The low level of small business development is primarily due to the lack of sufficient conditions for the development of small businesses in our country. Based on Figure 1, we can highlight the following problems of the low level of development of small businesses in Russia from the point of view of entrepreneurs themselves. Firstly, there is a high tax burden (47%) and limited financial resources (46%), secondly, there is corruption in government (32%) and high rents (31%), and thirdly, there are difficulties in obtaining a loan. (25%), fourthly, low qualifications of personnel (12%) and problems related directly to the registration of the business itself (11%) - from which it follows that limited financial resources are almost the main obstacle in the development of small businesses, from which also and this directly leads to the problem of obtaining a loan from a bank.
The share of loans issued to small businesses in GDP is: in Russia (1%), USA (20%), EU countries (30%), Japan (35%). In terms of support for small and medium-sized businesses, Russia is in 148th place. As can be seen in Figure 2 below, the main problem of access of small enterprises and individual entrepreneurs to the bank’s financial resources is primarily related to the problem of providing collateral and guarantees (43%), secondly, high interest rates for using a loan (32%), complexity and 26% of respondents are concerned about the length of time it takes to complete the relevant documents, short deadlines are a problem for 15%, they do not see the need to attract additional funds - 12% and do not have the trust of the bank - 5%.
Rice. 1. - Main problems of small business development:
Rice. 2.
However, financing of small businesses is characterized by high dynamics: an increase of up to 50% per year, while the volume of ruble loans provided by banks to non-financial enterprises and organizations, in general, according to the Bank of Russia, increased by 28.3%. Moreover, there is reason to believe that in 2008 the small business lending sector will be a record holder in terms of growth rates. A necessary condition for issuing a loan is the availability of collateral. In 31.20% of cases, when securing loans, banks most often give preference to inventories and valuables, in 23.80% - to real estate, including unfinished construction, buildings, and fixed assets. Only 6.00% of banks accept the balance of funds in the current account and 11.00% guarantees of another company or another bank, securities as collateral.
Rice. 3. - Preferences of banks in collateral when issuing a loan:
Apparently, in the next few years, loans for up to one year will remain the most popular.
Today, 42% of the total volume of loans are issued for a period of up to 1 year, 27% for a period of 1-2 years, 23% for a period of 2-3 years, and 8% of loans were issued for more than three years.
The main obstacle that stands in the way of banks expanding their operations with small businesses is their high lending risk.
Rice. 4. - Structure of loans by terms of provision:
The risks of lending to small businesses are due to both the specifics of this special type of business activity and the peculiarities of its development in Russia: the isolation of Russian small businesses from the formation of the entire business environment, the deformation of the economic environment of small businesses, manifested in the gap between real and formally recorded volumes of economic turnover, which causes low information transparency of this sector of the economy, insufficient professionalism of management, due to the peculiarities of the formation of a market economy in Russia.
It is no secret that, in an effort to protect themselves, commercial banks sometimes charge higher interest rates in order to compensate for possible losses from non-repayment of the loan.
So one of the main conditions for expanding banks’ lending activities in the small business sector remains risk reduction. Banks assess the client’s financial condition on the basis of his official statements, adjusted to take into account actual activities.
Most methods are based on the basic principles of credit relationships between banks and small businesses:
- - do not provide a loan if the company is in a critical situation or the funds are needed to repay another loan;
- - determine the borrower’s creditworthiness only on the basis of an analysis of his real financial situation;
- - take into account the borrower’s competitiveness, reputation, business and professional qualities of the enterprise’s management;
- - take into account the difficulties of providing small businesses with first-class guarantors using combined collateral;
- - promptly resolve the issue of granting a loan and, if the decision is positive, constantly monitor the state of the client’s business and the timing of loan repayment.
In this situation, the bank itself has to create the client’s credit history, starting work with him by issuing small loans for short periods and gradually increasing the amount and term.
When considering a loan application, its compliance with the Bank's Credit Policy is checked and, as a rule, the transaction (project) being financed is analyzed, the loan transaction is structured, and the level of credit risk is determined.
Review of loan applications includes the following procedures:
- - preliminary qualification of the Client;
- - collection of necessary information and documents;
- - checking the accuracy of the information and documents received;
- - identification and analysis of criminal and legal risks;
- - analysis of information and documents, assessment of credit risk.
To prequalify the Client, the following activities are carried out:
- - familiarizing the client with the loan products offered by the Bank;
- - determining the most suitable loan product for the client;
- - preliminary verification of compliance of the Client’s loan application with the Bank’s Credit Policy;
- - preliminary structuring of a credit transaction.
Taking into account the results of the preliminary qualification of the Client, the Bank makes a decision on qualifying the Client as a potential Borrower. If the potential Borrower agrees with the terms proposed by the Bank, the employee of the credit department invites him to fill out an application for a loan addressed to the head of the Bank (branch) in any form, indicating the loan amount, purpose, repayment period and type of collateral, as well as the Borrower’s questionnaire. The Bank verifies the accuracy of the information and documents received in all possible ways, for example: by visual inspection of relevant facilities (buildings, structures, equipment, vehicles and other property), studying primary documents, accounting and management accounting documents, conducting a survey of persons who may have the necessary information, sending written requests.
Reliable information is the basis for conducting quality analysis.
Based on the results of the inspection, the Department of Regime and Information Protection of the head office and the corresponding structural unit of the branch prepares a written opinion on the advisability of lending to the Borrower in the form of an internal memo. In order to identify legal risks, the documents of potential Borrowers who have submitted loan applications are usually subject to legal examination.
The analysis of information and documents relating to the potential Borrower is carried out in accordance with the Bank's internal documents on lending activities.
Determining the level of credit risk and preparing a conclusion containing the results of credit risk analysis implies the presence of a risk management service in the bank.
In what form should a conclusion on the level of credit risk be prepared in the absence of this service? The loan officer, who signed the conclusion on the feasibility of issuing a loan, reports his opinion to the Credit Committee. The decision of the Credit Committee is documented in a protocol.
The development of approaches to organizing lending and managing emerging risks depends on the correct classification of loans. All loans to small businesses are classified as loans of an investment nature, i.e. creating new investment value (loans for construction, loans for the purchase of cars, equipment), and loans for replenishing working capital that do not create investment value.
The competition between banks in the lending market for replenishing working capital of small businesses is extremely aggressive, and its result is the presence of a large number of banks and a fairly dense distribution of the market between them. An opposite trend can also be observed, when individual banks are reducing these operations due to high risks and the proportion of late payments. In leading banks, commodity lending for small business loans was also problematic.
Loans to small businesses have both general and special risks (Table 1.2).
In addition to the listed structural elements of the risk of loans to small businesses, it is necessary to distinguish between aggregate (general) and individual types of credit risk, and also take into account the features of credit and other risks that arise when lending to small businesses.
A more active and diversified development of lending is hampered by a number of risks: common to all types of lending and special, operating only in the field of loans to small businesses, which can be combined into the following groups:
General risks:
1. Risk of insufficient resources. At this stage of development, Russian banks do not have a sufficient resource base to issue long-term loans to small businesses for investments in the investment sphere and the development of new innovative industries. Thus, banks are limited to short-term lending or lending for up to 6 months or up to a year for the purpose of replenishing working capital of existing enterprises. Such caution increases the reliability and financial stability of banks, but does not satisfy the development needs of small businesses and does not contribute to the implementation of government programs for the development of innovative business.
Table 1.2. Classification of credit risk of loans to small businesses
|
Criteria classifications |
Sign of risk |
|
emergence |
|
modernization;
|
|
|
3. Character manifestations borrower and |
|
|
4. Character risky actions borrower |
|
|
Opportunity regulation |
|
|
6. Degree |
|
- 2. The risk of inadequate determination of the borrower’s financial situation. A serious obstacle to increasing the volume of lending for current needs is the relative opacity of the financial position of the borrower - a small enterprise, whose funds are often closely intertwined with its own funds as an individual.
- 3. Risk of collateral securing the transaction. Small businesses often do not have sufficient fixed assets, equipment and goods to formalize a pledge agreement and must resort to pledge of private property: houses, apartments, home furnishings, often without the necessary legal security.
- 4. Risk of concentration of loans in individual banks. There has been a tendency to concentrate lending to small businesses in large and regional banks (Sberbank, Vneshtorgbank, Rosbank), attract a fairly large promising sector of clients to them and increase the inequality of banks in the distribution of lending to small businesses across regions. This leads to the diversification of large and small banks in the terms of lending to small businesses, and therefore to the emergence of additional risks.
