The International Monetary Fund estimates that the coronavirus pandemic could cause a deeper global recession than the economic downturn triggered by the 2008-2009 financial crisis. Uncertainty about the duration of the pandemic and unpredictability of the scale of its consequences provokes the search for ways not only to counter the threats of economic downturn, but also ways to economic growth and promote innovation in the difficult times of the pandemic. With this in mind, this article will analyze the application of cluster analysis to assess the state of banks. The problem of bank clustering is classified as ill-structured. Expert technologies and tools of artificial intelligence should be used to solve it. At the preliminary stage of the research, expert technologies are used, on the basis of which a subset of banking performance indicators is determined, which are essential for the decision on clustering. In addition, the characteristics of the banks that most influence the cluster analysis are selected.The authors propose to use an analysis based on the construction of clusters from the main indicators that characterize the activities of banks instead of analyzing the risks of banks, based on the calculation of economic standards. Prospects for improving methods of regulating the activities of banks in Ukraine are considered.
The current state of Ukraine's economy is characterized by growing economic risks. For the
second year in a row, the world is in a state of quarantine, which is replaced from time to time by
stricter restrictive measures - national or local lockdowns, or, conversely, short-term mitigation when
schools, kindergartens, restaurants, cinemas, etc. open. Banks, as a result of non-repayment of loans
provided by firms for the purchase of goods, significantly reduced profits, reduced return on assets
and capital. The liquidity of the banking sector decreased. In order to maintain the liquidity of banks,
the National Bank began to fill the economy with money supply, which, in turn, provoked rising
inflation. Creating a developed and perfect model of state regulation of the national banking system
occupies a prominent place in the set of tasks for effective management of the banking sector and the
economy as a whole. The search for new tools aimed at improving the efficiency of the banking
system is becoming especially relevant today. Recently, there has been an adaptation of the banking
system to the economic situation through the use of regulatory mechanisms, which are due to the
limitation of risks due to economic standards. The most modern mathematical methods are used to
analyze banking risks. Information is the 21st Century gold, and financial institutions are aware of
this. Armed with machine learning and artificial intelligence technologies, they have the opportunity
to analyze data that originates beyond the bank office. Financial companies collect and store more and
more user data in order to revise their strategies, improve user experience, prevent fraud, and mitigate
risks [
The purpose of the paper is to analyze the possibility of using cluster analysis to assess the state of
banks. In addition, the possibilities of using expert decision-making technologies [
All manifestations of financial instability can be classified into three types: banking crises;
currency crises; crises in financial markets [
Active state intervention remains a bottleneck for the Ukrainian banking system. There are two forms of state regulation: direct - the direct influence of the state on aggregate supply and demand through the creation of the public sector and redistribution of income; indirect - indirect influence of the state through the functioning of credit, financial and tax systems. One way or another, the government is responsible for all the processes taking place in the country. In terms of the studied problem, we note that the regulation of the financial stability of the banking system involves the use of a special set of tools at the stage of responding to negative trends in the functioning of the banking system. This is especially important in times of financial instability in the world economy. Almost all banking operations are risky. There are both general and specific reasons for this.
Analysis of current risks of commercial banks in Ukraine shows that their condition is affected by the general economic crisis, unstable political situation in the country, incomplete formation of the banking system, as well as the absence or imperfection of some basic legislation and even the discrepancy between the legal framework and the real situation. Among the most significant external factors are changes in bank interest rates, inflation, changes in lending conditions, changes in tax rates and customs duties, changes in labor laws and more.
The ability to adequately respond to risk leads the bank to make a profit in the future and, conversely, the desire to make a profit puts the bank in a condition of acceptance of a risk. If one risky operation is successful, the profit from it can be so significant that it covers the losses from other small risky operations for a long time. The Basel III standard (2010) offers an international system for assessing liquidity risks, standards and monitoring and strengthens banks' capital requirements in order to prevent an international credit crisis.
According to the Guidelines for Banking Inspection "Risk Assessment System" [
According to the Instruction on the procedure for regulating the activities of banks in Ukraine [
The Committee on Supervision and Regulation of Banking, Supervision (Oversight) of Payment Systems has defined new criteria for certain groups of banks for 2016. This is stated in the decision of the Committee of December 31, 2015 № 657. According to the document, the following groups are provided: banks with a state share (in which the state owns a share of more than 75%); banks of foreign banking groups (banks, owners of controlling stakes in which are foreign banking institutions); group I (banks, the share of assets of which is more than 0.5% of the assets of the banking system); group II (banks whose share of assets is less than 0.5% of the assets of the banking system).
