Enhancing Regulatory Compliance in the Modern Banking Sector: Leveraging Advanced IT Solutions, Robotization, and AI

Abstract

In the current financial landscape, preventing banking fraud and managing risk are crucial, and integrating Artificial Intelligence (AI) offers a viable path for breakthroughs and maintaining regulatory compliance in these areas. This study explores the various ways that AI can be used to identify, stop, and handle fraud in the banking industry. The primary objective of this study was to assess how the banking industry's digitalization necessitates the use of AI to manage customer complaints and inquiries, ensure compliance with banking rules and regulations, and address other issues, either entirely or in conjunction with human interaction. Graph analytics offers a distinct perspective by visualizing transactional relationships, potentially highlighting suspicious activities such as rapid fund transfers that may indicate money laundering. Deep learning, specifically neural networks, is one of the noteworthy methodologies that, when trained on historical fraud data, can discern intricate patterns and predict fraudulent transactions with remarkable precision. Further, Natural Language Processing (NLP) can be utilized to enhance Know Your Customer (KYC) processes. It examines identity verification, risk management, and encryption best practices to strengthen digital infrastructures against violating compliance and unwanted access. It looks at new developments in the field of regulatory technology (Reg-Tech), including the use of artificial intelligence and advanced analytics to improve the capacity for compliance enforcement and monitoring. Technology's sustainability should be seriously investigated, as it is displacing humans and the personal touch that goes along with it. The financial services industry thrives on customization and customer satisfaction, so this is a significant question to ask beyond the ethical neutrality of technology and its attendant threats like cybercrimes and macro-financial risks. The results, which concluded a thorough literature analysis and interview responses, identify three factors: the goals of Cognitive AI, Environmental, Social, and Governance (ESG) and the complexity of putting the RPA solution into practice) essential for putting RPA into the banking industry in a sustainable manner. Through the examination of transaction locations and user interaction patterns, respectively, behavioral biometrics and geographic analysis further strengthen security's versatility is one of its main advantages.