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The Impact of AI on Job Polarization in Egyptian Banking: Patterns, Implications and Associated Risks

In: AI, Society and Digital Transformation

Author

Listed:
  • Nada Ali

    (Nanjing University of Aeronautics and Astronautics, College of Economics and Management)

  • Lamia Guseinova

    (Nanjing University of Aeronautics and Astronautics, College of Economics and Management)

  • Chuanmin Mi

    (Nanjing University of Aeronautics and Astronautics, College of Economics and Management)

Abstract

The expanding use of artificial intelligence (AI) in Egypt's banking and finance services industry has significantly impacted employment trends, leading to job polarization. This study investigates the relationship between AI adoption and industry job polarization using data from Egyptian financial institutions and national statistical agencies between 2015 and 2024. Employing principal component analysis and multiple regression analysis, the research reveals three key findings: (1) AI applications are driving job polarization, characterized by a decline in middle-skilled jobs (e.g., clerks, loan officers) alongside an increase in high-skilled (e.g., data scientists, AI engineers) and low-skilled employment (e.g., AI support staff); (2) Regional differences in AI adoption influence job polarization patterns, with urban areas like Cairo and Alexandria experiencing higher demand for highly skilled jobs compared to rural regions; (3) AI implementation creates new opportunities in high-skilled jobs focused on system development and low-skilled positions related to infrastructure maintenance, while simultaneously reducing demand for middle-skilled jobs that involve routine tasks. While growing social and economic gaps, regional disparities, and function-specific risk factors that call for strategic management represent some of the major risks related to implementation highlighted in the study.

Suggested Citation

  • Nada Ali & Lamia Guseinova & Chuanmin Mi, 2026. "The Impact of AI on Job Polarization in Egyptian Banking: Patterns, Implications and Associated Risks," Lecture Notes in Operations Research, in: Xiaolei Xie & Kejia Hu & Guiping Hu & Weiwei Chen & Robin Qiu (ed.), AI, Society and Digital Transformation, pages 143-157, Springer.
    • Handle: RePEc:spr:lnopch:978-3-032-13116-4_12
    DOI: 10.1007/978-3-032-13116-4_12
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