1 Introduction
Banking is a key component of the modern economy, and its efficiency and sustainability are essential for the financial stability and development of the country. The complexity and dynamism of this industry require banks to constantly monitor, analyse, and make informed decisions. In recent years, more and more attention has been paid to the use of mathematical models for the analysis of banking activities, and one of these models is the system dynamics model. The system dynamics model is a tool for modelling and analysing complex systems where the interaction of various components influences the dynamics of the system over time. In the context of banking, the use of a mathematical model of system dynamics allows taking into account the relationship and dynamics of various aspects, such as the loan portfolio, risk level, liquidity, and capital
[1].
The use of the system dynamics model in the analysis of banking activities can provide banks with new tools and insights for deeper analysis, forecasting, and management of various aspects and risks. This allows banks to make more informed decisions, increase the efficiency and sustainability of their activities, and comply with international accounting and reporting standards.
According to Bektenova and Arykov
[2], system dynamics is a powerful tool for analysing and modelling banking activities. It takes into account the complex interrelationships and dynamics of factors affecting banking operations and provides the ability to predict risks, manage liquidity, and assess the long-term impacts of economic conditions on the banking sector.
Japarov
[3] emphasised that system dynamics models can help banks overcome the challenges of managing risk and optimising operations. This approach makes it possible to analyse the impact of changes in economic conditions on the risk and profitability of the banking sector. System dynamics allows simulating various scenarios, including economic growth and recession, and evaluating their impact on the financial condition of the bank.
In the study by Kurmankozhoeva
[4], the author emphasises the importance of using a mathematical model of system dynamics for credit risk analysis and liquidity management. System dynamics takes into account the dynamic nature of credit risks and their relationship with other factors such as economic conditions and financial performance. This allows banks to develop effective risk management strategies, prevent potential problems, and improve financial stability.
Seitova and Tynychkyzy
[5] argued that system dynamics can be used to investigate the causes and consequences of banking crises. This allows simulating different scenarios and studying how financial and macroeconomic factors can lead to instability in the banking system. Analysis of system dynamics helps to identify vulnerabilities and warn against potential crises, as well as develop measures to prevent or mitigate their consequences.
In their work, Nezametdinova and Renat
[6] discussed the importance of using system dynamics models to analyse and predict the behaviour of banks. This allows a better understanding of the relationship between various factors, such as credit risk, capital levels, and returns. Analysing system dynamics enables the development of risk management and performance improvement strategies and provides the tools to make informed decisions over the long term
[7].
In general, with the help of the system dynamics model in the analysis of banking activities, banks are able to understand and manage the dynamics and risks. This contributes to informed decision-making, streamlining operations, and ensuring resilience in a rapidly changing economic environment.
The purpose of this study is to analyse banking activities using a mathematical model of system dynamics and assess the impact of changes in economic conditions on the bank's loan portfolio, risk level, and profitability. The research objectives are:
● To develop a system dynamics model to assess the impact of economic conditions on a bank's loan portfolio, risk level, and profitability. This involves modelling credit risk, liquidity, capital levels, etc. under different economic scenarios
[8].
● To use the system dynamics model to forecast and simulate the long-term effects of lending, investment, and regulatory decisions on a bank's financial sustainability and risk exposure.
● To identify how changes in specific banking variables like interest rates, reserve requirements, and capital ratios influence the stability and profitability of banks over time.
● To evaluate the usefulness of system dynamics for bank management in improving decision-making, optimising operations, and adapting to economic changes compared to traditional modelling approaches.
● To analyse the causes and transmission mechanisms of banking crises using a system dynamics model that incorporates financial sector interactions and macroeconomic linkages.
● To develop an integrated risk management framework for banks combining system dynamics with other methods like statistical analysis, optimisation, and AI to enable better quantification and mitigation of risks.
2 Literature Review
System dynamics is a method for modelling and analysing complex dynamic systems that was developed in the 1950s by Professor Forrester
[9] at the Massachusetts Institute of Technology and presented in the book "Industrial dynamics". In it, the author described his studies of dynamic processes in industrial systems and also presented the basics of the methodology of system dynamics. A review of the literature shows that the use of system dynamics in banking analysis is an active and growing area of research. A number of works use modelling of the dynamics of banking systems, taking into account macroeconomic conditions, regulation, customer behaviour, and other factors.
