Data is now the foundation of modern banking strategy no longer a backend asset in the rapidly evolving financial services industry. The sector is undergoing a significant transformation as digital capabilities blend with traditional roles, redefining operational efficiency, compliance, and customer experience. Amid this change, a new class of professionals is emerging, those with hybrid skillsets who can architect data-driven solutions while speaking the language of banking fluently.
One such expert is Vijayalakshmi Duraisamy, whose techno-functional expertise has shaped major data driven product decisions and large-scale banking applications in retail banking, cards, deposits, and mortgage services.
With years of experience developing core banking systems, Vijayalakshmi has led initiatives where data served both as the catalyst and the foundation. She played a pivotal role in building the U.S. Customer Complaint Processing System for retail banking customers. By leveraging historical data to automatically recommend solutions based on past cases, the system reduced redundancy, saved agents’ time, and significantly improved operational efficiency. It directly decreased agent handling time per case, leading to a 7% monthly cost saving. This wasn’t merely a technical achievement it was a functional innovation grounded in deep understanding of customer service dynamics and data behaviour.
She also served as a Subject Matter Expert during the launch of Youth Banking; a strategic product designed for customers’ children aged 13–21. This initiative was entirely informed by existing customer data, demonstrating how demographic and behavioural insights can drive product development. Another key project was the Customer 360 initiative, which consolidated all customer data points into a unified view, enabling the successful launch of senior citizen-focused digital banking products. Each of these projects underscores the importance of pairing domain knowledge with data acumen hallmarks of Vijayalakshmi’s approach.
Her impact within the organization has been both substantial and measurable. In regulatory data remediation, she addressed critical audit concerns involving materiality thresholds from $10 billion to $80 billion. Drawing on her deep functional understanding of product level data, she resolved discrepancies in Customer specific information in financial reporting, units, and directive line reporting efforts that safeguarded over $200 billion in submitted data and helped the bank avoid penalties and reputational damage. These solutions required not only technical fixes but also strategic insight into data lineage, source system profiling, and regulatory compliance.
Among her notable projects are the U.S. customer complaint management system, data remediation programs, data source optimization, and data acquisition frameworks for new commercial card products. Each presented unique challenges, especially in identifying the root causes of issues within decentralized data systems. In many cases, audit reports lacked specifics, forcing Vijayalakshmi to rely on her extensive banking knowledge to profile protected datasets, understand derivation logic, and trace the source of discrepancies tasks few were equipped to manage.
Quantitatively, her data driven initiatives have helped the bank avoid regulatory penalties close to $80 billion, saved costs across remediation efforts involving $200 billion in assets, and supported the data acquisition necessary to launch new products with over $100 billion in market potential. These outcomes not only demonstrate her value to the institution but also highlight the growing necessity of data savvy functional experts in modern banking.
Although she hasn’t published official research on the topic, Vijayalakshmi is passionate about sharing her insights. She believes the industry needs to have more conversations about developing hybrid skillsets. Her professional philosophy is clear: data professionals in banking must also possess domain expertise. A functional understanding of banking products, regulatory frameworks, and customer behaviour must be tightly integrated with data architecture and analytical capabilities solutions can no longer be developed in silos.
She emphasizes the importance of continuous upskilling and promotes a culture of techno-functional learning. As the industry continues to digitize, she believes the future belongs to those who can bridge the gap between strategy and systems, and between data and decision making.