Expanding the Boundaries of RPA with Intelligent Automation in Investment Banking Operational Processes

Abstract

Investment banking operational processes consist of data-intensive, repetitive tasks such as lien transactions and service agreements. The manual execution of these processes carries significant risks in terms of efficiency, cost, and accuracy. While basic Robotic Process Automation (RPA) applications offer rule-based solutions for reporting, they fall short in processing unstructured data, such as contracts. This technological gap makes Intelligent Automation solutions inevitable. This study demonstrates, through a case study, how the limitations of basic RPA in Aktif Investment Bank's Lien Transactions and Retail Service Agreements (RSA) processes are overcome with intelligent automation. In the Lien Transactions process, the competency of Optical Character Recognition (OCR) technology for understanding data in unstructured PDF documents is examined, while in the Retail Service Agreements process, OCR-based Intelligent Document Processing (IDP) technology has been integrated into existing RPA bots.As a result of the Retail Service Agreements process, document analysis times, previously conducted entirely manually, were reduced by 50% (achieving a gain of approximately 10.5 FTE), and the operational error rate was found to be near zero. This article reveals the concrete efficiency gains of intelligent automation in complex fields like investment banking and how it transforms the operational scope of RPA.