistency verification, risk response and organisation cooperation. Data quality assessment at the source end serves as the foundation for the cleaning operation. The Migration Strategy should balance the window for business interruption with the risk of the project. Whole-field comparison serves as the first-line verification method. The entire-process risk prevention and multi-departmental cooperation offer the final guarantee. Based on the public project data, discussions will be held to provide a reference for the design of a similar financial system migration plan.

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