- 5. Increase in overdue loans and the amount of reserves formed.
By name and structure, the general risks of lending to small businesses do not differ from the risks of lending to other legal entities, but the form of their implementation, qualitative and quantitative characteristics and the minimization methods used are different. Thus, for investment loans to small enterprises, reserves for possible loan losses are formed on an individual basis, and for all other loans (to replenish working capital, as opposed to loans to other legal entities) - based on the formation of homogeneous loan portfolios and the spread of the identified credit risk over a representative sample for the entire homogeneous portfolio of loans to small businesses.
Special risks of lending to small businesses:
- 1. Risk of uncertainty of results. With comparable costs for other types of lending to legal entities, it is not profitable for a bank to provide loans to small businesses when there is uncertainty about their results.
- 2. The risk of a large number of transactions and their poor execution. Loans to small businesses to replenish working capital, in contrast to loans of an investment nature, are characterized by a large number of transactions concluded in small amounts. There is a directly proportional relationship: more trades = more risk. There are no special procedures for lending to small businesses that would allow one to quickly and at low cost prepare and carry out a large mass of small transactions, while at the same time ensuring acceptable quality of homogeneous loan portfolios for certain types of lending.
- 3. Availability of unsecured loans. During the crisis of recent years, banks increasingly began to offer unsecured loans to small businesses. To develop such loans, banks must be well able to assess the financial condition of the borrower, his ability to repay the loan through regular sources of income. To achieve this, new technologies for assessing the financial position of a small enterprise, unified for all banks, must be developed. The absence of such systems leads to the assignment of the same borrower in different banks to different groups of creditworthiness and financial situation.
- 4. The presence of a large number of types of homogeneous portfolios. The problem of minimizing this risk is to apply adequate risk management methods for a specific type of homogeneous small business portfolio (Table 1.3).
As for the external (or systematic) risks of loans to small businesses to replenish working capital, we can include such general risks as: political; macroeconomic, social and inflationary (which will lead to a deterioration in the creditworthiness of the borrower - a small enterprise); risk of legislative changes.
Table 1.3. Comparative characteristics of investment loans and loans to replenish working capital of small enterprises (depending on the type) and the risks associated with them
|
Risks/types of loans for current needs |
Loans to replenish working capital |
Investment |
|
1) political |
||
|
2) social |
||
|
3) inflationary |
||
|
4) risk of legislative changes |
||
|
5) the risk of curtailing government programs |
||
|
Internal: |
||
|
A. Borrower's risk |
||
|
1) deterioration in the financial situation of the borrower |
||
|
2) moral hazard |
||
|
3) business risk |
||
|
4) insured event |
||
|
5) risks of non-fulfillment of obligations by the borrower |
||
|
6) risks of misuse of credit |
||
|
7) risks of fraud and abuse bank client side |
||
|
8) the risk of the collateral available to the borrower |
||
|
B. Bank risk: |
||
|
risks of an incorrectly chosen credit policy |
||
|
structural risk |
||
|
operational risk (including fraud and banking abuses) |
||
|
time risk |
||
|
interest rate risk |
||
|
risk of loss of income |
||
|
liquidity risk |
||
|
risk of poor management |
||
|
portfolio risk (total and for each homogeneous portfolio) |
||
|
resource risk |
||
|
risk of inability to realize the collateral |
For example, creating regulatory favorable conditions for providing certain types of loans and restrictions on others. A significant feature of the influence of external risks on loans to replenish working capital is that they are not affected or are only slightly affected by political risks and the risks of curtailing government programs, like all other types of loans. They may not be taken into account when developing these areas of lending.
As for internal risks, these include the risk associated with the borrower - a small enterprise, and the risk associated with the lender bank. The risk associated with the borrower - a small enterprise, includes: the risk of deterioration in the financial situation, loss of status (moral hazard) or market (business risk), death of the borrower's manager or an accident to him, risks of the borrower's failure to fulfill obligations, risks of misuse of the loan , risks of fraud and abuse on the part of a small business, risk of collateral held by the borrower.
A significant feature of the impact of small business risks on the replenishment of working capital is that these loans are not affected or are slightly affected by such risks as the risk of misuse of loans and collateral risks. They may not be taken into account when developing these areas of lending, in contrast to investment loans, where these types of risks become of utmost importance. But the possibility of an insured event risk occurring is high when issuing any type of loan to a small enterprise.
The risks associated with the creditor bank include the risks of an incorrectly chosen credit policy in the field of loans to small businesses to replenish working capital, structural risk, operational risk, including fraud and banking abuse, time risk, interest rate risk, risk of loss of income, risk liquidity, risk of imperfect management, portfolio risk (total and for each homogeneous portfolio), resource risk and the risk of inability to realize loan collateral. The latter risk is not as significant as for investment loans. But the risk of homogeneous portfolios becomes of paramount importance in the process of issuing and repaying loans to small businesses to replenish working capital.
The risk assessment for an individual loan to small businesses is influenced by factors such as the personal characteristics of the borrower and information about the loan provided. In order to determine the risk factors of an individual borrower - a small enterprise, we will group them by importance:
Table 1.4. Risk factors affecting the total risk of a homogeneous portfolio of small business loans
|
Type of risk |
Risk factors |
|
Credit homogeneous portfolio |
Financial situation of the borrower and other individual risk factors of the borrower |
|
Quality of debt servicing (including volume of overdue debt, availability of extensions, debt refinancing) |
|
|
Credit history (relationship between the creditor bank and borrower in the past) |
|
|
Loan security (quality and sufficiency of collateral) |
|
|
Quality of information base about the borrower |
|
|
The quality of the legislative framework (sufficiency and consistency of regulatory regulation of loans to small businesses) |
|
|
Limit, loan amount |
|
|
Type of loan (for replenishment of working capital, for investment costs, overdraft) |
|
|
Procedure for repaying the principal debt |
|
|
The amount of the penalty for late repayment of the principal |
|
|
Liquidity homogeneous portfolio |
|
|
Type of portfolio (by type of loans: to replenish working capital funds for investment costs, overdraft) |
|
|
The procedure for repaying the principal debt on the portfolio (with the involvement of special services, in court, in the usual manner, through the account of overdue loans, sale of collateral, etc.) |
|
|
Profitability homogeneous portfolio |
Interest rate |
|
Amount of reserve for possible loan losses |
|
|
Interest repayment procedure |
|
|
Penalty amount for late repayment of interest |
|
|
Volumes of overdue debt by interest |
- 1) financial situation;
- 2) social status;
- 3) professional activity, market sphere;
- 4) personal characteristics of the borrower;
- 5) credit history, loan information.
Each group of factors is specified by additional risk indicators for an individual loan to a small enterprise. An essential feature of risk assessment for an individual loan includes factors that characterize the management of a small enterprise as an individual. This reveals some similarities between loans provided to small businesses and individuals.
In addition to the listed factors, the total risk for the bank’s homogeneous portfolios is also influenced by additional factors related to the liquidity and profitability of the bank’s homogeneous portfolio (Table 1.4).
In addition to the risk factors affecting the individual and aggregate risk for small business loans, one should take into account the time of occurrence and the stage of manifestation of risk factors at various stages of the loan movement.
Table 1.5 presents a grouping of risk factors that influence the total risk of a homogeneous portfolio of small business loans at various stages of the lending process.
When choosing a method for regulating small business lending risks, the stage of the lending process should also be taken into account. Methods for regulating the risks of loans to small businesses are divided into analytical and practical. Analytical methods of risk management are used as a tool for proactive risk management and make it possible to develop forecasts and risk management strategies before the start of a credit transaction. The main task of analytical methods for risk regulation is to identify risk situations and develop measures aimed at reducing the negative consequences of their occurrence. The objectives of analytical risk management methods also include the prevention of risk situations.
Practical methods of risk management are designed to reduce the negative results of risk situations that arise during the implementation. As a rule, they are based on analytical methods of risk management.
Table 1.5. Indicators of risk factors at various stages of the credit process
|
Types of risk |
When providing credit and monitoring |
When repaying principal and interest |
|
Legal risk |
Quality of the legislative framework |
|
|
Borrower risk |
Financial position, business risk, qualitative parameters |
|
|
Debt service quality |
||
|
Credit history |
||
|
Securing a loan with collateral |
Loan security (in case of non-repayment) |
|
|
Quality of the information base about the borrower |
||
|
Volumes of overdue debt by interest |
||
|
Provisions for possible loan losses |
||
|
Risk of homogeneous loan portfolio for loans to small businesses |
||
|
Limit, portfolio amount |
||
|
Portfolio type |
||
|
Interest rate |
||
|
Procedure for repayment of principal and interest |
||
|
Penalty amount for late repayment of principal and interest |
At the same time, practical methods of risk management are the basis for creating an information base for risk management and the subsequent development of analytical methods. The following risk management methods are known: risk avoidance; risk limitation and risk reduction; transfer (transfer) of risk, including insurance and risk acceptance.
