Received 05.06.2024, Revised 10.09.2024, Accepted 10.10.2024
This paper aimed to evaluate the transformative role of Generative Artificial Intelligence in improving banking services efficiency through a systematic literature review. The review explored, how Generative Artificial Intelligence reshaped traditional banking practices by automating routine tasks, decision-making, and enhancing personalised customer experiences. The review also highlighted Generative Artificial Intelligences integration with advanced technologies such as blockchain and quantum computing, to achieve unprecedented levels of scalability, transparency, and operational excellence. The findings indicated the ability of Generative Artificial Intelligence to improve service quality through the automation of repetitive tasks such as loan applications and fraud detection, reducing operational costs, while optimising resource utilisation. AI-enabled chatbots and virtual advisors enhanced customer satisfaction by providing continuous service and personalised financial advice. The findings also validated the role of Generative Artificial Intelligence in preventing fraud through real-time anomaly detection and predictive analysis, reducing false positives and improving security scores. However, the findings identified major challenges such as algorithmic bias, risks from cyberattacks, and the opacity associated with “black-box” models, which complicate compliance and ethical governance. Regulatory frameworks and explainable AI models were identified as potential solutions to these problems. Additionally, employee upskilling was emphasised as essential for successfully adopting Generative Artificial Intelligence in banking. The review provided a holistic overview of the state of Generative Artificial Intelligence adoption in banking, the associated challenges, and future directions, enriching the academic discourse on enhancing innovation and sustainability within the banking sector
banking automation; artificial intelligence solutions; regulatory compliance; fraud detection; financial technology
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