Arquitt and Johnstone
[10] also used system dynamics to model banking activities, where they used a model that takes into account changes in bank balance sheets, including lending and deposit taking, as well as changes in interest rates. They showed how these factors affect the liquidity and profitability of banking activities and how risk management can affect these factors.
In [
11], the authors studied the interaction of various factors influencing banking activity. In particular, they assessed the impact of macroeconomic factors on the financial performance of banks, such as assets and equity. Using a system dynamics model, the authors showed that macroeconomic changes can have a significant impact on financial performance. However, the researchers emphasised that his work has some limitations, including limited data available and a simplified model. However, they believe that using a system dynamics model can be a useful tool for analysis in various economic scenarios.
In [
12], the authors used system dynamics to analyse credit risk management. The paper describes a model that takes into account the influence of various risk factors in the banking sector, including credit risk, operational risk, and market risk. Using a system dynamics model, they show how changes in one area can affect others, which can lead to unexpected consequences. Mazrae, et al.
[13] analysed the use of system dynamics for strategic planning, in which a system dynamics model was applied to assess the relationship between key variables such as lending volume, interest rate, reserves, and deposits.
In [
14], banking activity was analysed using system dynamics, and it was concluded that modelling can help banks more accurately assess risks and returns and make informed decisions in the face of market uncertainty and volatility. However, the authors note that the application of system dynamics in the banking sector requires caution and accuracy, since even small errors in modelling can lead to negative consequences. It is also necessary to take into account various factors, such as changes in laws and regulations, which may have a significant impact on banking activities.
In recent years, increasing attention has been paid to the use of system dynamics for the analysis of banking systems in the context of the requirements of the International Financial Reporting Standards (IFRS 9). For example, [
15] is devoted to modelling the impact of credit risk on the financial performance of banks, taking into account the requirements of the international standard. Similar studies have been carried out by other authors, such as Bastan, et al.
[16].
The research emphasises the significance of risk management and policy implications in banking, particularly in the context of changing economic conditions. For bank regulators and policymakers, the research recommends the implementation of countercyclical capital buffers that adjust with economic cycles, periodic stress tests for bank solvency, and restrictions on high-risk mortgages when household leverage is high. To improve financial stability, it is recommended to increase bank capital requirements and provisioning rules during periods of economic overheating. During systemic risk build-up, it is recommended to apply stricter limits on bank credit growth, leverage ratios, and liquidity. This research offers valuable guidance for implementing proactive risk management strategies and systemic risk mitigation policies that can improve stability and resilience in the banking sector.
A review of existing research shows that system dynamics is an effective tool for analysing the activities of banks and can be used to assess the risks associated with banking operations and to analyse the impact of macroeconomic conditions and regulation on the banking system as a whole.
3 Materials and Methods
Conducting a scientific study to study the topical issues of using a mathematical model in the banking sector was carried out using methods that revealed the content of the object. The analytical method was used for a detailed analysis and analysis of various aspects of banking activities, including the loan portfolio, risk level, and profitability. The functional method allowed us to explore the relationship between various functions and determine their contribution to the overall efficiency of banking. Using the method of systems analysis, it was possible to study the impact of changes in economic conditions on various components of the banking system and understand the long-term effects of these changes. The deduction method was used to identify general patterns and principles in banking based on case studies and observations. The synthesis method was used to combine various components and factors of banking activity for a holistic understanding of the model and concepts. The comparison method helped to determine the most effective approaches and methods in banking.
Vensim software was chosen to analyse banking activities in the Kyrgyz Republic using the system dynamics method. This software provides the ability to build and analyse system dynamics models using a graphical interface. Gross domestic product (GDP), capital, loans, deposits, and income were chosen as the basic indicators for building a model of banking activity. The interaction between them is considered using various parameters and ratios, such as the interest rate on loans and deposits, credit risks, and profitability. The developed banking risk model consists of several components. First, the borrower's default probability is estimated using statistical analysis methods. For this, historical data on the defaults of borrowers as well as the current economic situation are taken into account. Second, a loan loss provisioning ratio is calculated, which reflects the bank's ability to cover potential loan losses.
