TO WHAT EXTENT CAN LOW-CODE AUTOMATION USING VBA REDUCE RECONCILIATION TIME AND ERROR RATE IN COMPLEX FINANCIAL INSTRUMENTS LIKE CDOS?

This dissertation evaluates the impact of low-code automation, specifically Visual Basic for Applications (VBA), on reconciliation processes in structured finance, focusing on Collateralized Debt Obligations (CDOs). Using a two-phase mixed-methods design, the study first conducted a diagnostic survey of 50 operations professionals at BNP Paribas to capture manual workload, error frequency, and user readiness for automation. Results revealed persistent inefficiencies and strong user demand for tools that simplify repetitive tasks such as data matching and formatting. The second phase applied a quantitative pre- and post-intervention time-series analysis to assess the operational impact of a VBA macro implemented in April 2025. Post-implementation, daily error rates dropped by 64.4% and volatility in cash breaks was nearly halved, demonstrating significant improvements in efficiency and process stability. These findings corroborate survey responses and validate hypotheses related to error reduction, process consistency, and user acceptance, as theorized in the Technology Acceptance Model (TAM). While VBA proved effective as a lightweight automation solution, the research highlights potential scalability, governance, and end-user computing (EUC) risks. The dissertation concludes with practical recommendations for financial institutions seeking to modernize reconciliation processes through low-code solutions, emphasizing integration with Lean Process Redesign and user-centered adoption strategies.