Data Quality for Banking
The company, formed through the merger of large IT service providers in the banking sector, specializes in IT outsourcing of back-office services, offering integrated and flexible technological solutions designed to improve business performance.
The Context
The lack of a centralized tool for data governance and quality exposed the company to risks and delays.
Individual banking processes were fragmented and inefficient, with no quality controls or automated remediation and correction processes in place for the most critical data, such as customer information, regulatory reporting, anti-money laundering, and accounting records.
The absence of clearly standardized processes and applications meant that each client bank used a different system, which negatively impacted the quality of banking data and turned the maintenance of individual systems into a complex and costly process.
The company had already initiated a large-scale Data Governance project aimed at collecting and streamlining all of its clients’ banking data processes toward a centralized system. The goal was to implement a straightforward system capable of managing large amounts of data while ensuring compliance with the stringent banking regulations in Italy and across Europe.
A key component of the master Data Governance project was the design and implementation of activities directed at improving data quality, with the objective of creating processes that could automatically detect data errors and imperfections while ensuring rapid revision. To achieve this goal, the company sought a partner able to oversee the development and execution of the Data Quality project.
Solutions
During the planning phase, we carefully analyzed the project scope and the Data Quality requirements identified by the client, who provided us with the necessary guidance. Based on this input, we developed individual controls for the areas previously recognized as critical. Once the framework was in place, we moved on to the execution phase: the controls were applied across all banking data in order to detect anomalies and deviations from established quality standards. The results of these checks, classified as positive or negative, were automatically logged in detailed reports.
In the next step, we performed a double verification process. First, we made sure that the processes were carried out correctly, free from operational errors. Then, we checked that the positive and negative outcomes recorded were indeed consistent with the rules defined during the development phase.
Once the negative control results were obtained and validated, they were then reported to the respective Data Owners in order to proceed with the remediation phase. This consisted of reviewing and correcting data that failed the checks to ensure compliance in future quality controls.
The results
The company has begun converging all its internal Data Quality processes into a single application. The outcomes and supporting evidence from periodic Data Quality controls in the areas involved are now collected in a centralized and standardized manner. By using a single, user-friendly dashboard, the company provides clear reports to clients, highlighting the results of the controls with detailed insights, useful for data correction and improving accuracy.
A central hub allows the company to replicate this model and gradually extend the Data Quality project to cover all areas involved in data management and production.
Thanks to the system for reporting negative results, Data Owners now have a clear view of where and how to make the necessary corrections, thus minimizing the effort required for data checks. As a result, the information assets of client banks are becoming more consistent and reliable, improving in quality, and helping protect the company’s clients from sanctions and reputational risks.