The model takes into account several factors that can affect the potential losses of a commercial bank. The first factor is the growth of the loan portfolio. The more loans a bank issues, the higher the risks associated with the insolvency of borrowers and potential losses. The model uses the variable Credit Portfolio, which reflects the size of the loan portfolio. The second factor is the loss if the borrower defaults. The model uses the variable "Loan default rate", which is the percentage of losses that the bank will incur if the borrower defaults. This parameter can be estimated based on the classification of the loan and other factors related to the financial situation of the borrower. The third factor is the level of provisioning after loans are issued. The model uses the "Reserve for loans" variable, which reflects the amount of reserves that need to be created after the issuance of loans. This setting can help reduce a bank's risks and increase its ability to absorb potential losses. The fourth factor is the factor of economic activity, which reflects the current economic situation. The model uses the variable "Economic activity", which can be used to take into account the current economic situation and its impact on risk. The fifth factor, GDP, reflects the economic growth in a country, which affects the economic environment and hence economic activity.
The research is limited in scope to modelling only certain key banking variables. Many other complex dynamics and risk factors remain unexplored. The data used to build and validate the model constrains its accuracy and predictive capability. The simplifying assumptions in the system dynamics model also limit the representation of real-world complexities. Future studies could expand the modelling to include additional economic and internal banking variables as well as utilise more robust data sets.
4 Results
As already mentioned, the mathematical model of system dynamics is a set of equations and relationships that describe the behaviour and relationships of various variables in the system. It allows taking into account time delays, feedbacks, and cumulative effects, which reflect the real complexity and dynamics of banking activities. An analysis of banking activities using a system dynamics model makes it possible to investigate the impact of changes in the external environment on various aspects of banking activities. For example, it is possible to model the effects of changes in interest rates, inflation, unemployment, and other macroeconomic factors on a bank's loan portfolio, credit default risk, liquidity, and profit. By conducting various scenario analyses and experiments with the system dynamics model, it is possible to assess the long-term effects and the extent to which changes in the economic environment will affect banking activities. This helps banks make informed decisions and develop strategies for risk management, capital optimisation and profitability
[17].
The creation of threads and variables led to the definition of the relationship between the selected parameters. This made it possible to determine the causal relationship between them and how a change in one value affects another. Then the data of the model were determined, such as the initial values of the flows, the rate of change, coefficients, inputs, and outputs.
The main relationships between flows and variables are shown in the following diagrams (Figure 1).
Figure 1 Relationships between threads and variablesSourse: Developed by the authors based on Japarov[3], Kurmankozhoeva[4], Seitova and Tynychkyzy[5], Nezametdinova and Renat[6], Radzicki[17]. |
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Vensim software was used, which allows creating dynamic systems using blocks to represent flows and other elements of the system. The model used blocks to represent selected flows and their relationships, as well as blocks to represent model parameters.
The following assumptions were used as scenarios for modelling:
1. The baseline scenario is the GDP growth of the Kyrgyz Republic at the level of 3% per year.
2. Scenario of growth of the country's economy up to 5%.
3. Scenario of growth of the country's economy up to 7%.
4. The stagnation scenario for the development of the economy is at a level of 0%.
According to the actual data used in the model, the Kyrgyz Republic will have a positive growth rate until 2050. In the baseline scenario, the Kyrgyz Republic will reach the milestone of 1 trillion soms in 2028 (Figure 2). If the economy grows to 5%, or an ambitious 7%, there could be a significant surplus in economic activity compared to the baseline.
Figure 2 Forecasts of GDP developmentSourse: Developed by the authors based on Japarov[3], Kurmankozhoeva[4], Seitova and Tynychkyzy[5], Nezametdinova and Renat[6], Ongalo and Wanjare[18]. |
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In International Financial Reporting Standard IFRS 9, economic activity is one of the key parameters used to determine the probability of a client default and the corresponding amount of risk. In general, economic activity in IFRS 9 plays an important role in determining financial strength and solvency. This factor can help the bank assess the risks associated with lending and provide additional information for making investment decisions
[18]. The most obvious indicator of the relationship between economic activity and credit risk is the deterioration in the business activity of bank customers' borrowers. When the economic environment deteriorates, entrepreneurs find it difficult to sell their goods and services, which affects cash flow. In turn, the decline in income leads to problems with servicing their debt obligations to financial institutions. For a bank, the financial difficulties of its borrowers are expressed in the need to create additional reserves to cover losses. The model illustrates these processes quite clearly in
Figure 3(a), which shows the bank's overall provisioning needs for classified loans such as subprime, doubtful, and bad.
As can be seen from Figure 3(a), in the baseline scenario, the amount of special reserves is in the middle between the GDP growth and fall scenarios. This means that with the growth of the economy, the bank will be forced to form smaller reserves for problem loans, and in the opposite scenario, with the recovery of the economy, the bank will be forced to form larger reserves for the loan portfolio.
