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Artificial Intelligence Technologies and Process Efficiency in the Banking Industry: A Case Study of Selected Commercial Banks in Kenya

Author

Listed:
  • Annbel Nekoye Chibole

    (Kenyatta University)

  • Bernard Ondara

    (Kenyatta University)

Abstract

This study examined the impact of artificial intelligence technologies on process efficiency in selected commercial banks in Kenya. Guided by the Technology Acceptance Model, Resource-Based View, and Diffusion of Innovation Theory, the study analyzed machine learning, robotic process automation, natural language processing, and predictive analytics as determinants of process efficiency. Data were collected from 192 managers across selected bank branches using structured questionnaires and analyzed using SPSS. Regression results indicated that all four AI technologies significantly influence process efficiency, with robotic process automation having the strongest impact. The findings suggest that banks should increase investments in AI-driven technologies to improve operational performance and decision-making efficiency.

Suggested Citation

  • Annbel Nekoye Chibole & Bernard Ondara, 2025. "Artificial Intelligence Technologies and Process Efficiency in the Banking Industry: A Case Study of Selected Commercial Banks in Kenya," African Journal of Commercial Studies, African Journal of Commercial Studies, vol. 6(5).
    • Handle: RePEc:cwk:ajocsk:2025-105
    DOI: 10.59413/ajocs/v6.i5.12
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    References listed on IDEAS

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    1. Bonnie G Buchanan & Danika Wright, 2021. "The impact of machine learning on UK financial services," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 37(3), pages 537-563.
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