Modeling of bank credit risk management using the cost risk model

Yanenkova, Iryna; Nehoda, Yuliia; Drobyazko, Svetlana; Zavhorodnii, Andrii; Berezovska, Lyudmyla Article Modeling of bank credit risk management using the cost risk model Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Yanenkova, Iryna; Nehoda, Yuliia; Drobyazko, Svetlana; Zavhorodnii, Andrii; Berezovska, Lyudmyla (2021) : Modeling of bank credit risk management using the cost risk model, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 5, pp. 1-15, https://doi.org/10.3390/jrfm14050211 This Version is available at: https://hdl.handle.net/10419/239627 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Risk and Financial Management Article Modeling of Bank Credit Risk Management Using the Cost Risk Model Iryna Yanenkova 1, Yuliia Nehoda 2, Svetlana Drobyazko 3,*, Andrii Zavhorodnii 4and Lyudmyla Berezovska 2   Citation: Yanenkova, Iryna, Yuliia Nehoda, Svetlana Drobyazko, Andrii Zavhorodnii, and Lyudmyla Berezovska. 2021. Modeling of Bank Credit Risk Management Using the Cost Risk Model. Journal of Risk and Financial Management 14: 211. https://doi.org/10.3390/jrfm14050211 Academic Editor: Shigeyuki Hamori Received: 30 March 2021 Accepted: 27 April 2021 Published: 7 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Sector of the Digital Economy, NASU Institute for Economics and Forecasting, 26 Panasa Myrnoho St., 01011 Kyiv, Ukraine; [email protected] 2 Department of Finance, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony Str. 15, 03041 Kyiv, Ukraine; [email protected] (Y.N.); ber[email protected] (L.B.) 3The European Academy of Sciences LTD, 71-75 Shelton Street Covent Garden, London WC2H 9JQ, UK 4Department of Economics and Information Technology, Mykolayiv Interregional Institute for the Development Human Rights of the Higher Educational Institution “Open International University of Human Development” Ukraine, 2 Military Str. 22, 54003 Nikolaev, Ukraine; zavhor[email protected] *Correspondence: dr[email protected] Abstract: This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio. Keywords: bank credit risks; credit portfolio; observation; simulation modeling; bank expenses; rating; default 1. Introduction Lending is one of the main types of banking operations, which plays a crucial role in meeting the ever-growing consumer needs of the real economy and contributes to the production and socio-economic development of the country. Its dynamic development and the variety of forms and types of bank credit show that banks have a substantial interest in lending, as a source of high profit, and there is a constant demand from business entities. An increase in the lending volumes contributes to economic growth. Alongside this, the provision of bank loans is a fairly risky activity, and most of the risks associated with it are objectively inherent to the lending process. Therefore, the further development of bank lending depends, to a large extent, on the level and quality of risk management that banks are exposed to during this activity. Over the past few years, the role of bank lending in meeting the needs of business entities has been constantly growing, which increases its influence on the financial results J. Risk Financial Manag. 2021,14, 211. https://doi.org/10.3390/jrfm14050211 https://www.mdpi.com/journal/jrfm J. Risk Financial Manag. 2021,14, 211 2 of 15 of bank activities. Therefore, banks are paying more and more attention to credit risk management, and, in particular, credit and operational risks. Banks must deepen their awareness of the importance of risk management for the development of their business and develop and introduce clear procedures for granting loans using the corresponding tools to develop a risk management system with consideration of the best global experience. Nonetheless, lending continues to develop in many banks based on extensive approaches. The most widespread method of minimizing the risk used by the commercial banks is overstating the interest rate or establishing various types of surcharges (commissions) for using the loan, as a result of which there is a transfer of credit risk onto the responsible borrowers (Wilhelmsson and Zhao 2018). Apart from that, many banks do not have an appropriate level of organization for the monitoring and forecasting of operational risks due to the fact that the risk managers do not see them as a real threat, except for fraudulent actions of the borrowers or bank staff (Drobyazko et al. 2020a; Nosratabadi et al. 2011) . In the course of their activity, commercial banks may find themselves in a situation of high credit risk, which leads to them using systematic methods and approaches to risk identification and forecasting. (Maechler et al. 2007). The lending process is associated with the actions of numerous risk factors that can cause the non-repayment of a loan by a borrower within a specified period. The lending risk of a commercial bank can be minimized by carefully analyzing these factors (Chun and Lejeune 2020). In a previous work (Giordana and Schumacher 2017), it was noted that an appropriate and objective assessment of a borrower is extremely important in the lending process of a bank, which is determined by creditworthiness and level of lending risk. The reliability of this assessment significantly affects the results of specific loan agreements and the efficiency of the lending activity of a bank as a whole. The accuracy of the assessment is also important for a borrower, as a decision to grant a loan and the possible amount depend on it. As indicated in a previous study (Allen and Luciano 2019), a qualitative analysis of the financial indicators of a financial institution allows the bank to obtain the necessary information on credit risk. The analysis shows exactly how