In banking, provisioning seems to be a direct factor influencing a bank's bottom line. For shareholders, regulators, and other stakeholders of the bank, the final parameter is net profit. Accordingly, as indicated above, economic activity that directly affects the provisioning parameter has a direct impact on the formation of the net profit of a financial organisation (Figure 3(b)).
Figure 3 Indicator (a) "The amount of special reserves" and (b) "Net profit"Sourse: Developed by the authors based on Japarov[3], Kurmankozhoeva[4], Seitova and Tynychkyzy[5], Nezametdinova and Renat[6], Ongalo and Wanjare[18]. |
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As can be seen from Figure 3(b), the direct impact of the economic activity parameter on the performance of a conditional bank should be noted. If, according to the baseline scenario, in 2030, with a 3% growth of the economy, the bank's net profit will reach 85 million soms, with a 5% growth it will increase to 106 million soms (an increase of 124%), and with a 7% growth of GDP it can reach 113 mln. soms (growth by 132% compared to the baseline scenario). In turn, in the event of a deterioration in the situation in the economy, which will directly affect the clients of the bank's borrowers, the profit by the specified year may, according to forecasts, fall to 67 million soms (75% below the baseline).
Thus, it can be seen that economic activity has a significant impact on the financial position and profitability of the bank. If the economic situation improves, the bank can count on an increase in profits, which will affect its financial stability and development. However, if economic activity slows down, it will be more difficult for a bank to maintain a high level of profitability, and it may need to take additional measures to maintain its financial stability.
Return on equity (ROE) reflects a pattern similar to that of net income, in the sense that an increase in earnings is cumulatively reflected in the growth of a bank's capital. Accordingly, the higher the net profit attributable to shareholders, the higher the ROE (Figure 4(a)).
Figure 4 Indicator: (a) "ROE" and (b) "ROA"Sourse: Developed by the authors based on Japarov[3], Kurmankozhoeva[4], Seitova and Tynychkyzy[5], Nezametdinova and Renat[6], Ongalo and Wanjare[18]. |
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However, ROE not only reflects the dynamics of net profit but also takes into account the amount of capital spent on obtaining this profit. This means that ROE shows how effectively the bank uses its capital to generate profit. High net income may be a good indicator of the financial model chosen by the bank, however, if a large amount of capital is used to generate it, the ROE may be low. On the other hand, a bank may have a more modest net income, but with efficient use of capital, the ROE can be high. Thus, ROE is one of the key performance indicators of bank capital management, which measures how well the bank uses its resources to create value for shareholders.
The multidirectional dynamics of return on assets (ROA) is primarily due to the limited use in the model of the entire possible volume of bank balance sheet items, in particular asset items. At the same time, the absence of any asset classes in the balance sheet, such as loans and investments, was taken as the main reason for exclusion from the model. In this case, the absence of other assets definitely skews its growth rate, which ultimately leads to lower ROA.
It should be noted that the return on assets is an important indicator of the bank's financial stability and reflects its ability to generate income from all existing assets, and not just from the loan portfolio and investments
[19]. If a bank is effectively using its assets, including, for example, deposits and other financial instruments, its ROA may be higher.
In addition, it must be taken into account that ROA is the ratio of net income to average assets over a certain period of time. If a bank grows its loan portfolio and investments faster than other assets, this can lead to a decrease in ROA even if net income remains stable (Figure 4(b)).
A complete understanding of a bank's financial position requires an analysis not only of ROE and ROA, but also of other key indicators such as loan portfolio performance, risk level, and asset quality.
In this regard, the model considers the aggregate variable "Efficiency" which clearly reflects the impact of crisis situations that arise both at the macroeconomic level of the country and at the micro level associated with the activities of an individual business entity. It is going about COVID-19, which the world faced in 2020, which also led to the lockdown of the economy of the Kyrgyz Republic, which in turn led to the paralysis of all activities in the country. The negative consequences of the pandemic directly affected banking activities, which led to significant losses for financial institutions (Figure 5(a)).