Within this framework, there are the following main ways to reduce credit risk:
assessment of the financial situation of a small enterprise;
reducing the size of loans issued to one borrower - a small enterprise;
credit insurance;
attracting sufficient collateral;
issuing discount loans to small businesses;
obtaining sureties and guarantees;
preliminary assessment of possible losses using predictive methods of analysis of available static and dynamic reliable information about the activities of borrowers and their financial condition and the possibility of changing it;
taking into account the dynamics of interest rates;
risk diversification.
Thus, there are various risks when lending to businesses. The application and choice of a specific method of regulating or minimizing the risks of consumer lending largely depends on the method of risk assessment and the type of loan.
What are the specific credit risks that arise when Russian banks lend to small and medium-sized businesses? They are the reason for the low availability of loans. Let's consider the use of guarantee support mechanisms, which should meet the interests of both parties and give a synergistic effect - effective risk management for credit institutions, and the opportunity to obtain financing for small and medium-sized businesses.
The problem of financing a segment important for the development of the national economy - small and medium-sized enterprises (SMEs) - is one of the most pressing in modern conditions. But the expert community still raises the issue of the unavailability of credit resources for small and medium-sized enterprises implementing investment projects in non-trade and non-resource sectors.
Market for SME lending by Russian credit institutions
According to Rosstat and the Federal Tax Service of Russia, as of January 1, 2013, 6,037 thousand SMEs were registered in the Russian Federation, employing 17,729.2 thousand people. Every fourth worker in Russia is employed in the SME sector. At the same time, enterprises (legal entities) employ 12.2 million people (69.1%), individual entrepreneurs employ 5.45 million people (30.9%).
The share of products and services produced by SMEs in the total volume of products and services produced by enterprises in the country is about 25%.
At the same time, in the first half of 2013, a number of negative trends were recorded in the development of small and medium-sized businesses.
According to the Federal Tax Service of Russia, for the period from January 1 to July 1, 2013, the number of individual entrepreneurs decreased by 474.1 thousand people (11.5%).
In the first quarter of 2013, compared to the same period in 2012, according to Rosstat, the number of small enterprises decreased by 1.5%, the number of medium-sized enterprises decreased by 3.4%, and the number of employees in medium-sized enterprises decreased by 0.8%. workers.
An analysis of bank lending to the SME sector in 2013 shows that this market segment continues to develop dynamically. Thus, according to the Bank of Russia, as of December 1, 2013, the volume of newly granted loans to SMEs in the Russian Federation as a whole reached 7,176 billion rubles, which is 16% higher than the same period last year (6,177 billion rubles).
The volume of the loan portfolio of SMEs as of December 1, 2013 also increased and amounted to 5163 billion rubles, an increase of 15.4% (Table 1).
Table 1
Total amount of debt on loans provided to SMEs
in the Russian Federation (thousand rubles)
|
Borrowers |
for 12 months, % |
|||
|
Legal entities and individual entrepreneurs |
||||
|
incl. SME |
4 471 152 |
4 493 760 |
5 163 343 |
|
|
Individuals |
||||
|
SME sector share |
-0.52 p.p. |
The following information from the Bank of Russia is also interesting: in the third quarter of 2013, when lending to small and medium-sized businesses, there was a tightening of requirements for the borrower’s financial position and collateral.
At the same time, according to the same source, the demand for new loans with a maturity of over 1 year is growing at a significant pace (the demand for loans with a maturity of up to 1 year has not increased), as well as the demand for prolongation of previously issued loans. According to forecasts, demand will increase by another 20% in the next 3 to 6 months.
Judging by the results of the study "Entrepreneurial Climate in Russia: Support Index - 2012", obtaining a short-term (up to 1 year) loan is associated with significant difficulties for 26% of companies, and for another 12% of companies it is “virtually impossible.” It is “quite difficult” to attract a loan for a period of 1 to 3 years, according to 27% of respondents, and “almost impossible” - according to 14%. As for long-term loans (for a period of more than 3 years), there are even more negative assessments: “quite difficult” - 27% and “almost impossible” - 17%.
According to the SME Trust Index of business sentiment for small and medium-sized businesses, the factor of access to financing has the greatest impact on the development of small and medium-sized businesses in Russia. In the total share in 2012 it increased compared to the beginning of 2012 from 38 to 54%.
Thus, the issue of access to credit for SMEs is still very pressing. The main problems that entrepreneurs experience are high interest rates and the lack of reliable collateral.
Specific risk of SME lending
From the entire range of credit risk factors, we can highlight those that are typical for SME lending:
Non-transparency of clients’ activities: lack of a long-term track-record (a retrospective documented history of the activities of the enterprise, its owners or management in the relevant area, reflecting the achievement of any results), credit history, audit confirmation of the reliability of financial statements, etc.;
Lack of business reputation of the project initiators (often project companies managed by nominee directors are attracted to finance investment projects);
Enterprises in the SME sector are more susceptible to traditional technological, production and commercial risks due to weaker diversification of production and sales;
The presence of accounting losses even with profitable financial activities, caused by unprofessional accounting along with the process of optimizing the tax burden;
Low capital base.
Taking into account the above risks, but not limited to them, credit institutions are forced to create increased reserves for possible losses, while simultaneously increasing the interest rate. Practice shows that loans issued to SMEs are classified no higher than quality category III in accordance with Bank of Russia Regulation No. 254-P dated March 26, 2004 “On the procedure for credit institutions to form reserves for possible losses on loans, on loan and similar debt” ( hereinafter - Regulation N 254-P) even with good debt servicing. In this situation, the only tool that can objectively reduce credit risk, as well as the reserve formed for possible losses, is the provision of liquid collateral that covers the amount of the loan and interest at the collateral value.
Risk of insufficiency and low liquidity of collateral
However, at this stage, the situation is further aggravated by a risk that we have not mentioned, which stands apart when lending to SMEs at present. This is the risk of insufficiency and low liquidity of collateral.
So, according to ch. 6 of Regulation N 254-P, collateral can be used to adjust the created reserve, and it is divided into two subgroups: quality categories I and II. Category I collateral for the purpose of adjusting the reserve is accepted at its full estimated value. However, it mainly includes financial instruments (bank guarantees, guarantee deposits, securities), the issuer of which is a company with a high international long-term credit rating. Naturally, SMEs do not have this kind of security.
Quality category II provision is accepted with a 50% discount to the estimated cost. It may include, in fact, any pledge of things in the presence of a stable market for the pledged items and (or) other sufficient grounds to believe that the corresponding pledged item can be sold within a period not exceeding 180 calendar days from the date of occurrence of the grounds for foreclosure on the pledge . SMEs have the property for this kind of collateral. However, even if we assume that it is liquid, its estimated value should be 200 - 300% of the loan amount to cover its full cost and interest. The credit policy of some banks assumes that collateral will cover interest payable for the entire lending period; thus, for long-term lending, the total amount of required collateral may exceed the amount of the requested loan by three or more times.
Also, Russian practice has not yet developed adequate approaches to assessing the value of intangible assets (intangible assets) for collateral purposes, which could be useful for small and medium-sized enterprises in the technological sector, where the lion's share of assets and liabilities consists of intangible assets and arrears of wages for personnel. Thus, with constant, basic credit risks arising when lending to SMEs, the lack of adequate collateral for the planned loan makes lending for banks too risky and ineffective (due to the need to allocate created reserves for losses in advance), and for SMEs - practically inaccessible .
This problem is inherent in SME lending not only in Russia. Similar problems are faced by credit institutions and their SME clients in developed and developing countries around the world. To bridge this kind of “gap” between the need for SMEs to obtain a bank loan and the possibility of them receiving it, governments of various countries are introducing tools to support small and medium-sized businesses based on the provision of guarantees and guarantees for loans attracted by SMEs from banks.
Next, we will consider guarantee mechanisms implemented in various countries, as well as a new instrument operating in the Russian market, which allows credit institutions to obtain adequate loan collateral and reduce risks (reserves), and for medium-sized businesses to obtain a loan.
Foreign experience of government support for SMEs
According to the OECD SME Finance Handbook (2013), collateral requirements were one of the biggest barriers SMEs faced when seeking new credit.
