In an interaction with Asia Business Outlook, Rasha Salama, Head of Data Risk Management, Retail and Wealth Banking, UAE and MENAT, HSBC, shared her views and thoughts pertaining to the data quality strategy to enhance customer experience as well as a robust data governance framework to enhance operational efficiency and reduce risk in a banking environment. With over 20 years of banking experience, Rasha specializes in regulatory/tax programs, financial crime, network management, and data management (including governance, quality, and privacy) across the Middle East, North Africa, and Turkiye. Currently, she leads data management risk for Wealth and Personal Banking, driving sustainable business growth.
Data has become the front and centre of strategic transformation for many organizations and a robust data quality strategy significantly enhances customer experiences by ensuring that organizations and businesses have accurate, reliable, and up-to-date information to make a customer interaction smooth and personalized.
Having the right data quality framework and operating governance allow the business to understand customer needs and purchase patterns and leads to serving customers effectively according to their needs, tailor experiences, and recommend products and services that align with individual preferences and hence a more satisfying experience. Additionally, it supports the organizations with improved customer communication as they will always maintain accurate customer contact details minimizing the risk of miscommunication. It is also important to note that consistently accurate and reliable data builds customer loyalty over time, as customers trust the organization when they feel understood and valued.
A reliable data quality influences the value and accuracy of insights derived from data which subsequently influences the business objectives, decision making, outreach, and success. In today’s competitive markets, advanced analytics rely heavily on data to identify patterns, make predictions, and generate insights,as such, inaccurate or outdated data will lead to incorrect results and business may lose customers due to bad experiences. Quality data also enables predictive analytics to anticipate customer need, identify potential issues, and provide proactive support as needed. Similarly, predictive models produce reliable outcomes when high data quality is maintained. It is important to understand that data driven decisions are only as good as the underlying data quality. High quality and relevant information lead to better outcomes versus poor data quality which can result in misguided decisions.
AI and ML are increasingly becoming integral to fraud detection and risk management, and as mentioned earlier for data analytics, also for AI and ML technologies, they are only as good as the data they are trained on, this is why it is essential to ensure integrity and accuracy of data. In that aspect, good data quality can influence the effectiveness of fraud detection and risk management in various ways such as:
A clear and well-crafted data management including data quality is crucial for banks to stay compliant with current regulatory requirements and to remain agile in responding to future ones. Data quality strategy plays a crucial role in determining a bank’s ability to comply with regulations which helps in supporting and enabling accurate reporting, efficient risk management and operational resilience.
By looking at the key components of a strong data quality strategy; establishing a robust data governance framework, use of advanced technology, flexible systems, promoting data integrity, continuous validation, and monitoring of data, all backed up by training and a unified data led culture, together these key components can enhance the banking institution ability to comply with evolving regulatory requirements.
For instance, an efficient data quality framework improves the accuracy of regulatory reporting process and ensures accurate and timely reporting. Another example is the adaptability to accommodate new regulatory requirements such as real-time transaction monitoring. Additionally, monitoring the data collection points and data entry processes remain crucial in ensuring high data quality is used for the various regulatory reporting purposes to align with regulatory frameworks like BCBS 239, FATCA, and Common Reporting Standards (CRS).
If data is mismanaged, it can have a negative impact on a business’s performance leading to loss of sales, unhappy customers, etc. A strong data management and governance framework help banks to optimize their operations while ensuring regulatory compliance and risk mitigation which ultimately leads to a sustained growth and resilience. This can be achieved by improving data quality through reliable inputs for decision making, continuous monitoring of data quality, automation of data checks and validation, reducing data duplicates and anomalies and scale operations efficiently. When having the effective data management framework in place, you minimize the risks associated with human errors, fraud, and system failures and mitigate the operational risks associated with modelling and scenario analysis. I would like to also mention that having clearly defined roles and responsibilities and accountability of data ownership support the overall framework within an organization.
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