Figure 5 Indicator: (a) "Efficiency"; (b) "Control Quality" and (c) "Reliability of banking"Sourse: Developed by the authors based on Japarov[3], Kurmankozhoeva[4], Seitova and Tynychkyzy[5], Nezametdinova and Renat[6], Ongalo and Wanjare[18]. |
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As can be seen from Figure 5(a), in 2020, there will be a decrease in the efficiency of banking activities. This drop in efficiency in 2020 is due to a number of factors, such as a decline in economic activity and income levels, a deterioration in the quality of the loan portfolio, and increased uncertainty in the economy as a whole caused by the pandemic.
Despite increased uncertainty, banks were able to adapt to the new environment and implement cost-cutting and risk-mitigating strategies that helped stabilise the situation in subsequent periods. The model shows that banking performance is starting to improve again in 2021, driven by a gradual recovery in the economy and increased customer confidence in the future. In general, the model demonstrates that banks in a crisis can successfully withstand adverse factors and remain stable in the long term.
Human behaviour and psychology can significantly influence risk-taking and decision-making in banking. Individuals and groups within banks often exhibit heuristic biases and varying risk appetites that deviate from perfect rationality. For example, the availability bias may lead bankers to underestimate the likelihood of rare events, such as financial crises, based on recent experiences. Overconfidence and the illusion of control can cause excessive risk-taking. Herding behaviour and groupthink among bank executives can propagate an excessive risk appetite across an organisation. Principal-agent problems emerge when bankers' incentives are misaligned with the bank's best interests, particularly when compensation rewards short-term profits over long-term risks. Risk managers may lack independence if their career prospects depend on business line managers. Behavioural factors, such as cognitive biases, incentives, motivations, ethics, and group dynamics, can contribute to poor judgement and excessive risk-taking in banking. It is crucial to identify and account for human psychology in order to effectively manage risk.
A pandemic is an unpredictable event according to the Black Swan theory and cannot be controlled by any known standard tools and methods. According to
Figure 5(b), it can be seen that the quality of management, as an indicator of the effectiveness of the bank's internal management, will show negative indicators in 2020. It is also a consequence of the inability of banks to anticipate and prepare for this crisis, which in turn affects the quality of their management. The situation with the pandemic has led to a change in many of the performance indicators of banks, which were previously familiar and stable. In particular, the transition to remote work, congestion in the banking infrastructure, and other difficulties have affected the quality of bank management in general
[20].
The other most indicative is the "Bank Reliability" ratio (Figure 5(c)). As can be seen from Figure 5c, there is a generally good uptrend in the indicator until 2020, which breaks in 2020. "Banking Soundness" demonstrates a bank's ability to cope with external factors such as economic crises, financial shocks, or other unforeseen events that may affect its operations. The higher the score, the more stable and resilient the bank is and the less likely it is to face financial difficulties in the future. Looking ahead to 2050, the bank's sustainability indicator is unstable, which may be due to the influence of the overall indicator in the form of GDP. In turn, macroeconomic factors such as inflation, political and economic crises, rising interest rates, and other changes in the economy can have an impact.
In the proposed model, achieving the sustainability of banking activities includes scenarios for increasing revenues and reducing costs. First, one of the ways to increase profits is to increase revenues, which leads to an improvement in the rate of return on assets, operating margin, productivity, and income of the bank, which ultimately affects the positive stability of the bank. On the other hand, the reduction in costs is associated with an improvement in the bank's performance, management quality, and profit rating, which also has a positive impact on banking results.
Improved economic conditions lead to increased profits, enhancing financial stability and growth for banks. Conversely, a slowdown in economic activity makes it challenging for banks to maintain high profitability levels, necessitating additional measures to sustain financial stability. The study effectively illustrates the direct impact of economic activity on key banking parameters like provisioning for loans and net profit, underscoring the cause-and-effect relationship between macroeconomic factors and banking performance.
The study presents an innovative approach to analysing banking activities using mathematical modelling and system dynamics. The model integrates economic, financial, and risk factors to assess the impact of lending, investments, and capital decisions on a bank's sustainability. By conducting scenario analyses, the model provides international banks and regulators with deeper insights into optimising operations, adapting to economic changes, and ensuring stability. The research demonstrates advanced analytical capabilities that are valuable for banks worldwide seeking to make data-driven decisions amidst uncertainty. Adopting these sophisticated modelling techniques can help banks better manage risks, increase competitiveness, and increase profitability. The novel framework demonstrates strong research competence and technological leadership that are attractive to an international community seeking the latest innovations in banking and finance. Adopting innovative modelling and simulation techniques enables banks to thrive in unpredictable global markets.
