The rise of information technology (IT), internet penetration, and online banking availability have made the banking sector's adoption of Artificial Intelligence (AI) technologies progressively easier. New technological advancements have facilitated the emergence of AI systems into numerous banking functions, primarily through adopting machine learning (ML) and natural language processing (NLP).

These attempts could substantially benefit customer service, financial disbursement, spending reports, and fraud detection. As Kaur et al. (2020) have commented, integrating AI within the banking industry considerably increases automation, reduces operational errors, and decreases expenses, enabling banks to shift their attention to more value-adding activities. In an era of growing digitalization where financial institutions struggle to maintain a competitive edge, AI has also facilitated a critical shift towards personalized customer service, improving engagement and retention rates, which is vital for a competitive edge (Vijai, 2019).

While AI possesses several benefits, its application within the banking industry has specific challenges that can affect its success. One such challenge is over-automation, where heightened reliance on AI systems can lead to grave dissatisfaction within the workforce, which may culminate in employee resignations or layoffs. Sastry (2020) mentions several societal implications of automation, one of which is the adverse effects on employees, especially for individuals with lower skill sets. In addition, bias through AI-powered decision-making algorithms poses significant challenges toward equitable and fair treatment within society. Talwar et al. (2017) argue that there are instances where the application of deep learning and predictive modeling in banking may unintentionally increase customer bias, thus resulting in unequal treatment of the clients. Thus, this calls for banks to act on these issues by reducing the AI system's biases to ensure equity and transparency in the institution's decision-making processes.

Apart from such internal difficulties, adopting AI within the banking sector is challenged externally. One of these challenges is what has been designated as the digital gap, which serves as a barrier to income-deprived or low-tech-savvy populations from accessing AI-driven financial services. As Caron (2019) states, automation through technology, which eliminates the “human emotional element,” can sometimes decrease customer loyalty and trust, which makes it even more challenging for banks to deal with disadvantaged groups. The solution to this challenge is to ensure that banks can serve underserved populations by integrating comprehensive service features that will help bridge the digital divide.

On the other hand, privacy and cyber security are critical risks that must be considered when using AI in banking. Artificial intelligence systems are built on abundant consumer information, mostly saved in cloud servers. Consequently, sensitive customer data becomes exposed to cyber invasions, resulting in a loss of market share, legal action, and distraction of brand equity (Ghandour, 2021). Banks must make substantial capital expenditures on IT and AI systems to protect consumer data from breaches. Still, these actions do not calm the lingering fears of data privacy, for the legal protection of customers’ information in the traditional sense is dismantled by AI’s access to and ability to process massive datasets.

One of the most notable barriers to adopting AI technology is technological readiness. Mogaji et al. (2021) contend that technological maturity, a metric defining the readiness of a particular society or industry to accept new technologies, serves as an important indicator as to why the implementation of AI within the banking industry has been so sluggish. Several non-ICT businesses, such as several financial institutions, will likely face challenges in adopting digital technologies due to infrastructure, leadership, and resource constraints (Pelin & Osoian, 2021). The integration of systems alongside the processing of data and training models, which is often necessary to develop AI, is incredibly costly, compounding these difficulties. Reliable data, which serves as a prerequisite for practically training and evaluating AI solutions, is challenging, thus impeding the simulation and modeling of the systems (Priya & Sharma, 2023).

Beyond that, AI system sophistication entails a significant barrier to their adoption in the banking sphere. AI models demand considerable machine learning expertise, data processing, and deployment of models, which banks might not be equipped with, causing delayed implementation (Romao et al., 2019). Furthermore, the sophistication of AI is ascribed to its exorbitant installation costs, such as personnel recruitment, purchasing of infrastructure, and skills training. AI has significantly impacted the management of an organization’s IT infrastructure because it incorporates numerous systems management functions like server maintenance and resource distribution, which decreases the dependence on human productivity to complete basic and repetitive tasks (Mogaji et al., 2021). However, the systems are costly and may inundate resource-strapped banks.

The competition among banks accentuates the necessity to implement AI technologies. Changes brought about by new players like Fintech companies require traditional banks to adopt AI in order to remain competitive in the marketplace. AI enables banks to improve customer service, enhance business processes, and develop new products. On the contrary, AI adoption comes with significant costs regarding data preparation, personnel training, and the technological infrastructure itself, which are challenging for resource-constrained banks to manage (Nguyen et al., 2021). With aggressive competition within the industry, financial players have little choice but to embrace AI at an unprecedented pace, without which they will lose the market to other faster-adopting rivals.

Despite these obstacles to implementing AI in banking, there are enormous benefits to be gained, such as improved efficiency, enhanced customer experience, and increased security. Therefore, its implementation is imperative for the evolution of modern banking. To take these benefits, however, the banks must confront challenges in AI implementation, such as being technologically prepared, safeguarding privacy, dealing with sophisticated AI systems, and being affordable.

References:

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• Ghandour, M. (2021). Cybersecurity in the age of AI: Protecting banking data. Cybersecurity in Financial Services, 10(2), 123–136.
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