It is important to adopt a comprehensive approach to data migration, including a thorough analysis of the data and systems involved, careful planning and testing of the migration process and ongoing monitoring and review to ensure the accuracy and completeness of the data when it is implemented in the new system.

For regulated organisations in financial services sectors such as superannuation, banking, insurance and wealth management, poor data can result in breaches, potential fines, and reputation loss. Here’s how to ensure data quality success in financial institutions.

As a first step, our client was looking to streamline their operations and accelerate efficiencies. This called for a purpose-built solution that could be automated and customised to meet their organisations data quality needs. Another requirement was the ability to have a real-time, dashboard view of all the data discrepancies across multiple systems and data points within the organisation.

Our client would then use the rich data to inform organisational growth through data analytics, strategic decision making, digital transformation initiatives, automation and investment in artificial intelligence and machine learning.

As part of AUSTRAC Customer identification and verification obligations, Financial Institutions must identify any customers present on several, centralised-Government managed and issued customer listings. ​

Historically, organisations would meet this obligation by providing a significant volume of customer-sensitive data to an external party, who would assess the data and provide potential findings back to the organisation. ​

This presented not only a significant data security risk to organisations but also, an inability to keep pace with a rapidly changing political climate, such as the sanctions that were imposed due to the conflict in Ukraine.

Heavily regulated industries are required to monitor for financial crime and other related activities as a part of Anti-Money Laundering (AML) and Counter-Terrorism Financing (CTF). This includes activities such as transaction monitoring, fraudulent behaviour and screening of customers on a regular basis. Our client, a large superannuation fund, wanted a way to securely manage their fraud prevention and transaction monitoring.

InvestigateDQ was integrated into everyday BAU processes across multiple teams with 160 staff members in the production environment using it daily and the intention to push it out across the entire company. These are staff members from all aspects of the business, using it for everything from day-today work queues to report generation and risk management. Before transitioning, the InvestigateDQ team were able to train a few staff members, who in turn trained everyone else; a testament to the usability of the system

Using Investigate DQ, the project team were able to connect to each data location and easily apply rules that could identify data discrepancies across members, attributes and financial data. Customised reporting and intuitive dashboards allowed the team to track and report on the number of issues identified at each stage of the migration. This provided clarity and transparency to the stakeholders involved on the project to understand shifting priorities and target areas of concern.

Navigating changing technological and regulatory environment, while meeting customer needs and maintaining a reputation can prove to be complex and difficult to tackle without robust data quality management.

Overall, using Investigate DQ proved to be critical in the successful generation and distribution of member benefit statements on a quarterly basis. Not only were various teams and departments able to collaborate efficiently, a significant reduction in errors was also observed with each statement cycle, providing confidence in the process and better outcomes for members.