the financial institution operated in the previous period, and this gives an assumption about future operations, i.e., whether borrowers will be able to repay credit obligations to a bank. Credit risk assessment for a commercial bank takes into account the legal and economicfinancial aspects of a borrower, the quality, availability, and sufficiency of credit collateral, which is a precondition for a borrower to obtain credit funds, as well as their repayment within a specified period (Richard 2006). For the bank, the purpose of a credit risk assessment is to obtain a qualitative assessment of the activity of a borrower, based on which a decision is made whether to lend or terminate credit relations. In determining the lending risk of a financial institution, the purpose of assessing the financial condition of a borrower is to recognize the possibility of the repayment of debt on the loan from internal sources. Credit risk is often associated with default—the inability or unwillingness of a counterparty to comply with obligations on time and/or in full volume, which leads to violation of the terms of the contract and allows the creditor to begin debt repayment procedures. A comprehensive analysis of the credit risk should not only include an assessment of the probability of bankruptcy of the counterparty, but also consider the occurrence of credit events that lead to the deterioration of the creditworthiness of the borrower or the credit properties of financial instruments. The existence of risk is not always a cause for concern. Risks are considered justified if they are clear, controlled, and can be measured, and correspond to the bank’s ability to quickly respond to negative circumstances. Unjustified risk may arise from intentional or unintentional actions. If the risks are unjustified, the risk managers should engage with the management and supervisory board of the bank, encouraging them to mitigate or eliminate these unjustified risks. The measures that the bank should implement, in this case, include the reduction in the sums at risk and an increase in the capital or strengthening of the risk management processes (Maechler et al. 2007). J. Risk Financial Manag. 2021,14, 211 3 of 15 The specified reasons and realia of bank lending practice demonstrate the need for considerable improvements to the processes of credit risk management in the banking sector, considering the introduction of appropriate models and granting them the status of system integrators and protectors of credit fund losses. In this context, the study of forms, methods, and tools for managing bank lending risks, and the development of methodological foundations for the control of bank lending credit risks is relevant both from a scientific and practical point of view. The research objective is focused on the improvement and development of methodological support and practical recommendations for lowering operational risk levels in crediting commercial banks and the improvement of the value-at-risk (VaR) methodology for the assessment of credit risk. 2. Materials and Methods Many business entities operate in the conditions of a market economy, and a commercial bank is one such example, so in terms of its activities, the goal of a bank is to maximize profits. As noted by Duffie and Pan (1997) and Michta (2005), all credit institutions are aware of the need to analyze and manage risks in the course of credit operations over time. Researchers such as Chun and Lejeune (2020) and Moore and Zhou (2013) insist that a commercial bank should develop a risk profile to identify the risks that threaten the bank and the level of risk it can tolerate. Such measures are required to balance the yield-to-risk ratio at such a level that the bank does not default. In the works of Steiner et al. (2006), it is noted that an important step in determining the risk profile is to control the risks and to find the means that can keep them at the desired level. This is required to assess all the risks that may arise from attempts to increase profitability, the search for business line and product line expansion, as well as increase the customer base. Segoviano and Goodhart (2009) define three fundamental stages in the risk management system: (1) risk analysis (risk identification and risk assessment); (2) risk control (credit risk monitoring); (3) risk minimization (risk mitigation). The definition of “risk analysis” consists of the initial identification of credit risk, as well as of its subsequent evaluation. In fact, credit risk analysis is about identifying parameters that increase or directly reduce a particular type of risk when performing specific banking transactions (Turnbull 2018). It follows that credit risk assessment is the measurement of its level by qualitative and quantitative methods. Ronald and Sundaresan (2000) note that the magnitude of credit banking risk is nothing more than an estimate of the risk in value that can be expressed as the maximum amount directly lost by a bank as a result of varying risk factors over a period of time. After identifying the risk as a threat of bank loss, where the level of risk is determined by the size of the loss, we can analyze the probabilistic meaning of this concept. In conclusion, credit risk can be determined from a loss analysis with a sufficient degree of accuracy (Arici et al. 2019;Drobyazko 2020). As a result, the major part of credit risk assessment is based on probability theory, which is a systematic statistical method of determining the probability that an event may occur in the future, expressed as a percentage. In the methodological aspect, the authors state the same type of scientifically based methods for assessing the creditworthiness of the borrower and the risk of repayment of loans and making decisions regarding the possibility and conditions of lending; this can lead to a deterioration in the quality of the bank’s loan portfolio. There is a range of special methods that can be used in assessing the risk of lending to financial institutions, the most common of which are: the statistical method, the method of analyzing the feasibility of expenses, the method of expert estimates, the analytical method and the method of using analogues. The assessment of the industry risk of financial institutions, assessment of client risks, and calculation of competitive risks are examples of the statistical method’s use in practice. This method enables the analysis and assessment of scenarios for the implementation of specific activity of financial institutions. J. Risk Financial Manag. 