University of Isfahan Financial Accounting Research 2322-3405 15 3 2023 11 22 A Combined Analytical Hierarchy Process-Goal Programming Model for the Optimization of Bank Liquidity A Combined Analytical Hierarchy Process-Goal Programming Model for the Optimization of Bank Liquidity 53 74 28385 10.22108/far.2024.139765.2014 FA Ali Asghar Anvary Rostamy Professor of Finance, Department of Planning and Management, Tarbiat Modares University, Tehran, Iran. Journal Article 2023 11 11 One of the most critical responsibilities of bank managers is liquidity management. The purpose of this study is to design and implement a combined mathematical model of the Analytical Hierarchy Process (AHP) and Goal Programming (GP) for bank liquidity management. Initially, a conceptual model was drawn up, followed by the definition of model variables based on the bank's financial statements. Due to the presence of multiple, conflicting goals and the differing importance of each goal, the GP technique was utilized. The weighted nature of the GP model allowed for the calculation of the importance of ratios affecting the bank's liquidity within the objective function through the Analytical Hierarchy Process. The model was solved, and the analytical results were presented to bank officials. Finally, the effectiveness of the proposed model was validated through a survey of seven main questions related to seven evaluation dimensions from 30 banking experts. Given the similarities in banking systems in the country, using the proposed model and pattern of this research, with minor modifications, the necessary grounds for efficient liquidity management can be provided in other banks as well. Introduction Banking is one of the most important businesses in the economy and serves as the primary bridge between the supply and demand of monetary resources. The principal functions of the banking system involve the management of the provision and allocation of monetary resources and the provision of monetary services in the money market (Basel Committee on Banking Supervision, 2008). In Iran, banks operate as economic service institutions that earn profits through Islamic contracts and partnerships with clients (Abdorrahimian et al., 2020). Banking is the art of managing liquidity challenges, and liquidity management plays a crucial role in the survival, continuity, and success of banks. Consequently, it is expected that economic enterprises with appropriate liquidity management systems will have lower risks of liquidity and bankruptcy. Bank liquidity management includes forecasting the bank's liquidity needs over time and meeting these needs with the least costs and risk and the maximization of bank value. Liquidity management is the process of managing and balancing between risk and return. (Glants & Mun, 2002). Given the multiple objectives of banks and the difficulty or impossibility of simultaneously achieving all these diverse and often conflicting goals, the question of this research is what kind of model is the optimal liquidity management model for the banks? This applied research aims to answer the aforementioned question through the design, implementation, testing, and evaluation of a mathematical model of liquidity management in banks, while observing the ratios affecting the bank's liquidity within the standard limit, to optimize the amount of bank liquidity and the variables of the liquidity system inputs and outputs. Methods and Material To develop an optimal mathematical model for bank liquidity management, the steps include identifying the system's input and output variables, recognizing the most influential ratios, selecting the appropriate mathematical model type, determining the model variables and parameters, establishing the system and goal constraints, and formulating the objective function. After solving the model, the results were compared with the bank's current state to plan the transition to a desired state. Finally, the effectiveness of the proposed model was evaluated by 30 bank users and experts. Findings The proposed model enables sensitivity analysis of parameters and the examination of various scenarios. If there are practical limitations, alternative optimized solutions can be prepared to implement the most feasible solution vector and outline corrective actions in a roadmap (Anvary Rostamy & Nematolahi Ardestani, 2003). Although these alternatives slightly deviate from the optimal initial value, they offer the most practical benefits. The model was evaluated across seven dimensions: flexibility, usability, impact, acceptability of variables, accuracy of goal and systemic constraints, and ease of understanding and application by 30 banking experts. The evaluation results confirmed the flexibility, practicality, and suitability of the considered ratios and variables, as well as the appropriateness of the model's goal and systematic constraints at a 95% confidence level. Conclusion & Results This research aimed at designing an optimal mathematical model for managing bank liquidity. The goal programming model was used due to the multitude and contradictions among decision-making criteria. The model was developed using data from a specialized bank sample and then was tested. The effectiveness of the proposed model was reviewed, scored, and approved by 30 banking professionals and experts. The results of solving the models enable the formulation of a road plan and allow managers to immediately see the results of any potential changes in the model. This modeling tool also enables managers to create future scenarios and plan corrective actions. Given the similarities in the Iranian banking operations, despite some differences, the application of the proposed conceptual and operational model with minor modifications will provide the necessary conditions for more efficient liquidity management in other banks. The authors suggest considering off-balance sheet items and economic and political shocks and risks in the model completion and modeling using fuzzy data. Future research could measure the bank's