AI, automation, and cloud-native platforms are reshaping financial services, yet the very technologies built for speed and security continue to expose institutions to hidden risks. Fragmented systems, inconsistent reporting, and weak governance leave many banks struggling to satisfy regulators and keep leadership informed. Automation, often viewed as a cure-all, introduces its own concerns—from algorithmic bias to “black box” decision-making. This tension between trust and technology now defines boardroom priorities across global finance.

The work of Ravikumar Mani Naidu Gunasekaran has evolved within this complex environment. As a prominent figure in financial IT, he has built a reputation for strengthening data governance foundations and embedding ethical automation into the most sensitive areas of international banking. His contributions highlight not only technical mastery but cultural transformation—where accountability, stewardship, and trust matter as much as code.

While leading a multi-year overhaul at a Tier-1 bank, he transformed enterprise data governance end-to-end. The results were substantial: a 40% reduction in data quality issues affecting high-stakes regulatory reports like FR 2052a and Basel Risk-Weighted Assets. “You can’t fix what you can’t trace,” he explained. “By establishing ownership, lineage, and stewardship, we brought clarity where there had only been silos.”

The changes reassured regulators long frustrated by inconsistent submissions, and they empowered business units with accurate, traceable data. For Ravikumar, governance is not merely compliance—it is resilience.

His approach to automation reflects the same philosophy. “Automation without oversight can create a faster path to mistakes,” he warned. At the same bank, he designed a machine-learning pipeline for liquidity risk monitoring that reduced manual errors by more than 70%. But its true breakthrough was the built-in guardrails: regulatory alignment, auditability, and data-ethics checks on every automated decision. This governance-first model prevented the pitfalls of opaque AI and earned strong auditor praise.

Under his leadership, FR 2052a data quality issues dropped from 32% to under 5%, and swap data reconciliation breaks fell 89%. His cloud-native modernization cut reporting latency from T+1 to near real-time, enabling treasury teams to forecast liquidity in hours. “These aren’t just efficiency gains. They are risk reductions,” he noted. “Fixing these issues protects both compliance and credibility.”

Cultural change, he says, is the hardest part. By championing ethical automation forums, drafting responsible-AI policies, and mentoring teams, he helped shift mindsets. “The hardest part isn’t designing the system. It’s earning trust in the system.”

For him, the future of financial IT lies in transforming governance and ethics into strategic differentiators. “Transparency builds trust— with regulators, with customers, and with employees.” Ethical automation, he argues, is not about replacing humans but empowering them: “An algorithm is only as good as the processes and data that support it.”

As financial technology accelerates, his message remains clear: “Technology will keep evolving, but trust will always depend on methodology.”