2021,14, 211 4 of 15 In the banking sector, the average expected value and standard deviation indicators are widely used as a criterion in the qualitative assessment of the risk of crediting financial institutions’ activities. The method of cost and benefit analysis, which are grounded in the fact that there are expenditures for each particular activity of financial institutions, as well as for their elements, has different levels of risk (Steiner et al. 2006)—that is, the degree of risk of different activities of one financial institution and the degree of risk of its individual elements of expenses within the same activity of the financial institution are different. Based on the expert assessments, one efficiently solves the following important risk analysis tasks: identification of sources and causes of risk, identification of all possible risks, identification of areas of risk reduction, creation of scenarios in case of risk realization, forecasting of competitors’ actions, etc. The heuristic methods include widely known methods, which are used in international practice: the BERI methodology and the methodology of the Swiss Banking Corporation (Slovik and Cournède 2011). These have a global nature and enable the possibility to determine the degree of risk of the entire economy, but not a specific financial institution. The analytical method is a kind of combination of statistical assessment and the principles of expert analysis. The work of Twala (2010) defines this method as a system of statistical assessments based on a preliminary expert selection of the key parameters for further analysis of the impact of factors on them. The analogue method is used when other risk assessment methods are unacceptable. This method is most useful when it is necessary to identify the degree of risk of any innovative activity of a financial institution in the absence of a basis for comparison, but this method usually takes into account only one activity—innovation (Yan et al. 2021). In the present study, an integrated approach will be used, which will be formed on the elements of analytical and statistical methods for expansion of the scope of the value-at-risk (VaR) methodology of assessing the credit risk of the bank and assessment of their impact on the likelihood of bankruptcy of a commercial bank. The hypothesis of the study is that the complexity of identifying and processing different types of credit risks in the activity of a commercial bank requires a symbiotic use of modern and proven methods and techniques. However, the authors consider it necessary to introduce into the complex practice of the model risk assessment both valueat-risk (VaR) and neuro-fuzzy methods. It is their combination that can take the quality and accuracy of credit risk identification and prevention for banks to a new level and provide an opportunity to minimize both risk losses and operating costs for technologies and risk management processes. 3. Results The authors believe that the bank’s credit risk management system (Figure 1) should ensure flexibility, integrity and integration cooperation of organizational, institutional and methodological components, as well as coordination of the activities of the corresponding responsibility centers of the bank, combined with the target and functional subsystems, under formation and support of the optimal system by the structure and quality of the bank’s loan portfolio and the performance of banking activities following the main priority objectives of the bank’s loan policy. The authors deepened the methodological approach to classification of the borrower’s credit risk factors, according to which they are identified at the micro and macro levels with simultaneous distribution of the factors of general action and factors specific to a certain borrower. Such an approach provides for an increase in the speed of management decisions due to the formation of a two-level system for managing the bank’s credit risk; this makes it possible to differentiate management tools into: general (by all components of the loan portfolio to eliminate the negative impact of common factors) and specific (for the country; the localization region of borrowers’ business interests; the type of economic activity and industry; the borrower’s business, product or service) (Protter 1990). J. Risk Financial Manag. 2021,14, 211 5 of 15 J. Risk Financial Manag. 2021, 14, x FOR PEER REVIEW 5 of 16 with simultaneous distribution of the factors of general action and factors specific to a certain borrower. Such an approach provides for an increase in the speed of management decisions due to the formation of a two-level system for managing the bank’s credit risk; this makes it possible to differentiate management tools into: general (by all components of the loan portfolio to eliminate the negative impact of common factors) and specific (for the country; the localization region of borrowers’ business interests; the type of economic activity and industry; the borrower’s business, product or service) (Protter 1990). Figure 1. The system of the bank’s credit risk management. (Adapted with permission from Arici et al. 2019;Chun and Lejeune 2020). Thus, for example, the factors specific to a certain type of economic activity or industry are found in basic properties such as (Gupta and Chaudhry 2019): (1) the borrower’s resistance to macroshocks—i.e., a change in the profitability of activities and sales volumes under the influence of negative changes in macroeconomic factors, which is determined by the technical and technological characteristics of the organization of activities in a certain J. Risk Financial Manag. 