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The primary aim of this systematic literature review (SLR) was to identify, assess and synthesize the extant evidence about the opportunities and challenges concerning the use of Artificial Intelligence (AI) in the banking sector. From the SLR, it is evident that AI has several opportunities for the sector. There are many fin-tech start-ups that offer banking AI solutions, and banking regulators are fostering AI adoption through legislation and collaboration. Other opportunities include the following: personalized services, smart wallets, decision-making and problem-solving, customer satisfaction and loyalty, process automation (especially targeting repetitive tasks), transactional security and cybersecurity improvements, and promotion of digital financial inclusion. Nevertheless, the key banking industry stakeholders have to formulate appropriate strategies aimed at overcoming existing and prospect AI challenges. Among the AI challenges that should be prioritized we include the following: job loss and user acceptance concerns, privacy breaches, creativity and adaptability loss, restrictive implementation and operational requirements, digital divide, availability of vast quality data, AI-business strategy alignment, and loss of emotional “human touch”. However, existing studies are largely descriptive and based on secondary sources of data. This necessitates empirical studies to expand the existing body of knowledge regarding AI opportunities and challenges in the banking industry.
Ahmad Ghandour (Fri,) studied this question.
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