Providing a Multi-objective Mathematical Model for Optimizing the Equipment and Allocation of Financial Resources of the Banking System

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

2 Assistant Prof., Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Abstract

Objective: As economic enterprises that seek to earn a profit, banks collect and accept financial resources from depositors in the form of financial intermediaries. They allocate the resources to economic activities (investors) by providing various facilities. Therefore, banks play a very sensitive and important role in the economic system of a country and have a significant effect in regulating the economic relationships in society. Banks need to pay attention to the accurate allocation of facilities and resources. Accordingly, in today's financial landscape, effective asset and debt management has become a crucial component for the success of banks and other financial institutions. It is now essential to optimize the provision and allocation of financial resources in the banking system to reduce the costs of collecting financial resources and increase income via the distribution of financial resources in the form of various facilities. Introducing a multi-objective mathematical model and considering the limitations of the problem, this study seeks to present a mathematical model for equipping and optimal allocation of financial resources.
Methods: After reviewing the literature and previous studies conducted in the field of the research topic, using the opinions of banking experts, the researchers defined the objectives of the study as maximizing the income from the allocation of resources and minimizing the costs from the collection of resources. The first objective was defined to maximize the income from the payment of facilities (allocation of financial resources) and the second objective was defined to minimize the costs of the collection of financial resources (equipment of financial resources). The objectives were determined according to the rules and regulations of the Iran Central Bank as well as the financial regulations governing the business of the Iranian banking system. After defining and coding the model, the problem was solved using meta-heuristic algorithms. Finally, the effectiveness of the algorithms in providing the desired answer was assessed. Due to the NP-HARD nature of the problem, three meta-heuristic algorithms NSGAII, MOPSO, and CUCKOO were chosen to solve the problem. Due to the high efficiency of the MATLAB program, this software was used to perform the coding and calculations.
Results: The results of the research indicated that the credit and operational risks of the bank will be reduced in case the banking system's resources are equipped and allocated scientifically. The achieved results from the evaluation of the algorithms confirmed the superiority of the mass particle swarm algorithm (MOPSO) over the other two algorithms. Long calculation time was found to be one of the disadvantages of genetic algorithms. Also, in the evaluation of various indices, the cuckoo algorithm did not demonstrate any significant advantage over the other two algorithms under scrutiny.
Conclusion: The results showed that thanks to its optimal allocation of financial resources, the model presented by this study can significantly increase the profitability of banks.