2021,14, 211 6 of 15 industry with the corresponding spending structure; (2) the level of dependence of the borrower’s default losses on the average liquidity of the property of enterprises in the industry with consideration of the related collateral concentration; (3) correlation between the probability of defaults of the borrowers which have the same industry affiliation, which increases when crisis events occur in the economy. 3.1. Value-at-Risk (VaR) Methodology for Conducting a Bank Credit Risk Assessment The purpose of the study was to assess the default risk of a commercial bank credit portfolio using the VaR methodology. The VaR methodology is nothing more than an estimate of the magnitude of the losses directly expressed in the base currency, and which, with a set confidence probabilities, will not exacerbate the loss of the credit portfolio over a certain period of time: PLossp≺VaR=p(1) where Lossp—the amount of losses by a portfolio, p—a set confidence probabilities. The confidence level as well as the length of time for which this indicator should be calculated are the main parameters in calculating the VaR estimate. The confidence level can be selected in accordance with regulations established by regulators or a direct risk attitude (Bedin et al. 2019). The time over which a commercial bank credit portfolio has not changed significantly is used as a time horizon. To calculate the quantitative estimate of the risk arising from bank credit operations, it is necessary to first build an empirical function of distributing monetary losses for the selected credit portfolio and then to calculate the VaR directly as a quantile of the required order. The most popular methods of calculating the VaR are: Monte Carlo experiment method, analytical method, as well as historical modeling method (Cochrane 2011 ; Drobyazko et al. 2020b). In accordance with VaR methodology, credit risk is the maximum possible loss of a bank for a specific credit portfolio, with a given level of confidence probability. The maximum losses can be divided into what the bank expects (Expected Loss,ELp), and those that are unexpected (Unexpected Loss,ULp): VaRa=Expected_Loss +Unexpected_Loss (2) The calculation of expected and unexpected losses is actually the main task in the analysis and assessment of credit risk for a portfolio (Richard 2006). This implies that the expected bank losses are nothing more than the average credit loss incurred by a borrower in defaulting in full. The unexpected losses show the deviation of the expected average loss. 3.1.1. The Calculation of Expected Losses (Expected Loss, ELp) The expected losses are nothing more than a mathematical expectation of losses in the event of the borrower defaulting on its obligations. The following formula calculates the expected portfolio losses for each borrower: ELp= N ∑ i=1 (PDi×CDi×(1−RRi)) (3) where PDi (probability of default)—the probability that the default of the borrower will occur— namely, the likelihood that the counterparty will not fulfill the terms of the contract in a certain period; CDi(credit exposure)—the value of assets that are at risk at the time of default; RRi (recovery rate)—the level of compensation for the losses incurred and the share of debt that can be repaid in the event of borrower default through the performance of guarantees, collateral, etc. J. Risk Financial Manag. 2021,14, 211 7 of 15 Assessment of the likelihood of bankruptcy of each borrower is one of the biggest problems in calculating the level of expected losses. Many studies based on models of discriminant analysis, neural networks, rating systems, as well as logit and profit models are devoted to this topic. The metric PDican be found in two stages: (1) During the first stage, information on statistics for credit operations of a commercial bank should be collected, and a complete analysis of the factors that directly affect the likelihood that the borrower may not repay the credit should be the next step. Regression analysis is an effective research tool at this time (Frahm and Huber 2019). As a result, a specific logit model should be constructed, which shows the direct dependence of the individual borrower default case on certain available characteristics, where the default data of commercial bank borrowers over the last three years are taken as a basis. (2) The second stage is characterized by the choice of a particular method that will be the basis for assessing the probability of default of each borrower. As a result of the estimated probability of default of each borrower, the expected losses will be calculated. 