International Journal of Banking, Risk and Insurance

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Loans Portfolio Optimization of Commercial Banks using Genetic Algorithm : A Case Study of Saudi Arabia


Affiliations
1 University of Relizane, Algeria
     

   Subscribe/Renew Journal


This study aims at testing the optimal mechanism of bank lending decisions using artificial intelligence techniques. It is based on a sectoral diversification strategy to minimise risk and maximise return of credits facilities portfolio and support bank managers in their decision making. In this context, we suggest a dynamically self-regulating method to optimise the bank lending decisions, by the application of the metaheuristic approach represented by genetic algorithms optimization. It has been used and improved in more recent empirical studies; the method has become a hot research topic. The reason for choosing GA is its convergence and flexibility in solving multi-objective optimization problems, such as credit assessment, portfolio optimization, and bank lending decision. Furthermore, we have also used Markowitz model to construct a mean-variance optimization problem, based on estimate expected return and risk. Finally, the optimal loans portfolio, among 11 economic activity sectors in the Kingdom of Saudi Arabia during the period 1998-2020, has been selected. We have also compared the results of the genetic algorithm with the classic Markowitz model in its static form.

Keywords

Credit Risks, Optimal Loans Portfolio, Return & Risk, Sectoral Diversification, Genetic Algorithms optimization.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Agarana, M. C., Bishop, S. A., & Odetunmibi, O. A. (2014). Optimization of banks loan portfolio management using goal programming technique. International Journal of Research in Applied, Natural and Social Sciences, 2(8), 43-52.

  • Fekri, R. (2018). Optimization of bank portfolio investment decision considering resistive economy. Journal of Money and Economy, 11(4), 375-400.

  • Gupta, K. M. (2018). Linear programming techniques to optimize bank of Baroda’s loan portfolio. International Journal of Trend in Scientific Research and Development (IJTSRD), 2(4), 591-597.

  • Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms (2nd ed., pp. 27-50). John Wiley & Sons, USA.

  • KPMG. (2020). Kingdom of Saudi Arabia banking perspectives 2020. Retrieved from https://home.kpmg.com/sa

  • Messac, A. (2015). Optimization in practice with MATLAB for engineering students and professionals. Cambridge University Press, First published 2015, USA.

  • Metawa, N., Hassan, M. K., & Elhoseny, M. (2017). Genetic algorithm based model for optimizing bank lending decisions. International Journal of Expert Systems with Applications, 1-27.

  • Misr, A. K. (2013). Portfolio optimization of commercial banks - An application of genetic algorithm. European Journal of Business and Management, 5(6), 120-129.

  • Ning, J., Zhang, C., Sun, P., & Feng, Y. (2019). Comparative study of ant colony algorithms for multi-objective optimization. Journal of Information MDPI, 10(11), 1-19.

  • Orlova, E. V. (2020). Decision-making techniques for credit resource management using machine learning and optimization. Journal of Information MDPI, 11(144), 1-17.

  • Prigent, J. L. (2007). Portfolio optimization and performance analysis (1st ed.). Taylor & Francis Group.

  • Sadaf, A., & Ghodrati, H. (2015). An improved genetic algorithm method for selection and optimizing the share portfolio. International Journal of Computer Science and Mobile Computing, 4(1), 342-353. S

  • Sefiane, S., & Benbouziane, M. (2012). Portfolio selection using genetic algorithm. Journal of Applied Finance & Banking, 2(4), 143-154.

  • Soeryana, E., Halim, N. B. A., Sukono, Rusyman, E., & Supian, S. (2018). Mean-variance portfolio optimization on Islamic stocks by using non constant mean and volatility models and genetic algorithm. International Journal of Engineering & Technology, 7(3.20), 366-371.

  • Tursoy, T. (2018). Risk management process in banking industry. MPRA Paper No. 86427. Retrieved from https://mpra.ub.uni-muenchen.de/86427/


Abstract Views: 182

PDF Views: 0




  • Loans Portfolio Optimization of Commercial Banks using Genetic Algorithm : A Case Study of Saudi Arabia

Abstract Views: 182  |  PDF Views: 0

Authors

Zouaoui Habib
University of Relizane, Algeria
Meryem-Nadjat Naas
University of Relizane, Algeria

Abstract


This study aims at testing the optimal mechanism of bank lending decisions using artificial intelligence techniques. It is based on a sectoral diversification strategy to minimise risk and maximise return of credits facilities portfolio and support bank managers in their decision making. In this context, we suggest a dynamically self-regulating method to optimise the bank lending decisions, by the application of the metaheuristic approach represented by genetic algorithms optimization. It has been used and improved in more recent empirical studies; the method has become a hot research topic. The reason for choosing GA is its convergence and flexibility in solving multi-objective optimization problems, such as credit assessment, portfolio optimization, and bank lending decision. Furthermore, we have also used Markowitz model to construct a mean-variance optimization problem, based on estimate expected return and risk. Finally, the optimal loans portfolio, among 11 economic activity sectors in the Kingdom of Saudi Arabia during the period 1998-2020, has been selected. We have also compared the results of the genetic algorithm with the classic Markowitz model in its static form.

Keywords


Credit Risks, Optimal Loans Portfolio, Return & Risk, Sectoral Diversification, Genetic Algorithms optimization.

References