In the period after the financial crisis of 2008 - 2009. Access to finance for SMEs has deteriorated, with many governments creating or expanding existing credit guarantee programs (Table 2).
Table 2
Government policy measures to support SMEs in various countries during crises
2008 - 2009
|
Public policy measures |
|
|
Increasing the volume of government guarantees on loans and (or) the percentage of guaranteed loans, the number of enterprises eligible to receive a loan, and countercyclical loans |
Canada, Chile, Denmark, Finland, France, Hungary, Italy, South Korea, Netherlands, Portugal, Slovakia, Slovenia, Switzerland, Thailand, UK, USA |
|
Special guarantees and loans for new businesses |
Denmark, Netherlands |
|
Increasing the volume of government guarantees for export loans |
Denmark, Finland, the Netherlands, New Zealand, Portugal, Sweden, Switzerland |
|
State co-financing |
|
|
Increasing the volume of targeted lending |
Chile, Hungary, Korea, Slovenia |
|
Venture capital and equity financing and underwriting |
Canada, Chile, Denmark, Finland, France, Italy, Netherlands, New Zealand, Sweden, UK |
|
New programs: business consulting |
Denmark, New Zealand, Sweden |
|
Tax exemption, deferred payment |
France, Italy, New Zealand, Sweden |
|
Credit intermediation |
In 2009 - 2012 In some countries, new job creation programs have been launched as anti-crisis measures, and some guarantee instruments have been tailored to specific categories of small and medium-sized enterprises, such as start-ups or innovative companies.
In Ireland, overall business lending fell during the crisis and further during the recovery. In April 2012, the government announced the creation of the first credit guarantee scheme. Guarantees cover 75% of the loan (up to 1 million). The target group is successful entrepreneurs with high productivity, a prepared business plan and a defined market for their goods and services. The positive experience of France also shows that one of the most significant measures to promote lending to small and medium-sized enterprises is the provision of guarantees by OSEO - an organization financed by both the state (58.3%) and the private sector (41.7%) . The organization provides guarantees, co-financing, and direct loans to support innovations and services. In addition, OSEO provides guarantees for venture capital funds. Traditional beneficiaries are micro-enterprises (46.5%), small (31%) and medium-sized enterprises (17.5%).
To facilitate access for small businesses to bank loans, OSEO shares risks with banks on loans issued for the creation of an enterprise, guaranteeing a repayment of financing in amounts ranging from 40 to 70%, or lending to enterprises together with banks. OSEO's activities in the field of financing and guarantees cover three types of needs of small businesses and their partners: long-term lending and joint financing with banks, short-term financing, various types of guarantees.
The Japanese government also created three public financial institutions: Japan Finance Corporation for Small & Medium Enterprises, National Life Finance Corporation and Shoko Chukin Bank. These financial institutions play a buffer role in providing preferential loans to small and medium-sized enterprises in need at reduced interest rates and relaxed requirements for collateral and loan guarantees. It should be noted that approximately 90% of the total volume of lending to small and medium-sized businesses is provided by commercial banks and only about 10% of loans are provided by these state financial organizations.
In addition, the Japanese government has created a nationwide system to provide guarantees for loans needed by starting and developing small and medium-sized enterprises. The basis of this system was the Credit Guarantee Corporation for Small and Medium Enterprises (Credit Guarantee Corporation for SMEs), consisting of 52 independent branches that provide a total of 32 trillion yen in guarantees annually. In order to increase the financial stability of this organization, the Japan Finance Corporation for Small Business and Medium Enterprise (JASME) provides reinsurance for about 50% of issued loans.
In Italy, there are many guarantee funds that differ in territorial coverage and industrial affiliation of the beneficiary companies. More than 200 institutes are united into 7 large federal funds according to their sectoral affiliation. In fact, the system is divided into two levels: regional and federal.
The first level is regional, this is direct work with small and medium-sized enterprises. At the second level, work is carried out not directly with small and medium-sized enterprises, but with regional funds. This two-tier system, organized by a group of identical institutions, allows you to cover a larger volume of risks. However, banks can also turn to the second level for direct guarantees from government guarantee funds, such as the Central Guarantee Fund.
In South Korea, in 1989, in accordance with the law on financial support for new technology businesses, the Korean State Fund KOTEC (Korean Technology Finance Corporation) was created - a non-profit guarantee organization that provides credit guarantees to innovative companies. More than 80% of the total number of guarantees was provided to companies whose activities are aimed at the development and use of new technologies. KOTEC also provides technological and management support, which includes technology valuation, technology commercialization and investment attraction, comprehensive technology assessment, monetarization and business conditions.
Thus, international experience shows that guarantee systems are designed to provide comprehensive support to both small and medium-sized businesses, taking into account the difference in their needs. Guarantee systems are also an integral mechanism for stimulating economically significant sectors of the economy. Systems are built on the principles of uniform standards and achieve maximum efficiency when combined under the control of a central organization.
The techniques described above can be presented in the form of a table. 3.
Table 3
Guarantee mechanisms to reduce the risks of SME lending
|
Operator |
State participation |
Coating |
Scope of programs |
Year founded |
|
|
80% of the loan amount |
€4.2 billion (portfolio as of 2012) |
||||
|
Subsidiary financing |
70 - 80% of the loan amount |
||||
|
National Federation of Loan Guaranty Corporations |
Financing a network of loan guarantee corporations (75% participation) |
80% of the loan amount |
~$36 billion (portfolio as of 2012) |
||
|
Small Business Administration |
Providing guarantees to partner banks for lending to SMEs |
75 - 85% of the loan amount |
€5.9 billion (cumulative total by 2012) |
||
|
Germany |
Burgschaftsbanken |
Providing counter-guarantees |
80% of the loan amount |
€5.9 billion (portfolio as of 2011) |
|
|
South Korea |
Subsidiary financing |
80% of the loan amount |
$34.6 billion (portfolio as of 2011) |
||
|
European Union |
European Investment Fund |
Provision of bank guarantees to secure a portfolio of SME loans |
Guaranteeing 50% of the loan portfolio. 120 - 180 million euros per bank |
€6.6 billion (portfolio as of 2012) |
|
|
Subsidiary financing |
50 - 85% of the loan amount |
€1 billion (portfolio as of 2010) |
Russian guarantee mechanism
There are two guarantee programs to support SMEs in Russia.
The first is a system of regional guarantee funds. A guarantee fund is a legal entity, one of the founders of which is a constituent entity of the Russian Federation or a local government body, created to ensure access of small and medium-sized businesses to credit and other financial resources. Sureties (guarantees) are provided to commercial banks, insurance companies, and lessors who have entered into a cooperation agreement with the regional guarantee fund. The selection of such organizations is carried out on a competitive basis.
The scope of responsibility of the regional guarantee fund under concluded guarantee agreements (sureties) does not exceed 70% of the volume of obligations of a small and medium-sized enterprise and the organization of infrastructure to support small and medium-sized businesses to a financial organization.
Regional guarantee funds have now been created in 80 constituent entities of the Russian Federation (except for the Nenets, Chukotka Autonomous Okrugs and the Jewish Autonomous Region) with a capitalization of more than 37.7 billion rubles. Over the entire period of implementation of the program (since 2006), guarantee funds have issued 35.1 thousand guarantee obligations totaling more than 100.9 billion rubles. This volume of guarantees allowed small and medium-sized businesses to attract credit funds in the amount of more than 214.1 billion rubles.
The second new program is a state guarantee mechanism to support medium-sized businesses, implemented by the Vnesheconombank group.
It is a vertical system of counter-guarantees, structured as follows: a medium-sized company that meets certain requirements and lacks collateral can apply to OJSC SME Bank (operator of the guarantee mechanism) to obtain a bank guarantee. All obligations of SME Bank OJSC under bank guarantees issued under the mechanism are secured by a bank guarantee from Vnesheconombank, the amount of which is 40 billion rubles. In turn, the bank guarantee of Vnesheconombank is partially secured by the state guarantee of the Russian Federation. Since SME Bank OJSC guarantees 50% of issued loans, the volume of guarantee support is 40 billion rubles. will allow commercial banks to provide loans to medium-sized businesses in the amount of 80 billion rubles.
Support under this mechanism is provided to medium-sized enterprises operating in non-resource and non-trade sectors. The size of projects financed with guarantees ranges from 100 million to 2 billion rubles. (the size of one guarantee is from 50 million to 1 billion rubles). In this case, the enterprise must attract at least 20% of its own funds to the project. The loan term guaranteed by MSP Bank OJSC is from 2 to 10 years. The cost of guarantees for medium-sized businesses will not exceed 1.8% per annum and will be in the range of 1.4 - 1.8% per annum (for innovative projects, guarantees will be provided at the lower limit of the cost corridor).
