A Comprehensive Analysis of Artificial Intelligence (AI) and Robotic Process Automation (RPA) Integration for Back-Office Transformation on Hyper-Automation in Banking

Banking back-offices worldwide are currently facing an unprecedented convergence of challenges, characterized by significant operational friction stemming from complex legacy infrastructure, high volumes of manual transactional processing, and severe regulatory compliance demands, all of which inhibit speed-to-market and elevate the cost-to-serve. This context necessitates a radical paradigm shift toward Hyper-Automation (HA) the end-to-end automation of processes leveraging a unified orchestration of technologies. This study undertakes a comprehensive analysis of the strategic integration of Artificial Intelligence (AI), specifically Machine Learning (ML) and Natural Language Processing (NLP), with Robotic Process Automation (RPA) frameworks to achieve transformative outcomes in banking back-office environments. The core objective is to move beyond siloed task automation toward an intelligent, scalable, and resilient operational ecosystem. We investigate how the synergy between RPA’s structured, reliable execution capabilities and AI’s cognitive abilities such as dynamic decision-making, exception handling, and unstructured data interpretation enables banks to unlock new dimensions of efficiency, accuracy, and operational agility previously unattainable through conventional, rules-based automation methods. This research focuses on defining the architectural prerequisites, governance structures, and key performance indicators necessary for maximizing the impact of this integrated automation model across critical back-office functions. To rigorously evaluate this integration, a stringent mixed-methods approach was adopted, combining a systematic literature review with exploratory case studies of tier-one global financial institutions successfully deploying HA platforms. The methodology utilizes a socio-technical framework centered on process mining to accurately map the complex interdependencies between human workflows, legacy systems, and intelligent bots, thereby quantifying the return on automation investment (ROAI) across functions like loan origination, Know Your Customer (KYC) documentation, and payments reconciliation. Preliminary findings indicate that the unified AI-RPA model, termed 'Cognitive RPA,' yields substantial operational improvements: processing cycle times were reduced by an average of ۶۵%, operational expenditure declined by an estimated ۳۰%, and compliance audit readiness improved demonstrably due to immutable digital trails and enhanced risk monitoring capabilities. Crucially, the analysis reveals that effective HA implementation requires not merely technological deployment but a fundamental shift in organizational design and strategic governance to effectively manage the complexity of intelligent workflows and mitigate emerging algorithmic risks. This research contributes significantly to the academic discourse on digital finance transformation by providing a robust analytical model for assessing the true transformative potential of integrated automation architectures, offering empirical validation for the scalable benefits of Hyper-Automation, and presenting prescriptive guidelines for banking leaders and technology strategists seeking sustained competitive advantage in the digital era.

کلیدواژه ها:

Hyperautomation ، Banking ، Artificial intelligence ، Back-office transformation ، Natural language processing ، Robotic process automation