Artificial Intelligence Technologies and Process Efficiency in the Banking Industry: A Case Study of Selected Commercial Banks in Kenya
DOI:
https://doi.org/10.59413/ajocs/v6.i5.12Keywords:
Artificial intelligence, Process efficiency , Banking IndustryAbstract
Banks are increasingly integrating Artificial Intelligence (AI) technologies in their systems to streamline operations, reduce manual effort, and enhance transaction accuracy. While AI is expected to improve key process efficiency metrics, such as transaction turnaround time, decision-making speed, process error rate, and overall process cost, the actual impact on these specific performance indicators remains underexplored in Kenya's banking industry. Thus, this research examined the impact of AI technologies on process efficiency in selected commercial banks in Kenya. The study applied the technology acceptance model, resource-based view model, and the diffusion of innovation theory. The four independent variables: machine learning, robotic process automation, natural language processing, and predictive analytics, were analyzed in relation to process efficiency. The target population comprised of the 38 commercial banks in Kenya. The sampling frame was Equity Bank, Kenya Commercial Bank (KCB), Cooperative Bank, NCBA Bank, and Standard Chartered Bank branches in Nairobi County. The unit of analysis included the banks' branch managers and information technology managers, who were selected via purposive sampling. The sample size was 192 participants. Data was collected via closed-ended questionnaires, with a pilot study involving 19 participants to inform the questionnaire's reliability and validity pre-tests. SPSS version 22 was used for statistical analysis, and the findings presented in tables and charts. The researcher complied with appropriate ethical protocols. The findings showed that machine learning, robotic process automation, natural language processing, and predictive analytics impacted process efficiency in various magnitudes. All the four regression coefficients were statistically significant at p<0.05. Robotic process automation had the most significant impact on process efficiency while predictive analytics had the least impact. The results of the correlation analysis showed a positive, strong and significant relationship between each of the four independent variables and process efficiency. The study concluded that AI technologies significantly impacted process efficiency in the selected commercial banks in Kenya. The findings underscore the need to incentivize investments in AI technologies in Kenya's banking sector. Future studies could explore associations underpinning other AI technologies and process efficiency indicators.
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