Data-Driven Approaches to Optimize Branch and Team-Based Targeting in Banking
Abstract views: 104
/ 
PDF downloads: 43
Keywords:
Target Prediction, Machine Learning, Banking, Data ScienceAbstract
The utilization of machine learning techniques to enhance strategic and operational decision
making within the banking sector is explored in this research. The second-largest state bank in Turkey
conducted the studies, focusing on performance target prediction for two fundamental SME banking
products: non-cash loans and demand deposits. Given the complex influencing factors such as volatile
market conditions, customer creditworthiness, macro and microeconomic indicators, and team-specific
variables, accurate performance prediction remains a significant challenge. The aim was to develop robust
machine learning models capable of accurately predicting performance targets, thereby enabling efficient
resource allocation and performance management. Techniques ranging from data mining and data
preprocessing to feature selection and predictive modeling were applied in the studies. The effectiveness
of the Orthogonal Matching Pursuit CV algorithm for branch targeting of non-cash loans and Stacked
Regression algorithm for a dynamic team-based targeting process of demand deposits was revealed in the
findings. The transformative potential of data analytics in banking and the importance of refining these
models to cater to evolving industry needs are underlined by these insights.
Downloads
References
I. Met, A. Erkoc，, and S. E. Seker, “Performance, efficiency, and target setting for bank branches: Time series with automated machine learning,” IEEE Access, 2022.
A. S. Aphale and S. R. Shinde, “Predict loan approval in banking system machine learning approach for cooperative banks loan approval,” 2020.
F. Yetiz, M. Terzio˘glu, and M. Kayakus，, “An analysis of turkish deposit banks’ customer forecasting with machine learning methods,” Sosyoekonomi, vol. 29, no. 50, pp. 413–432, 2021. doi: 10 . 17233 / sosyoekonomi.2021.04.19.
L. Wang, F. Jia, L. Chen, and Q. Xu, “Forecasting smes’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques,” Annals of Operations Research, 2022. doi: 10.1007/s10479-022-04518-5.
A. Shinde, Y. Patil, I. Kotian, A. Shinde, and R. Gulwani, “Loan prediction system using machine learning,” in ITM Web Conf., vol. 44, 2022, p. 03 019. doi: 10.1051/itmconf/20224403019.
H. Hassani, X. Huang, E. Silva, and M. Ghodsi, “Deep learning and implementations in banking,” Annals of Data Science, vol. 7, pp. 433– 446, 2020.
S. Dosalwar, K. Kinkar, R. Sannat, and N. Pise, “Analysis of loan availability using machine learning techniques,” International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), vol. 9, no. 1, pp. 15–20, 2021.
J.Wang, S. Kwon, and B. Shim, “Generalized orthogonal matching pursuit,” IEEE Transactions on signal processing, vol. 60, no. 12, pp. 6202– 6216, 2012.
R. Polikar, “Ensemble learning,” in Ensemble machine learning: Methods and applications, 2012, pp. 1–34.
M. Amakobe, “The impact of big data analytics on the banking industry,” Ph.D. dissertation, Colorado Technical University, 2015.
