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This study aims to fill critical research gaps by providing empirical evidence on the practical application of generative AI in the banking sector. It explores managerial preparedness, regulatory compliance and data privacy challenges in implementing this technology, offering insights into its operational effectiveness and potential in financial services.

The research employs a qualitative approach, conducting in-depth interviews with bank managers and industry experts. These interviews are analysed to identify key factors influencing the integration of generative AI in financial institutions.

The study identifies five critical factors – recognition, requirement, reliability, regulatory and responsiveness – that collectively impact the adoption and operational effectiveness of generative AI in banking. These factors highlight the challenges and opportunities of integrating this technology within the highly regulated financial industry.

The findings have significant theoretical and managerial implications. Theoretically, the research contributes to understanding AI integration in regulated industries, particularly financial services. Managerially, it provides a roadmap for financial institutions to adopt generative AI responsibly, balancing innovation with regulatory compliance and ethical considerations.

This study is among the first to provide empirical data on generative AI’s practical application in the banking sector, addressing the lack of real-world evidence and offering a comprehensive analysis of the factors influencing its successful implementation in a highly regulated environment.

The purpose of this study was to investigate the likely causes of failure of some sections of road pavements in Ajaokuta, Northcentral Nigeria. This was achieved through a geotechnical assessment of subgrade soils in affected areas.

The methods entailed field and laboratory methods and statistical analysis. Subgrade soil samples were retrieved from a depth of 1,000 mm beneath the failed portions using a hang auger. The soils were analyzed for natural moisture content (NMC), Atterberg limit (liquid limit, plastic limit and linear shrinkage), grain size distribution, compaction and California bearing ratio (CBR), respectively.

The results of the geotechnical tests ranged from NMC (12.5%–19.4%), sand (84%–98%), fines (2%–16%), LL (16.0%–32.2%), PL (17%–27.5%), LS (2.7%–6.4%), PI (2.5%–18.4%), maximum dry density (1756 kg/m2–1961 kg/m2), optimum moisture content (13.2%–20.2%), unsoaked CBR (15.5%–30.5%) and soaked CBR (8%–22%), respectively. Pearson’s correlation coefficient performed on the variables showed that some parameters exhibited a strong positive correlation with r2 > 0.5.

Funding was the main limitation.

Comparing the results with Nigerian standards for road construction, and the AASHTO classification scheme, the subgrade soils are competent and possess excellent to good properties. The soils also exhibited very low plasticity, a high percentage of sand, high CBR and low NMC, which implies that it has the strength required for road pavement subgrades. The likely causes of the failures are, therefore, due to the use of poor construction materials, technical incompetence and poor compaction of sub-base materials, respectively.