One of the main indicators of the state of the bank is capital adequacy. This indicator reflects the
possibility of covering the share capital of the credit institution's risky assets. The amount of capital
determines the volume of active operations of the bank, the size of the deposit base, the ability to
borrow funds in financial markets, maximum loans, the size of the open currency position and a
number of other important indicators that significantly affect the bank [
Under Basel II, capital, which has been identified by supervisors as a source of contingency losses, consists of three levels of capital. Tier 1 capital, so-called fixed capital, includes share capital and retained earnings, and tier two capital includes additional capital. Banking institutions can also attract short-term subordinated borrowings, ie third-tier capital, which they can use to meet the requirements of bank capital adequacy.
The National Bank of Ukraine issued a Resolution “On Approval of the Regulations on the Organization of the Risk Management System in Banks of Ukraine and Banking Groups”, in which it recommended banks to introduce internal bank documents on risk management, implement measures to implement requirements and develop appropriate software. The main risks of banks are the risk of insufficient capital to cover possible losses and liquidity risk, which is associated with the inability of the bank to meet its obligations to customers.
During the COVID-19 pandemic in Ukraine, the liquidity of the banking sector decreased. In order to maintain the liquidity of banks, the National Bank began to fill the economy with money supply, which, in turn, provoked rising inflation. The prices of monopoly producers reacted especially actively. The question now is whether and how the Ukrainian economy will be able to keep up with moderate inflation. How will the price of the national currency react, because our consumer sector is too sensitive to inflation spikes. Which goods of Ukrainian exports will become a "lifeline" for the trade balance of Ukraine. Prices in Ukraine during the pandemic are actively growing in almost all categories of goods: only in the first quarter of 2021 inflation exceeded 4 percent, and in March this year compared to March 2020, consumer prices rose to 8.5 percent, accelerating from 7.5 percent in February. This is especially true of food products, whose prices, along with soft drinks, have risen by more than 10 percent. It is also noticeable that fares in scheduled transport have risen by almost 8 percent.
For the second year in a row, the world is in a state of quarantine, which is replaced from time to time by stricter restrictive measures - national or local lockdowns, or, conversely, some mitigation when schools, kindergartens, restaurants, cinemas, etc. open. Businesses are closing, investment costs are falling, people who have lost their jobs are losing skills and motivation for many months. In the first wave of the pandemic responded, the so-called contact industries (industry of culture, leisure, tourism, hotel, restaurant business, trade), the closure of restaurants and bars could affect farms. Warehouses and shops are full of unsold products, which lose their meaning with the change of seasons. As we can see, a short period was enough for the wave of economic downturn to overwhelm such system-forming industries as agriculture, light industry, energy, and further - mechanical engineering. Compared to past world crises, the economic downturn has been sudden and profound. The GDP indicator is increasingly demonstrating its inability to be an adequate measure of economic development indicators; new aspects arise in questions about the nature and role of money in the functioning of a market economy. Banks, as a result of non-repayment of loans provided by firms for the purchase of goods, significantly reduced profits, reduced return on assets and capital.
The NBU has set the economic standard H2 in order to prevent excessive transfer of credit risk by the bank and the risk of non-return of bank assets to creditors and depositors of the bank. The regulatory capital adequacy ratio is defined as the ratio of regulatory capital to total assets and certain off-balance sheet instruments, reduced by the amount of created relevant reserves for active operations and the amount of loan collateral (investments in debt securities) by unconditional liability or cash collateral. and weighted by the degree of credit risk. To calculate the regulatory capital adequacy of a bank, its assets are divided into five groups according to the degree of risk and summed up taking into account the relevant weighting ratios.
The normative value of the H2 coefficient for existing banks must be at least 10%. For banks that
start operating, this standard should be: during the first 12 months of operation from the date of
obtaining a license - not less than 15%; during the next 12 months - not less than 12%; in the future
not less than 10% [
The largest banks (groups 1 and 2) are divided into three clusters: state-owned banks, large private banks and banks that are part of international banking groups. Clusters of small banks (groups 3 and 4) are not reported and the affiliation of a bank to the cluster will be considered a banking secret.