5 Discussion
A scientific study conducted as part of the analysis of banking activity allows us to better understand the significance of using a mathematical model of system dynamics for the state at the present time. The introduction of this model in Kyrgyzstan provides banks with new tools for making informed decisions. The system dynamics model takes into account the complex relationships and dynamics of variables in the banking system, which helps banks better understand and predict the possible consequences of their decisions. As a result, banks are able to optimise their operations, manage risk and liquidity more effectively, and take into account long-term trends and the impact of the economic environment. In general, the advantages of the system dynamics model in the analysis of banking activities lie in the ability to predict long-term effects and conduct scenario analysis, as well as the ability to optimise operations and improve the performance of the bank. However, limitations of a system dynamics model may include the difficulty of building and calibrating the model, the requirement for good data, and the difficulty of interpreting simulation results and making decisions based on those results.
This study shows that a system dynamics model can be an effective tool for predicting and analysing risks, managing liquidity, and assessing the long-term impact of economic conditions on banking operations. Also, the system dynamics model can be applied to study the causes and consequences of banking crises, analyse the impact of macroeconomic factors on the banking system, as well as to develop and test various scenarios and strategies. This helps banks better understand and anticipate the possible risks and challenges they may face and take steps to mitigate them. In the context of the Kyrgyz banking system, the use of a system dynamics model can be particularly useful for loan portfolio analysis and credit risk management. The system dynamics model allows assessing the impact of changes in economic conditions on the bank's loan portfolio and assessing its stability in various scenarios, including scenarios of economic growth and recession. This can help banks make more informed decisions about the allocation of capital and resources.
Scannella
[21] conducted a study aimed at developing a dynamic model of liquid risk management in banking. The method of system dynamics was used to identify the structure of risk formation and present the most effective solution for its management. The study simulated and analysed the results of four scenarios on the model. The results showed that the decrease in the volume of legal deposits and insolvent loans, as well as the increase in the attraction of deposits, have an impact on the liquidity risk of banks. These factors play a key role in liquid risk management, and taking them into account allows banks to make more informed decisions in this area.
It should be noted that the use of the system dynamics method in this study provides significant advantages for analysis and risk management in the banking sector. The system dynamics model takes into account the interrelationships and dynamics of variables such as the volume of legal deposits, insolvent loans, and the attraction of deposits. This contributes to more accurate risk modelling and forecasting and also allows for exploring the impact of various factors on it.
In their studies, Zamula and Kavun
[22] offered an improved approach to managing complex systems that combines fuzzy logic and system dynamics modelling. The banking system and the commercial bank were chosen as the objects of study, representing complex systems. The use of fuzzy logic makes it possible to take into account the uncertainty and fuzziness in the data, and the modelling of system dynamics allows analysing the dynamics and relationships within the system. System dynamics allows modelling and analysing long-term dynamic processes, while artificial intelligence provides tools for developing intelligent control and decision-making. Together, these methods allow for a better understanding and prediction of the functioning of complex systems, such as the banking system. The mathematical models developed for the banking system and commercial banks make it possible to more accurately describe their functioning and interaction with each other
[23, 24].
The results of this study open up prospects for improving the management of complex systems, in particular in the banking sector. The proposed approach, based on fuzzy logic and system dynamics, can also be applied to optimise management, forecasting, and decision-making processes in the banking system. Overall, this approach opens up new horizons for innovation and improvement in the banking sector, helping banks achieve new levels of success.
Nizam, et al.
[25] paid special attention in the study to the development of an integrated model for managing operational risks in the banking sector. As part of the study, a combination of system dynamics and statistical analysis methods was proposed, which made it possible to create a model capable of assessing operational risks and suggesting effective strategies for their management. This approach provides a deeper understanding of operational risk and enables banks to make informed decisions to mitigate risk and improve operational reliability.
It is worth adding that the use of an integrated operational risk management model has significant advantages for the banking sector. First, this approach allows us to more accurately assess operational risks and identify their causes and consequences. Second, the model facilitates the development of effective operational risk management strategies, which may include preventive measures, control procedures, and staff training
[26, 27]. Third, the integrated model enables banks to reduce operational risk losses and improve their financial performance.
Alfadli and Rjoub
[28] studied the impact of macroeconomic factors on the risk and return of the banking sector. Using the system dynamics model, mathematical models have been developed to assess the impact of economic variables such as inflation, interest rates, and unemployment on the risk and return of banks. The results of the study indicate the need to take into account macroeconomic factors when developing risk management strategies and forecasting the performance of banking activities.