3.1.2. The Calculation of Unexpected Losses (Unexpected Loss, ULp) The unexpected losses are nothing more than a direct deviation from the level of average expected losses, and also determine the degree of risk of the credit portfolio. The calculation of unexpected losses is made by Formula (4): Unexpected_Loss =VaRa−Expected_Loss (4) Within this article, it was decided to select the 98.8% confidence level for calculating the VaR, which satisfies the Basel Committee requirements and its recommendations. As a rule, the time horizon of calculating the VaR for commercial bank credit portfolios is one year. You cannot determine if the distribution of losses for the credit portfolio can be directly referred to one of the known classes of distributions. The losses for the credit portfolio cannot exceed 100%, and, compared to the normal distribution, their distribution can rather often have “heavy tail areas”. Such default data are aggregated into a portfolio, resulting in an aggregate estimate of portfolio losses. Initially, a large number of simulations of the level, the aggregate costs, are generated, on the basis of which the next step will be to build an empirical distribution of losses for the portfolio (Krkoska and Schenk-Hoppé2019; Pacelli and Azzollini 2011). 3.2. Conducting a Computational Experiment and Analyzing the Results We will conduct a risk analysis of the bank credit portfolio using the above scheme. The aggregate of outstanding balances for active bank credit transactions at a particular date is a credit portfolio. A corporate credit portfolio of a commercial bank, which is an aggregate of credits to individuals, will be considered. At the date under review, the bank credit portfolio consisted of 100 credits totaling EUR 57 million. The empirical basis of the study was the lending activities of the joint-stock commercial bank “Monobank” (Ukraine). The selection of this bank is justified by the fact that this bank uses the model of a “virtual bank” and provides banking services through a digital format of interaction with customers. The bank “Monobank” has the greatest need to develop a system for assessing and forecasting credit risks, especially on the basis of neuro-fuzzy technologies in the VaR risk model. 3.2.1. The Calculation of Expected Losses (Expected Loss, ELp) The first step was the task of analyzing statistics of direct credit operations of a commercial bank for previous periods for the analysis and assessment of credit risk. In the course of the study, the data on credits issued by a commercial bank to borrowers over a J. Risk Financial Manag. 2021,14, 211 8 of 15 one-year period were processed. The sample size was 100 issued credits (Maechler et al. 2007). We had the following information on each specific borrower: - the amount of credit received; - the internal credit rating of a borrower; - the presence/absence of credit history; - data on the financial statements of a borrower. Additionally, information about defaults on obligations was provided. In this study, the following events were considered a default: (1) credits with overdue principal debts and/or interest arrears exceeding 30 days; (2) credits to borrowers for which bankruptcy proceedings have been initiated or the process of liquidation is in progress; (3) credits to borrowers known for the facts of significant defaults on obligations to their counterparties. In the commercial bank, which is considered as an example, the rating system of analysis and assessment is used as a basis, making it possible to check the creditworthiness of a borrower. This methodology is based on both qualitative and quantitative characteristics of customers (Twala 2010). These include financial statements as well as the credit history of a borrower and more. You cannot disclose the actual list of parameters and weight ratios that make up the rating. At the end of the analysis, each borrower is assigned a certain credit rating and is included in a particular risk group (Mishura 2008). Totally, there were five credit risk groups defined: A, B, C, D and E, where group A is made up of the most reliable borrowers and group E is made up of the riskiest ones. It is indicated in Slovik and Cournède’s work (2011) that credit history is a history of financial relations between the borrower and banks, which directly indicates the repayment of obligations the former has assumed. By using the default rate of a particular credit risk group, you can calculate the probability of default (Angelini et al. 2008). For example, if we consider the group of borrowers with the highest rating A, and also assume that within the group of borrowers NA there are the ones NDA that have not repaid their obligations to the bank, then as a result we will have an estimate of the probability of default of the group of borrowers by the formula of Protter (1990): P(D)A=NDA NA (5) where P(D)A—estimation of the probability of default of borrowers with a certain rating; NDA—the number of defaults of borrowers in the group; NA—the total number of companies in the group. After carrying out the procedure for each risk group, we directly obtained a table of the ratio of the level of default and the ratings of borrowers. At the next stage, based on the data obtained, we solved the problem of calculating the expected losses of the analyzed credit portfolio (Allen and Luciano 2019). The expected losses are calculated by the formula: ELp= N ∑ i=1 (PDi×CDi×(1−RRi)),i=1 . . . . . . 100 (6) where: PDi (probability of default)—the probability that the borrower will not fulfill the terms of the credit agreement in due time. Each borrower is given a credit in accordance with the rating he has; CDi (credit exposure)—the value of assets at risk at the time of default. However, due to the lack of more detailed data, in this study, only the amount of current debt owed by the i-th borrower was used; RRi (recovery rate)—the rate of recovery, or the proportion of debt that, in the event of default of the borrower, can be repaid by executing guarantees, collateral, etc. A certain rate of recovery has been set for each category by way of peer review (Table 1; Table 2). J. Risk Financial Manag. 2021,14, 211 15 of 15 Bedin, Andrea, Monica Billio, Michele Costola, and Loriana Pelizzon. 2019. Credit Scoring in SME Asset-Backed Securities: An Italian Case Study. 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