The Effect of Data Mining on the Strategic Decisions in Jordanian Local Banks
DOI:
https://doi.org/10.59994/ajbtme.2025.1.35Keywords:
Data Mining, Strategic Decisions, Banking SectorAbstract
This study explores how data mining techniques contribute to supporting strategic decision-making in banks operating in Jordan. As these banks increasingly rely on data to stay ahead in a competitive market, techniques such as classification and regression have become crucial in identifying patterns within large datasets. The study follows a descriptive cross-sectional methodology and collects data through a questionnaire. The sample consisted of 395 employees working in local Jordanian banks in the Irbid Governorate, selected using a simple random sampling technique. The aim of the study was to assess the extent to which data mining contributes to improving the quality of managerial decisions. The results revealed a strong relationship between the use of data mining tools and the quality of decisions made. This highlights the importance of adopting advanced data processing methods in the banking sector. The study also emphasizes the need for banks to enhance their data management and utilization mechanisms in order to make smarter decisions, reduce risks, and maintain competitiveness. Overall, the study provides valuable insights for professionals and constitutes a significant contribution to the field of business intelligence.
References
Abu Ghanem, K. (2020). Business intelligence implementation challenges in Jordanian banks. Arab Journal of Administration.
Ahmed Said, A. (2016). Evaluation of open-source data mining tools: Analytical study (In Arabic). Journal of Taibah University Arts & Humanities. 5(10), 792-865.
Ahmed, M. (2018). Business intelligence and data utilization in Jordanian banks. Arab Journal of Administration.
Al-Najjar, F. J., Al-Najjar, N. J., & Al-Zoubi, M. R. (2013). Methods of scientific research: an applied perspective. Al-Hamid Publishing and Distribution Bureau, Amman, 63.
Al-Saadi, M., & Zabar, A. (2013). Information quality and its impact on strategic decisions in Iraqi banks (In Arabic). Al-Qadisiyah Journal of Administrative and Economic Sciences. 15(3), 7-41.
Arouf, A. (2018). The role of business intelligence systems in achieving competitive advantage: A case study on banks in Constantine (In Arabic). (PhD dissertation), Université Larbi Ben M'hidi Oum El Bouaghi, Algeria
Balan, V. J. R. V. S. (2013). A review on data mining in banking sector. American Journal of Applied Sciences, 10(10), 1160–1165.
Cai, J., & Durkin, J. (2005). Use of expert knowledge for decision tree pruning. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI-05) (pp. 1600–1601)
Gujarati, D. N. (2004). Basic econometrics (4th ed.). McGraw-Hill Education.
Gujarati, D. N. (2004). Basic econometrics (4th ed.). Tata McGraw-Hill Publishing Company Limited.
Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics. McGraw-hill.
Hamid, A. J., & Ahmed, T. M. (2016). Developing prediction model of loan risk in banks using data mining. Machine Learning and Applications: An International Journal, 3(1), 1–9.
Hassani, H., Huang, X., & Silva, E. (2018). Digitalisation and big data mining in banking. Big Data and Cognitive Computing, 2(3), 18.
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Ledhem, M. A. (2021). Data mining techniques for predicting the financial performance of Islamic banking in Indonesia. Journal of Modelling in Management, 3, 896–915.
Md Abu Sufian Mozumder et al. (2024). Optimizing customer segmentation in the banking sector: A comparative analysis of machine learning algorithms. Journal of Computer Science and Technology Studies, 6(4), 01–07.
Mousa, M. E.-S., & Kamel, M. A. (2022). An integrated framework for predicting the best financial performance of banks: Evidence from Egypt. Journal of Modelling in Management, 17(3), 964–986.
Ngai, E. W., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision support systems, 50(3), 559-569.
Rupnik, R., Kukar, M., & Krisper, M. (2016). Integrating data mining and decision support through data mining-based decision support system. Journal of Computer Information Systems, 47(3), 89–104.