Indicators such as inflation, interest rates, and unemployment are key variables that can significantly affect the financial health of banks
[29]. Taking into account these factors allows banks to make informed decisions and develop flexible strategies that adapt to changes in the macroeconomic environment.
Königstorfer and Thalmann
[30] presented an approach to the analysis and management of credit risk in the banking sector using system dynamics and artificial intelligence methods. The use of the system dynamics model made it possible to take into account the complex relationships between various variables in the bank's loan portfolio and assess risks in the long term. With the help of artificial intelligence methods, an intelligent decision support system was developed that can provide recommendations on credit risk management.
The research highlights that the system dynamics model, as used in the study, is effective in analysing and predicting the banking sector's responses to various economic conditions. This is consistent with the findings of Beerbaum and Ahmad
[15] and Nizam, et al.
[25], which also emphasise the model's usefulness in examining the interplay between macroeconomic factors and banking performance. Moreover, this research supports the work of Zamula and Kavun
[22] by demonstrating the usefulness of system dynamics in risk management and decision-making in the banking industry. The study confirms and expands upon previous research, highlighting the practical application of system dynamics in analysing the banking sector.
The results of the study confirm the effectiveness of the proposed approach in the analysis and management of credit risk. The integration of a system dynamics model and an intelligent decision support system allows banks to more effectively assess and manage credit risk. This helps to increase the efficiency of banking activities, reduce losses, and ensure a more stable financial position of the bank.
In general, the application of a mathematical model of system dynamics in the analysis of banking activity is a promising and relevant approach that can help banks make informed decisions, manage risks, and reach a new level of development in a rapidly changing economic environment. Also, the methods and approaches proposed in the studies, such as an integrated operational risk management model, analysis of macroeconomic factors, and the use of artificial intelligence, demonstrate the potential to improve banking management, reduce risks, and increase performance.
6 Conclusions
In this study, an extensive series of scenario analyses was carried out to investigate the impact of changes in economic conditions on the bank's loan portfolio, risk level, and return. The analysis considered various scenarios, including economic growth and recession. The results obtained made it possible to establish that unfavourable economic conditions are accompanied by a significant increase in risks for the bank, while a favourable external environment contributes to their reduction. The study showed that the use of such models allows banks more accurately assess loan losses in different scenarios and take preventive measures to effectively manage risks. The creation of the model also helps banks to determine the necessary reserves to cover potential credit losses, which contributes to financial stability and strengthens the credibility of the bank. The statement is supported that the introduction of the requirements of International Financial Reporting Standards into the practice of banks allows for more accurate accounting of potential credit losses, which leads to a realistic assessment of risks and an improvement in the quality of risk management.
This study can serve as a basis for the further development of more sophisticated system dynamics models for risk analysis in the banking sector. The proposed approach, based on system dynamics and scenario analysis, has great potential for accurate forecasting and effective risk management, as well as for optimising management processes in the banking sector. The development and application of such models can significantly reduce losses, increase efficiency, and ensure stability in a rapidly changing economic environment. This is critical for banks, which need to adapt intelligently to market changes and make informed decisions in order to remain competitive and long-term sustainable. In further research, the application of system dynamics models can be extended to other aspects of banking, such as managing credit risks, operational risks, liquidity risks, and other types of risks. It is also possible to deepen research in the field of modelling the interaction of various banks and their impact on the stability and efficiency of the banking system as a whole.
In conclusion, this study emphasises the importance of using system dynamics modelling for in-depth analysis of banking activities and associated risks. It highlights several key research implications. Firstly, banks can use these advanced simulation techniques to gain deeper insights into areas such as credit risk management, liquidity management, and long-term profit optimisation. Secondly, system dynamics modelling enables banks to quantify their risk exposures, test strategies, and make more informed decisions in the face of uncertainty. Thirdly, regulators can use these models to enhance their oversight of the banking system and assess the impact of macroeconomic conditions. Furthermore, this approach can be extended to model other complex banking variables and relationships beyond the scope of this study. Finally, the adoption of innovative techniques such as system dynamics can give banks a competitive advantage in the complex world of data-driven decision-making, positioning them for success in the dynamic and volatile global financial markets. In conclusion, this research highlights the importance of embracing innovative modelling techniques to excel in the banking landscape, promoting better risk management and strategic decision-making.
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Figure 1 Relationships between threads and variables