The main advantage of the created mechanism is the combination of an instrument (bank guarantee) and a guarantor (MSP Bank OJSC), which will allow credit institutions to consider the received collateral as first-class.
Thus, according to clause 6.2.4 of Regulation N 254-P, quality assurance category I (accounted for at the full estimated cost) may include, among other things, “guarantees (guarantees) of legal entities, if these legal entities have an investment rating not lower than “BBB” "according to the classification of the rating agency S&P (Standard & Poor's) or a rating not lower than a similar one according to the classifications of Fitch Ratings, Moody's."
OJSC "SME Bank" as of the current date has the following investment ratings assigned by the S&P agency:
Thus, the guarantee mechanism for supporting medium-sized businesses, which appeared in August 2013, operated by a subsidiary of Vnesheconombank - OJSC SME Bank, can become an effective tool for reducing risks when lending to medium-sized enterprises.
It should also be noted that at present, at the level of the Government of the Russian Federation, a decision has been made to create a Credit Guarantee Agency, which at the first stage will perform the functions of coordinating the activities of regional guarantee funds.
A separate direction of development could be the creation of a credit risk management system using a bank guarantee from SME Bank OJSC when lending to entities. This system could provide methodological support taking into account the specifics of the mechanism under consideration. Also, within the framework of this model, it would be advisable to consider the use of other state guarantee instruments for supporting entrepreneurship presented in Russia, and based on the results of a comprehensive analysis of the impact of the guarantee mechanism, prepare proposals for its optimization and customization, taking into account accumulated data on international experience.
Conclusions
The demand for long-term lending from small and medium-sized businesses is high and continues to grow. At the same time, there is a further tightening of requirements for the financial condition of the borrower and collateral when lending to SMEs. The risks of lending to SMEs are also traditionally high; the collateral value of the collateral that an enterprise can provide is usually not liquid and does not cover the loan.
The created guarantee support mechanism can become an effective tool that increases the attractiveness of lending to specific investment projects of medium-sized businesses and reduces the risks associated with collateral securing a loan transaction.
- Lending to small and medium-sized businesses is traditionally classified as a high-risk category. However, statistics on non-returns and growth rates of portfolios in this market segment indicate the opposite. Without a borrower assessment model that has been proven over the years, banks are forced to constantly balance between the quality and cost of risk management techniques.
Shine light on dark places
According to the Central Bank, in 2007 the volume of loans provided to enterprises and organizations increased by 50.5% and reached 8.7 trillion rubles. The small business lending market, according to RBC estimates, grew somewhat faster - the increase over the year was 55%. But with such rapid growth, there are also many pitfalls.
The main problems that arise when lending to small and medium-sized businesses are the low transparency of this segment and the lack of reliable collateral. Anna Malysheva, head of the lending department for medium and small businesses at Rus Bank, stated: “In approximately 50% of cases, financial statements do not reflect the real financial and economic state of the enterprise’s activities, so we have to appeal to management accounting data, which is difficult to confirm and control in the future.”
As Andrey Kuznetsov, head of the small business development department at MDM Bank, said, his bank periodically conducts research on small and medium-sized businesses. And so far the results indicate that this market is at the “maturing” stage. Entrepreneurs, as a rule, think about lending only at the moment when they need to close the current “holes” of the business. Another inhibitory factor is the closeness of entrepreneurs to the bank and some of their cunning when working with an expert. “The majority of entrepreneurs,” the speaker noted, “receive a refusal precisely because during negotiations with the bank they do not know how to properly demonstrate their business.”
Although many banks have only a few years of experience in this segment (if we take it in bulk), credit institutions have accumulated enough experience to predict which skeleton might fall out of the closet.
Irina Bychkova, head of the lending and guarantees department at Investtorgbank, shared her experience: “Sometimes problems arising from conflicts between owners lead to serious consequences. There have been cases of business being taken away from the owner by company management, and attempts at raider takeover have been observed. Problematic enterprises are those that depend on a narrow circle of suppliers or buyers. There is also a risk of unbalanced growth of a company that has difficulties, including in repaying borrowed funds. Often, due to a lack of collateral, it is necessary to attract the personal property of managers, although, in essence, this is not entirely correct. Often you have to act as an auditor, sometimes problems are solved even by replacing employees in the company.”
These factors are rather subjective. With a serious and thoughtful approach to business and the loan application procedure, most issues can be removed from the agenda. But the difficulties that arise when lending to small businesses can also be of an objective nature, and even “experienced” employees can be baffled. In particular, Natalya Golovanova, head of the small and medium-sized business department of the Russian Development Bank, noted that when assessing enterprises that apply special tax regimes, it is more difficult to verify some of the borrower’s financial indicators.
Bankers are faced with the question of how to check enterprises operating under the so-called “simplified system”. Some experts believe that it can be extremely difficult to assess the real condition of the borrower using a minimum set of documents. Thus, even complete financial statements may not contain the necessary information and may not fully reflect current business processes, therefore, experts believe, unambiguous conclusions cannot be drawn only on the basis of official documentation.
Therefore, banks, based on global experience and their own data, are developing various methods for assessing the creditworthiness of a small enterprise.
From particular to general
There is a pattern in the banking industry: if a bank switches to a scoring model for assessing the borrower, it means that the product is becoming mass-produced. For example, after the introduction of scoring in mortgage lending, this product became available to the general public.
When lending to small businesses, the task becomes somewhat more complicated. Firstly, we are often talking about larger amounts. Secondly, it is much easier to check information and evaluate an individual than a business. If when lending to an individual, most questions can be answered unambiguously (age, marital status, number of children, etc.), then when assessing an enterprise, important factors are the personal qualities of the manager, relationships with partners, reporting form, relationships in the team, prospects for industry development, etc.
Nevertheless, today, when the interest of banks in lending to small and medium-sized businesses is constantly growing (fueled, among other things, by government programs), and those wishing to receive a loan are practically lining up, automated systems for assessing borrowers may come in handy.
Formally, there are two main methods for assessing a borrower: individual analysis and scoring. It is no secret that the scoring model has two main advantages: high processing speed and low cost. But at the same time, according to the law of “genre”, the quality of verification with this approach decreases. Ivan Khomenko, head of the small and medium-sized business lending department at Moscow-Credit Bank, explained: “The cost of the scoring assessment is certainly lower, since the application is reviewed in less time and employees with a lower level of qualifications and wages can be used for its analysis. As loan amounts increase, the borrower’s analysis becomes more individual, as the loan’s profitability in absolute terms increases.”
At the initial stage, it is important for the bank to correlate possible risks and cost items. It is for this reason that most credit institutions use a mixed system for assessing borrowers.
Thus, VTB 24 Bank, Russian Development Bank, UniCredit Bank, Moscow-Credit Bank, Soyuz Bank use a scoring system for assessing borrowers when implementing “microcredit”, that is, under programs in which the loan amount is the minimum for a given segment at this credit institution.
At Investtorgbank, the choice of borrower assessment methodology depends not only on the loan amount, but also on the region in which and how long the client has been working in this business. Chairman of the Board of Investtorgbank Vladimir Gudkov explained this approach: “Regions differ very much from each other in terms of wages, living wages, infrastructure and service sector development... This imposes certain features on the principles of doing business. The Moscow region has switched almost 90% to a “transparent” business scheme, so scoring can be used in the capital district. In the regions, on the contrary, “gray” schemes predominate, which means that it is necessary to carry out an individual assessment of the borrower.”
However, the methodology used by banks cannot always be called scoring. As the head of the department for work with SMEs, Alexandra Bugaeva (Svedbank), noted, the SME lending market in Russia is young: “The necessary credit history has not been accumulated, the work of the credit history bureau is not yet effective. In addition, scoring is applicable only for standard applications and, when making a decision, it uses a finite number of factors provided for when developing the program, which means it does not take into account the specifics of enterprises, possible additional factors, which is very important for SMEs, and which a credit analyst would pay attention to " Rus Bank has created a special simplified borrower assessment procedure, close to scoring. Anna Malysheva (Rus Bank) explained that this procedure is based on an analysis of certain stop factors and their compliance with the parameters of the lending program.
Natalya Golovanova (Russian Development Bank) drew attention to the other side of the problem of assessing the borrower: “Scoring assessment systems are not able to fully objectively evaluate the borrower’s business, and it often happens that the scoring system refuses “good” borrowers, while “bad” or even fraudsters , on the contrary, gives a positive answer. This is undesirable both for banks and for borrowers themselves.”