Note that in 2015 the NBU used the division of banks into groups according to the size of assets. The first group of the largest banks included PrivatBank, Oschadbank, Ukreximbank, Prominvestbank, Raiffeisen Bank Aval, Ukrsotsbank, Alfa-Bank, VTB Bank, Finance and Credit, Ukrsibbank, FUIB, UkrPasbank. The second group of large banks included Credit Agricole, Pivdenny, ING Bank, Citibank, Khreschatyk, Fidobank, Megabank, Kredobank, Credit Dnipro, PlatinumBank, Ukrinbank, Universal Bank and Diamantbank. Then it was decided to move to the classification by owner.
Cluster analysis is, by excellence, an unsupervised learning technique, that identifies the complex
relationships between variables, without imposing any restriction [20. 21]. Cluster analysis focuses on
the examination of the interdependencies between variables [
Clustering analysis is currently one of the most popular and advanced mathematical grouping
methods both in finance and other existing sciences [
It should be noted that the assessment of financial stability, as well as the efficiency of banks is
carried out, traditionally, with the help of analytical tools, the main disadvantage of which is inability
to take into account internal dependencies that can be hidden in analytical data. In contrast, we
propose to use the tools of artificial intelligence and machine learning, which feature it is the search
for such dependencies [
The goal of the present study is to identify resembling credit institutions, which can be included into homogenous groups, according to a series of prudential and profitability indicators. Our study aims to provide an alternative to the peer group techniques, implemented by supervisory authorities in the process of off-site surveillance. According to this technique, credit institutions are, firstly, grouped by size or volume of activity, and then, for each group, are made comparative analyses between the current values of financial indicators and the previous ones. The disadvantage stems from the fact that this method cannot signal the impairment in the financial condition of the whole group, but only the distress of a particular credit institution in that group.
For effective and adequate application of cluster analysis in the banking sector, it is appropriate
and even necessary to use expert technology [
In order to systematically assess banking risks and successfully prevent credit risks, attention should be paid to at least the following aspects of the application of expert technologies in the banking sector: Selection of banking experts and formation of an expert group; Determining the coefficients of competence of experts in the selected field; Selection of performance indicators of banks that significantly affect the adequacy of the division of many banks into clusters; Selection of characteristics from the whole set of characteristics used, which will be used to evaluate banks for their further clustering; Choice of clustering method for adequate division of banks into groups according to selected criteria.
Expert technology is a tool to support decision-making in poorly structured subject areas where it is not possible to obtain reliable numerical information.
In [
Note that the performance of banks can be measured on different scales. This fact should be taken into account when using expert technology. In particular, the performance of banks can be set in: nominal scales; dichotomous form; discrete form on scales with several gradations; in the form of fixed weights, which are real numbers; in the form of intervals of real numbers; in the form of functions of belonging to a fuzzy set; in the form of various combinations of the given ways of representation.
Thus, the problem of bank clustering can be classified as poorly structured. To solve it we will use mathematical modeling, expert technologies and tools of artificial intelligence.
Suppose a set A, consisting of n objects (in our case - banks), which must be compared with each other:
Today, the weights of competence of experts, defined in various scales of measurement, are widely used. The most common are the following ways of representing the values of weights: arbitrary real or natural numbers i , i I ;
ai A, i I 1,..., n. (1) Objects (banks) (1) are described by a set of m parameters (indicators), are points of parametric interval form of weights:
i iН ,iВ , 0 iH iB , i I. weights in the form of a function of belonging to a fuzzy set.
The problem of determining the importance of banks, the importance of performance indicators of banks and the competence of experts in most cases are similar and differ only in interpretation. Therefore, the definition of weights in general should be considered, bearing in mind that this problem can be interpreted in any of these aspects.
That is, among the whole set of parameters (1) are selected only those that are important for further clustering of a given set of banks a j A, i I , j J ' J , A m'. (4)
i
The next step in the application of expert technology is the selection of characteristics by which objects of the species are evaluated (4). In our case, these are banks of the whole set of Ukrainian banks that should be clustered, or banks of a certain subset.