liquidity sensitivity to changes in the economic market, such as changes in exchange rates, the country's exports and imports, and competitor markets like capital markets and real markets. * Corresponding author One of the most critical responsibilities of bank managers is liquidity management. The purpose of this study is to design and implement a combined mathematical model of the Analytical Hierarchy Process (AHP) and Goal Programming (GP) for bank liquidity management. Initially, a conceptual model was drawn up, followed by the definition of model variables based on the bank's financial statements. Due to the presence of multiple, conflicting goals and the differing importance of each goal, the GP technique was utilized. The weighted nature of the GP model allowed for the calculation of the importance of ratios affecting the bank's liquidity within the objective function through the Analytical Hierarchy Process. The model was solved, and the analytical results were presented to bank officials. Finally, the effectiveness of the proposed model was validated through a survey of seven main questions related to seven evaluation dimensions from 30 banking experts. Given the similarities in banking systems in the country, using the proposed model and pattern of this research, with minor modifications, the necessary grounds for efficient liquidity management can be provided in other banks as well. Introduction Banking is one of the most important businesses in the economy and serves as the primary bridge between the supply and demand of monetary resources. The principal functions of the banking system involve the management of the provision and allocation of monetary resources and the provision of monetary services in the money market (Basel Committee on Banking Supervision, 2008). In Iran, banks operate as economic service institutions that earn profits through Islamic contracts and partnerships with clients (Abdorrahimian et al., 2020). Banking is the art of managing liquidity challenges, and liquidity management plays a crucial role in the survival, continuity, and success of banks. Consequently, it is expected that economic enterprises with appropriate liquidity management systems will have lower risks of liquidity and bankruptcy. Bank liquidity management includes forecasting the bank's liquidity needs over time and meeting these needs with the least costs and risk and the maximization of bank value. Liquidity management is the process of managing and balancing between risk and return. (Glants & Mun, 2002). Given the multiple objectives of banks and the difficulty or impossibility of simultaneously achieving all these diverse and often conflicting goals, the question of this research is what kind of model is the optimal liquidity management model for the banks? This applied research aims to answer the aforementioned question through the design, implementation, testing, and evaluation of a mathematical model of liquidity management in banks, while observing the ratios affecting the bank's liquidity within the standard limit, to optimize the amount of bank liquidity and the variables of the liquidity system inputs and outputs. Methods and Material To develop an optimal mathematical model for bank liquidity management, the steps include identifying the system's input and output variables, recognizing the most influential ratios, selecting the appropriate mathematical model type, determining the model variables and parameters, establishing the system and goal constraints, and formulating the objective function. After solving the model, the results were compared with the bank's current state to plan the transition to a desired state. Finally, the effectiveness of the proposed model was evaluated by 30 bank users and experts. Findings The proposed model enables sensitivity analysis of parameters and the examination of various scenarios. If there are practical limitations, alternative optimized solutions can be prepared to implement the most feasible solution vector and outline corrective actions in a roadmap (Anvary Rostamy & Nematolahi Ardestani, 2003). Although these alternatives slightly deviate from the optimal initial value, they offer the most practical benefits. The model was evaluated across seven dimensions: flexibility, usability, impact, acceptability of variables, accuracy of goal and systemic constraints, and ease of understanding and application by 30 banking experts. The evaluation results confirmed the flexibility, practicality, and suitability of the considered ratios and variables, as well as the appropriateness of the model's goal and systematic constraints at a 95% confidence level. Conclusion & Results This research aimed at designing an optimal mathematical model for managing bank liquidity. The goal programming model was used due to the multitude and contradictions among decision-making criteria. The model was developed using data from a specialized bank sample and then was tested. The effectiveness of the proposed model was reviewed, scored, and approved by 30 banking professionals and experts. The results of solving the models enable the formulation of a road plan and allow managers to immediately see the results of any potential changes in the model. This modeling tool also enables managers to create future scenarios and plan corrective actions. Given the similarities in the Iranian banking operations, despite some differences, the application of the proposed conceptual and operational model with minor modifications will provide the necessary conditions for more efficient liquidity management in other banks. The authors suggest considering off-balance sheet items and economic and political shocks and risks in the model completion and modeling using fuzzy data. Future research could measure the bank's liquidity sensitivity to changes in the economic market, such as changes in exchange rates, the country's exports and imports, and competitor markets like capital markets and real markets. * Corresponding author

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