The line between a “reliable” and an “unreliable” borrower can be very thin. Two grocery stores in the same city may turn out to be completely different, since they have different suppliers, a different range of products, different managers, different points of sale, different service, etc. It is extremely difficult to take into account all these nuances mechanically. Therefore, bank employees prefer to make field trips and get acquainted with the production and management of the enterprise. Sometimes one visit can provide more information than a pile of documents.
Many banks are developing their own methodology for interacting with clients, which does not require a potential borrower to submit numerous certificates from various departments. A personal manager goes to the site and independently analyzes management reporting. In this way, it is possible, firstly, to minimize temporary losses on the part of the borrower, secondly, to analyze and provide for all possible risks and, thirdly, to optimize the process. The data is compiled into a standard form, on the basis of which it will no longer be difficult to make a fateful decision.
Of course, with this approach, the costs are immeasurably higher. But the choice of methodology can be influenced by many factors: from the size of the branch network and the volume of business to the minimum loan amount determined by the bank. As Eduard Issopov, a member of the board of UniCredit Bank, noted, lending to small businesses can be attractive for a bank if it has a clearly set up risk management system: either the required number of people have been recruited for underwriting, or a scoring system has been developed.
How to balance costs and acceptable risks - today credit institutions decide this issue on their own.
Reserves wholesale and retail
Improving the quality of the loan portfolio for banks is associated not only with the prevention of defaults, but also with a decrease in the amount of reserve funds.
Regulation of the Central Bank No. 254-P “On the procedure for the formation by credit institutions of reserves for possible losses on loans, on loan and similar debt” allows for the purpose of reserving to combine minor (no more than 0.5% of the bank’s capital) loans with similar conditions lending, into portfolios of homogeneous loans.
Alexandra Bugaeva (Svedbank) noted the general trend in the lending market: “Banks are striving to increase the number of standard loans by developing a wide product line. This allows loans to be grouped into portfolios in order to form reserves for the entire portfolio, which saves the bank’s time and effort, and also reduces the cost of the loan product for both the bank and the borrower.” Therefore, the bulk of loans belong to the category of so-called standard loans.
Anna Malysheva (Rus Bank) stated: “In accordance with the opportunity provided by the Central Bank, Rus Bank combines all loans issued under the lending program for medium and small businesses into portfolios of homogeneous loans, and also creates a reserve for these portfolios with the amount of reserve rates starting from 1%. This mechanism significantly simplifies the bank’s work with this business segment, reduces labor intensity and allows for a more flexible approach to the process of issuing and maintaining a large number of standard loans.”
It is important for banks to accurately determine which category of borrower a client belongs to at the assessment stage and attach it to the appropriate portfolio. If the system fails and there are delays in servicing the debt, this loan is removed from the portfolio of homogeneous loans, the borrower is assigned a lower quality category, and additional reserves are placed on the loan. Of course, such a procedure is undesirable for banks.
If a loan does not correspond to any portfolio of homogeneous loans in a number of ways (for example, the loan size exceeds the permissible volume or there is a shortage of collateral), the bank uses individual reserves. As a rule, the share of such non-standard loans in the bank is insignificant.
It is more convenient for banks to adhere to a standardized approach and not issue high-risk loans. However, as Alexandra Bugaeva (Svedbank) notes, credit institutions often accommodate enterprises halfway, taking into account their management statements when analyzing them: “If the bank is willing to take on a higher risk, this will make the loan more expensive for both the borrower and the bank. The bank is forced to increase the amount of reserves, which entails additional costs, and provide compensation for the risk. For the borrower, this is reflected in an increase in the interest rate on the loan and more stringent lending conditions. Banks encounter similar situations quite often.”
As practice shows, behind humanity in banks there is always a logical justification and financial guarantees. Perhaps it is precisely thanks to this approach that the risks in lending to small and medium-sized businesses remain quite low today.
How are the risks associated with small business lending assessed? What requirements have been established by the Bank of Russia for creating reserves for possible loan losses? The Department of Banking Regulation and Supervision of the Bank of Russia answered these questions specifically at BO’s request.
Risk assessment for loans provided to small businesses is carried out in the manner established by Bank of Russia Regulation No. 254-P dated March 26, 2004 “On the procedure for credit institutions to form reserves for possible losses on loans, on loan and similar debt” (hereinafter - Regulation No. 254-P).
In accordance with clause 3.1.1 and clause 3.1.2 of Regulation No. 254-P, an assessment of the credit risk of an issued loan (professional judgment) must be made by a credit institution based on the results of a comprehensive and objective analysis of the borrower’s activities, taking into account its financial situation and quality of debt servicing on the loan, as well as all information available to the credit institution about any risks of the borrower, about the functioning of the risk on which the borrower operates.
Regulation No. 254-P does not provide for any special requirements for the creation of minimum reserves for possible losses on loans provided to small businesses.
At the same time, the Bank of Russia pays due attention to the issues of creating conditions for the bank to implement more effective risk assessment procedures and the formation of reserves for possible losses on loans provided to these entities. The implementation of these approaches helps to save banks' labor costs on lending to small and medium-sized businesses, while at the same time the use of modern risk assessment techniques makes it possible to form adequate reserves for loan losses.
For example, as part of clarifying approaches to evaluating loans and forming reserves for possible loan losses in accordance with Bank of Russia Directive No. 1759-U dated December 12, 2006 “On amending Bank of Russia Regulation No. 254-P dated March 26, 2004” On the procedure for the formation by credit institutions of reserves for possible losses on loans, on loan and equivalent debt”, which entered into force on July 1, 2007, provides for the exclusion from the requirements of clause 3.14.1 of Regulation No. 254-P, which establishes a list of loans for which a reserve is formed in the amount of at least 21% of loans provided to pawnshops, cooperatives, small business support funds and used by them to provide loans to small businesses and individuals. This clarification makes it possible not to classify loans provided to these entities and used to provide loans to small businesses in the III quality category with the formation of a reserve in relation to such loans.
Directive of the Bank of Russia dated December 28, 2007 No. 1960-U “On amending clause 6.3 of Bank of Russia Regulations dated March 26, 2004 No. 254-P “On the procedure for credit institutions to form reserves for possible losses on loans, on loan and similar debt » it is possible to recognize as collateral for the purposes of Regulation No. 254-P the guarantees of business support funds established by constituent entities of the Russian Federation and funds to promote lending to small and medium-sized businesses, which allows the formation of a reserve for possible loan losses taking into account such collateral (that is, reducing the amount of the reserve by security amount).
In addition, in order to simplify the assessment of loans that are not significant in size, which, as a rule, include loans provided to small businesses, Regulation No. 254-P (clause 1.5 Chapter 5) provides for the possibility of combining similar loans into portfolios. This approach involves assessing the risk in relation to the entire loan portfolio based on data on the amount of losses for a group of similar loans for the previous period, while ensuring the comparability of all material circumstances relating to the nature, volume of loans, operating conditions of borrowers and other circumstances.
Currently, the issue of extending to loans granted to legal entities - small businesses, an approach that provides for the possibility of forming a portfolio of loans with a general level of impairment, determined by the presence and certain duration of overdue payments, established by paragraph 5.1 of Regulation No. 254-P, is being considered.
Currently, this approach is implemented in relation to loans provided to individuals.
Expert opinion
The prospects for the development of the small business lending system and the existing system of reserving funds for possible loan losses were assessed by the President of the Association of Regional Banks "Russia" Anatoly Aksakov:
Currently, banks independently determine the amount of reserves for loans issued to small and medium-sized enterprises, based on a thorough analysis of the financial and economic activities of enterprises. Of course, this slows down the process. In an effort to simplify the procedure, some banks issue loans to individual entrepreneurs, registering them as loans to individuals. After all, a loan can be issued to an individual within 24 hours, whereas upon receiving an application from an entrepreneur, it is necessary to conduct a financial analysis of the borrower. The Rossiya Association, in cooperation with bankers, has prepared proposals to amend Bank of Russia Regulation 254-P “On the procedure for forming reserves for possible loan losses.” In our opinion, the amount of reserves for possible loan losses to calculate the optimal interest rate for loans to small and medium-sized businesses should be at least 1-1.5% (for a portfolio of loans without overdue payments) and increase depending on the duration of the delay.