The characteristics of banks are well developed and sound. We will mark them through where s the number of characteristics that, in principle, can evaluate the activities of banks.
After the application of expert technologies, the set of characteristics of banks of type (5) decreases, s ' s. Thus, the set of characteristics that are essential for solving the problem of clustering of banks will look like: real numbers subject to unilateral or bilateral restrictions:
i 0, i I ; 5 i 5, i I ; 0 i 1, i I ; real or natural numbers, taking into account the condition of centering: i 0, i , i I ; iI real numbers taking into account the condition of normalization:
i 0, i I ; i 1, iI idealized weights: max i 1, i 0, i I .
iI ai a1,..., aim , ai A, i I , A m , i
m' m , m ' m . space m : (2): where a j , i I , j 1,..., m J , the value of the j th indicator of the i th bank.
i On the basis of a survey of k experts there is a narrowing of the parametric space of the species (2) (3) (5) (6)
Characteristics that are calculated to formally determine the financial condition of banks are used in a variety of practical situations. To solve a specific problem, in particular, the clustering of banks, an additional expert survey should be organized. Based on the use of expert technologies, the analysis of bank valuation tools is carried out. As a result of the application of this toolkit, from the set of all possible characteristics of banks (5), only those (6) are selected that are essential for the decision to combine subsets of banks into clusters.
The methods of conducting the relevant examination and the methods of aggregating expert information depend on many factors. These procedures require further research and will be described in future research.
To date, a wide range of clustering methods has been developed. These methods are characterized by different approaches, have different conditions of application and can lead to clustering results that do not coincide with each other. Therefore, for the situation of application of cluster analysis on many banks, it is necessary to conduct a preliminary study of the suitability of the use of effective clustering methods among the full range of such methods. The admissibility of the application of specific methods of cluster analysis to the study of many banks is determined by the use of expert technology. In the future, relevant research will be carried out and procedures will be described, which substantiate the feasibility of using certain approaches.
Research on the feasibility of using different methods of clustering in the banking sector requires a lot of effort and is not the subject of description in this paper.
The cluster approach (analysis) is a component of classification methods and consists in splitting a given sample of objects (situations) into subsets - clusters so that each cluster consisted of similar objects, and objects of different clusters differed significantly. The application of cluster analysis involves the following steps: o sampling for clustering, in our case these are indicators of banks' activity; o determination of the characteristics by which the objects in the sample will be evaluated. (We choose the indicators of return on assets and capital, as well as the indicator of capital adequacy); o selection of the method of calculating the values of the degree of similarity between objects application of the method of cluster analysis of k-means (ordering a set of objects into relatively homogeneous groups).
The goal is to divide n observations into k clusters so that each observation belongs to the cluster with the closest average value. The principle of the algorithm is to find the centers of such clusters. Clustering can be considered as a task of constructing the optimal division of objects into groups. Each object is identified by a vector of characteristics. X = (x1 ..., xd). The optimality can be defined as the requirement to minimize the root mean square partition error:
e2(X,L) = ∑j=1∑i=1 ||x(j)i − cj ||2, where c j is the "center of mass" of the cluster j.
"Center of mass" of the cluster is a point in the space of characteristic vectors with the average for this cluster values of characteristics.
Let us describe the k-Mean algorithm [
2. Assign each object to the cluster with the nearest "center of mass". 3. List the "centers of mass" of clusters according to the current membership. 4. If the algorithm stop criterion is not satisfied, return to step 2.
As a criterion for stopping usually choose one of two: 1. No transition of objects from cluster to cluster in step 2. 2. Minimum change of the root mean square error.
The algorithm is sensitive to the initial selection of "centers of mass".
Let us analyze some indicators of Ukrainian banks for the first half of 2021 (according to the NBU) using cluster analysis (Tables 1, 2 and 3). Our goal is to cluster banks using their financial data. The data can be obtained from reports (or site) of the National bank of Ukraine.
A very large number of bank failures in last years triggered a financial crisis. Although unprecedented intervention of central banks and governments helped economies to recover, the global financial systems remains at risk. As we have previously mentioned, cluster analysis, as part of maching learning, is an exploratory technique which organizes large amounts of observed data into a reduced-size meaningful structure. In order to discover the hidden information in our set of financial indicators, we have applied the clustering technique for each of the economy normatives. 8