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References

 
Abolhasani Komleh, S. Z, Rahnamaye Roodposhti, F., Shahorani, A., Hosseinzadeh Lotfi, F. (2017). Balance sheet management in a development bank based on the model of mathematical verbal problems. Journal of Financial Engineering and Securities Management (Portfolio Management), (34), 21-48. (in Persian)
Abu Nouri, A. A., Mirzaei, H., Hamoni, P. (2017). Determining the optimal portfolio of Agricultural Bank using ideal planning. Journal of Financial Engineering and Securities Management (Portfolio Management), (35), 1-18. (in Persian)
Al Borzi, M., Pourzarandi, M. I., Shahriari, M. (2011). Management of resources and costs in banks with a dynamic systems approach. Journal of Financial Engineering and Securities Management, (6), 41-59. (in Persian)
Ayesta, U., Erausquin, M., Ferreira, E. & Jacko, P. (2016). Optimal dynamic resource allocation to prevent defaults. Operations Research Letters, 44, 451-456.
Bolo, Q., Arabi, M. (2018). Identifying effective factors on comprehensive risk of government-owned banks. Journal of Government Accounting, 5(2), 25-46. (in Persian)
Cao, Ch., Dong, Y., Hou, W., Liu, Y. & Qian, X. (2019). Provincial official turnover and bank loans. Pacific-Basin Finance Journal, 57, 101202.
Dehghan nayeri, M. & Khazaei, M. & Alinasab imani,F.(1400). Boundary Judgments Analysis of the Beneficiaries in Banking Loan Allocation. Journal of Industrial Management. 13(1), 27-52. (in Persian)
Du, X., Du, C., Chen, J., & Liu, Y. (2023). An energy-aware resource allocation method for avionics systems based on improved ant colony optimization algorithm. Computers and Electrical Engineering, 105, 108515.
Ekhtiyari, M. & Alamtabriz, A. (2015). Optimizing the portfolio of banks' resources and expenses using linear programming (case study of Saderat Bank of Iran). Journal of Financial Management Perspective, 5(12), 135-158. (in Persian)
Eslami Bidgoli, Gh.R., Mehregan, M.R., & Gholami, P. (2012). Asset Liability Management (Alm) In Banks With Use Of Ahp-Fuzzy & Goal Programing. Financial Engineering and Securities Management (Portfolio Management), 2(9), 23-46. (in Persian)
Ghosh, S. (2019). Loan delinquency in banking systems: How effective are credit reporting systems? Research in International Business and Finance, 47, 220-236.
He, W., Li, W., & Wang, W. (2021). Developing a resource allocation approach for resource-constrained construction operation under multi-objective operation. Sustainability, 13(13), 7318.
Hosseinipour, S. M., Mohseni, S., Jafari Moghadam, M. (2017). Comparison of three linear, fuzzy and ideal planning methods in the optimal combination of resources and expenses of Agricultural Bank. Journal of Financial Engineering and Securities Management, (38), 357-374. (in Persian)
Hunjra, A. I., Islam, F., Verhoeven, P., & Hassan, M. K. (2022). The impact of a dual banking system on macroeconomic efficiency. Research in International Business and Finance, 61, 101647.
Ji, T., Khadidos, A. O., & Abo Keir, M. Y. A. (2021). Application of regression function model based on panel data in bank resource allocation financial risk management. Applied Mathematics and Nonlinear Sciences, 7(2), 181-192.
Katoh, N., & Ibaraki, T. (1998). Resource allocation problems. Handbook of Combinatorial Optimization, 3, 905-1006.
Khatami Firouzabadi, S. M. A., Taqvi Fard, S. M. T., Sajjadi, S. J., Bammad Sufi, J. (2017). Multi-objective optimization model of service allocation to bank customers using data mining and simulation. Journal of Smart Business Management Studies, 8(30), 85-110. (in Persian)
Lubello, F., Petrella, I., & Santoro, E. (2019). Bank assets, liquidity and credit cycles. Journal of Economic Dynamics and Control, 105, 265-282.
Monjazeb, M. R. & Mousavi, I. (2019) Providing Optimized Banks Resource Allocation by Emphasizing on the Role of Risk Management (Total Criteria Approach and Sequential Unconstrained Optimization Technique). Journal of Financial Management Strategy, 8(29), 23-40. (in Persian)
Nakshineh, N., Hanifi, F. & Kordlouee, H. (2013). Managing bank assets and liabilities using linear multi-objective programming with econometric simulation "Case study: Bank X". Journal of Financial Engineering and Securities Management (Portfolio Management), (20), 95-116. (in Persian)
Parsafard, M. & Shirkavand, S. &Tehrani, R. & Mirlohi, S.(1397). Standalone Credit Rating of the Country's Banks. Journal of Industrial Management, 10(4), 575-606. (in Persian)
Pekandeh, M. & Nazi, M. (2019). Investigating the relationship between credit risk and the cost of money, a case study of mellat banks in Ilam province. Journal of applied research in management and accounting, 5(17), 1-19. (in Persian)
Pérez-Martín, A., Pérez-Torregrosa, A., & Vaca, M. (2018). Big Data techniques to measure credit banking risk in home equity loans. Journal of Business Research, 89, 448-454.
Tavakoli, M. & Nouruzi, N. & Kalami, M. & Salamatbakhsh, A. (2013). meta-heuristic algorithms (theoretical foundations and implementation in MATLAB). Islamic Azad University, South Tehran branch press. (in Persian)
Thakor, A. V. (2021). Politics, credit allocation and bank capital requirements. Journal of Financial Intermediation, 45, 100820.
Zhou, B., & Zhang, C. (2023). When green finance meets banking competition: Evidence from hard-to-abate enterprises of China. Pacific-Basin Finance Journal, 101954.
Zhu, W., Huang, Y., & Yu, Y. (2023). DEA model for Partial Centralization Resource Allocation among Independent Subset of DMUs. Computers & Industrial Engineering, 109013.
Industrial Management Journal
Volume 15, Issue 2
2023
Pages 272-298

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