Loans to small and medium-sized businesses are classified as high risk. Most likely, they will become more expensive, and borrowers will face tougher conditions and refusals. However, in general, the implementation of the Central Bank’s initiative will contribute to the development of lending to small and medium-sized businesses and stimulate the activity of banks in this sector, because individual reservation of a mass borrower is too expensive for banks, and the introduction of a single algorithm will automate the process.
The problem of reporting and transparency of small and medium-sized businesses remains relevant. Many entrepreneurs still prefer to work according to “gray” and “black” schemes, without disclosing their turnover. Of course, this affects the possibility of obtaining a loan, significantly reducing it. An increase in reserves for possible loan losses will entail a tightening of lending conditions - because bankers will begin to check the information declared by a potential borrower in even more detail. Perhaps this measure will encourage many market participants who are still working under “gray” and “black” schemes to legalize.
But, as both theory and experience in assessing and managing credit risk show, credit risk has a complex internal structure. In addition to the risks caused by the characteristics of each individual borrower, there are other components. The SME lending market in our country is the last segment of the credit market, which is still insufficiently covered by banking services. How should systems be designed to assess and manage the risks of lending to enterprises in this segment?
What does credit risk consist of?
Simultaneously with the rapid growth of the credit market in Russia in the first decade of the 21st century. The risks associated with the lending business have also increased. Because of this, the importance of methods for assessing the creditworthiness of borrowers for Russian banks also increases. But to create and use methods for measuring and managing credit risks, you must first understand what types of credit risks exist and how they relate to each other. In Fig. Figure 1 shows a hierarchy of types of credit risk, which is formed into a three-level structure.
Figure 1
At a basic level, credit risk is represented by transaction risk. This risk is associated with the variability of the creditworthiness of individual borrowers, which arises in response to changes in economic, industry, socio-demographic and other factors affecting it. This risk manifests itself in the variability of the cash flow of enterprises or the income of individual borrowers. Because of this, the likelihood of repayment or, conversely, non-repayment of borrowed funds may also undergo changes. At the next level of the hierarchy there are risks associated with the “behavior” of large groups of loans, united by the principle of similarity into a “single large loan,” called a portfolio. Combining loans into a portfolio is dictated by the need to reduce management costs: it is assumed that such a portfolio can be managed as one large loan. But then such a meta-credit must be characterized by some parameters that allow one to assess its inherent risk - the so-called portfolio risk. The portfolio combines loans that are exposed to the same risk factors, which include both economic (for example, the state of demand in the industry) and social (for example, the level of income of the population) factors. Let's take an example of a possible situation with a portfolio of SME loans: in the event of a fall in demand, the weakest enterprises with the least diversified product portfolios or with the least diversified distribution systems will have their income fall first and, therefore, the likelihood of default will increase. The next, third, level of the hierarchy is represented by allocative credit risk - a risk caused by the distribution of bank assets across industries, regions of its presence and bank products. Different dynamics of development and different states of regional economies, industries and, for example, demand for different types of bank loans determine the variability in the quality of loan portfolios formed by the bank. Thus, investing in different proportions of funds in lending to the same composition of industries, which are offered the same credit products, and the industries are localized in the same regions, will lead to the fact that each of these different possible allocations of credit resources will generate its profitability and will be characterized by its level of credit, allocation, and risk. In this article, we will focus on the lowest—basic—transaction level of credit risk.
The foundation of transactional credit risk management systems is scoring
Let us begin the description of the methodological support for assessing and managing transactional credit risk with the definition of the term “scoring”. Credit scoring is a fast, accurate, objective and sustainable procedure for assessing credit risk, which has a scientific basis. Scoring always represents one or another mathematical model that correlates the level of credit risk (the probability of borrower default) with many different parameters characterizing the borrower - an individual or legal entity. Let us immediately note that there can be many scoring models for solving the same problem, for example, assessing the credit risk of SMEs. Moreover, each of these models is built according to an individual algorithm, uses its own set of factors characterizing the risk associated with lending to the borrower, and as a result receives a threshold assessment, which allows us to divide borrowers into “bad” and “good”. The point of credit scoring is that each loan applicant is assigned an individual assessment of credit risk - the probability of default. Comparing the probability of default value obtained for a particular borrower with a threshold estimate specific (we emphasize this) for each scoring model helps to solve the most difficult problem of choosing when issuing a loan: to give funds to a given borrower or not. Thus, scoring is essentially an automatic or automated procedure that classifies borrowers into the required number of classes. In the simplest case, there are two such classes - those to whom a loan can be issued, and those to whom it is strictly “contraindicated.”
Thanks to the use of scoring, the bank is able to reduce the number of “bad” loans by filtering the flow of client loan applications. As evidence, we present data on lending to individuals using the Fair Isaac scoring system. After “passing” the factors characterizing the borrower through the scoring model, we obtain a number (score) that determines the level of credit risk inherent in this borrower. This number takes one of the values in the range from 500 to 800. Each of the values in this interval characterizes a different probability of repayment of loan obligations by the borrower. That is, different credit scoring values imply different ratios of “good” and “bad” borrowers (Fig. 2).
Figure 2
In Fig. 2, the horizontal axis shows the value of the scoring score calculated by the model, and the ordinate axis shows the probability of default of the borrower corresponding to this scoring score. As can be seen from the figure, an increase in the borrower's scoring score is accompanied by a decrease in the probability of his default: the higher the scoring score, the more resistant a particular borrower is to credit risk. The figure illustrates this dependence: if 100 people with a scoring score exceeding 800 contact the bank, then only one of them will not return the funds taken. And vice versa, if 100 people with a scoring score of 499 or less contact the company, then 87 of them will not return the funds borrowed. Thus, by lending to borrowers with a high scoring value, the bank reduces the likelihood of loan default. This reduces losses and increases profits from lending activities without lowering lending standards.
How does scoring work? What's inside?
Several ingredients are needed to create scoring systems. We will begin our consideration with an analysis of scoring models used to assess the creditworthiness of enterprises, since scoring models have already been developed for enterprises, the structure of which is described in scientific periodicals. The most famous of these models is the E. Altman model, the first version of which was developed in 1968 based on statistical data from less than 70 American companies, half of which went bankrupt. This model is designed to assess the creditworthiness of large public companies in basic sectors of the American economy. The Altman model cannot be used to assess the creditworthiness of, for example, small businesses. Therefore, in 1984, researcher D. Fulmer created a special model for assessing the creditworthiness of small businesses with an annual turnover of about $0.5-1 million. The third of the models we are considering was created by the world famous company Fair Isaac - a recognized leader in the development of scoring models for lending to individuals . This is one of the least public models, with little known about its internal structure. Can anything unify scoring models for such different entities: large enterprises, small businesses and individuals? It turns out - yes, it can! This unifying point for all three types of models is equality:
where Z is the scoring value (scoring point);
a k are weight coefficients characterizing the significance of risk factors;
X k are risk factors that determine the borrower’s creditworthiness.
This formula is designed to calculate the credit scoring value, or a numerical value characterizing the quality of the borrower's creditworthiness. It is this (or similar) formula that is the “core” of almost any scoring system. In particular, in the Altman model it takes the form:
where the model coefficients take values 1.2; 1.4; 3.3; 0.6; 0.999 and are weights that determine the significance of risk factors; characters A, B, C, etc. — risk factors. For example, A is the ratio of working capital to total assets; B is the ratio of retained earnings from previous years to total assets; C is the ratio of earnings before interest and taxes to total assets; D is the ratio of market capitalization to the total book value of debt obligations; E is the ratio of sales volume to total assets.
In the Fulmer model, a similar formula for assessing creditworthiness takes the following form:
Z = 6.075 + 5.528V 1 + 0.212V 2 + 0.073V 3 + 1.270V 4 + 0.120V 5 +
+ 2.335V 6 + 0.575V 7 + 1.083V 8 + 0.849V 9,
where V 1 is the ratio of retained earnings of previous years to total assets;
V 2 - the ratio of sales volume to total assets;
V 3 - the ratio of profit before taxes to total assets;
V 4 is the ratio of cash flow to total debt;
V 5 is the ratio of debt to total assets;
V 6 - the ratio of current liabilities to total assets;
V 7 - logarithm of tangible assets;
V 8 is the ratio of working capital to total debt;
V 9 is the logarithm of the ratio of earnings before interest and taxes to interest paid.
The two scoring models described, like many other models, share a common property - their multidimensionality, which can be illustrated in the simplest case for two risk factors by a geometric “interpretation” (Fig. 3), where credit risk factors are certain variables X1 and X2 (their specific meaning is not important in this case).
Figure 3
Borrowers of two classes are represented in the figure by ovals of different colors: some, for example, “bad” ones, are represented by a gray oval, while others (“good”) are represented by a black one. It is not possible to distinguish “bad” borrowers from “good” ones by any single risk factor (due to the significant overlap of risk factor distribution functions - bell-shaped curves). In Fig. 3 bell-shaped curves along the axes of risk factors are formed by projecting groups of “good” and “bad” borrowers onto these risk factors. These projections—probability density functions—describe the frequency of occurrence of borrower properties used for scoring in classified groups. The larger area of intersection of these curves for any of the risk factors indicates the impossibility of distinguishing “bad” borrowers from “good” ones. Borrowers of different classes are very similar to each other when assessed by the first and second risk factors. The scoring model “searches,” using the statistics of previously processed loans, for such a “viewing angle” on the data in the space of risk factors (in our case it is two-dimensional, but in the general case it is multidimensional) so that objects of different classes viewed from this “angle” are as are not similar to each other. In Fig. 3, this “viewing angle” is indicated by a dotted line passing between the gray and black ovals and separating them. The perpendicular to this line is the scoring axis, projection onto which the images of “bad” and “good” borrowers makes it possible to distinguish them from each other. The intersection point of these straight lines gives the threshold scoring value (cut-off level) - Z*. The density functions of borrowers of different classes when projected onto the Z scoring axis become different from each other. Where do the numerical values of the coefficients that weigh the risk factors included in the model come from? These coefficients are the result of a training procedure, when, to configure the model, it is presented with available statistical data on loans issued and the effectiveness of this process (“bad” and “good” borrowers) and it iteratively “selects” the coefficients in such a way that the accuracy of recognizing “bad” and “good” borrowers “good” borrowers was maximum. In Fig. 3 is the selection of the angle of inclination of the straight line cutting the gray and black ovals, and the point of intersection of this straight line with the ordinate axis.
To determine the model coefficients, it is necessary that the statistical sample be divided into those groups of borrowers (in the simplest case, there are two of them - “bad” and “good”) that the scoring model should recognize. This problem is referred to as “credit graveyard.” Moreover, the data used to select ratios is subject to rather strict requirements: in order for these ratios to “feel” “bad” borrowers, there must be quite a lot of them (and in many of our banks the number of “bad” borrowers is small, since they are just trying to learn lend to SMEs). The figures characterizing the ratio of “bad” borrowers to “good” ones are typical for many banks: from 1 to 100 to 10-15 to 100 (in our experience, the order of magnitude does not vary much). Of course, as a result of the 2008 crisis, the number of “bad” borrowers grew to such an extent that many banks were close to collapse, but... problems with statistical databases still exist even for such banks, since their fall was due to high concentration enterprises of specific industries in their loan portfolios. Such industries that prevailed in banks' portfolios included construction and trade. It is not yet possible to talk about the possibility of implementing the statistical learning procedure, even with such “credit cemeteries”. In addition to the quantitative ratio in the training statistics of “bad” and “good” loans, an important factor is the total number of examples for each industry. And the formation of portfolios from such economically different sectors as trade and construction leads to the fact that we are trying to describe various objects with one model. Construction is characterized by a significant volume of fixed assets and a fairly slow capital turnover, while trade is characterized by a small volume of fixed assets and high capital turnover. Note that the more detailed the description of the borrower (which, of course, any lender strives for, using a larger number of characteristics), the greater the number of both “good” and “bad” examples the “credit graveyard” used should contain. Thus, to create a scoring system that uses a supervised learning procedure (this includes both models we are discussing), it is necessary to have a sufficient number of borrowers who have caused damage to the bank. There is a workaround that requires the use of expert knowledge. However, when choosing it, you should understand how you can assess the composition of the characteristics required for scoring, the significance of a particular sign of creditworthiness, and how to combine the opinions of many experts on this matter, since relying on the opinion of one person when issuing loans is dangerous. That is why, when formalizing expert knowledge, we still find ourselves on the “road” that leads us to statistical scoring.
To conclude this section, let us note that the algorithms we chose to train the scoring model belong to the class of statistical models: to build models, a training sample and a statistical training procedure are required. This means that the scoring module must have at least two modes of operation. The first, if data is available, is the mode for training the model: finding such model coefficients that will best classify a sample of statistical data. The second mode is the actual operation of the constructed model; in this mode, the model ensures the implementation of the classification of the input flow of borrowers into classes pre-established in the training mode. To implement the first mode—training a scoring model—several preconditions must be met. Firstly, statistical data must be pre-prepared in a special way: the data sample must be divided into two parts - training and testing. In the training sample, it is necessary to collect excess data on potential borrowers. It should include variables that could potentially be useful in deciding the creditworthiness of borrowers, and the selection of specific variables for inclusion in the scoring model is carried out during the learning process and without human intervention. Secondly, the user (banking specialist) should be able to choose from several types of scoring models (we are talking about the two most common algorithms: logistic regression and decision trees). All of the above is illustrated in Fig. 4.
Figure 4
The left side of the figure shows a block diagram of the functioning of the scoring module in the training mode, and the right side shows the operation mode. The first of the described operating modes of this module ensures the selection from a redundant set of features of that subset that provides the required level of classification, that is, it is in this mode that the mathematical scoring model is built (for example, logistic regression coefficients are determined). But to build a scoring model, it is necessary to somehow ensure that the training procedure is stopped, for which a special model is used that calculates the so-called ROC curve and the quality indicator of the scoring model - AUC. After the required level of model quality is achieved during the statistical training process, the statistical training procedure is completed and the model goes into operation mode. In Fig. Figure 5 shows the ROC curve obtained when implementing a scoring system in one of the Russian banks included in the top 100. The vertical axis on the graph shows the percentage of “bad” borrowers that the model catches from the total number of borrowers with insufficient credit quality. The horizontal axis shows the share of borrowers from the total flow of borrowers - potential clients who will be refused to receive credit funds. The right angle bisector going from left to right in the figure shows the scoring model, which “flips a coin” (random classifier) to make a decision. It is clear that the better the scoring model, the steeper the ROC curve should be.
In an ideal model, it should coincide with a right angle (upper left). This means that the model recognizes all “bad” borrowers in the training set, but does not unreasonably deny anyone a loan, and this cannot happen. As can be seen from the illustrative example characterizing scoring in an almost ideal situation, there is always some overlap between the images of “good” and “bad” borrowers. Therefore, in reality, the curve should lie in an intermediate position (between the bisector and the upper left corner). The quality of the model, the ROC curve for which is shown in Fig. 5 was quite high, with an AUC of 0.85.
Figure 5
Problems of obtaining a scoring model for SME lending
As mentioned above, the introduction of scoring in banking management is becoming very relevant due to the growth of both consumer and commercial lending. Let us outline the problems that the banking community will have to face along this path.
An attempt to apply the Altman model to Gazprom, Rosneft or LUKOIL, at least from a formal point of view, will not encounter any difficulties. There are official reporting data, there are weighting coefficients, which means it is possible to calculate an assessment of the borrower’s creditworthiness. But what to do if you need to evaluate not the largest companies mentioned, but “Father Fedor’s candle factory,” whose shares are not quoted not only on the NYSE, but even on the MICEX (remember that we are considering an assessment of the creditworthiness of SMEs). Even a quick glance at the corresponding formula is enough to see that out of five explanatory variables in the case of a “candle factory,” only four variables remain in the formula. The scoring (the Z value in the formula) will decrease (although, strictly speaking, this will only happen if D is replaced by 0), which in fact does not correspond to the case under consideration). As noted, the scoring value for a specific borrower is compared with a threshold value:
Z > Z* - “good” borrowers;
Z< Z* — «плохие» заемщики.
However, if it is impossible to take into account some variables (if the company’s shares are not quoted on the stock exchange, then variable D is absent in the description of credit quality), the “measuring instrument” itself, represented by the model, breaks down (a numerical assessment without taking into account factor D, for example, will always be shifted to the region worst scoring scores). In reality, the situation is even more complicated: without taking into account the variable D, we unwittingly change the geometry of the space of risk factors and, as a consequence, the weighting coefficients for other risk factors of the borrower. The model itself changes: the critical value of the scoring assessment (cut-off threshold), with which the assessment of each borrower is compared, becomes different. Consequently, in our conditions, the very choice of explanatory variables for assessing the scoring of Russian companies is a very non-trivial task. E. Altman built his model on data from only 60 companies, it reflects the very specific industry specifics of business (basic sectors of the American economy), it does not take into account the risks associated with business cycles in Russia and the risks inherent in companies with other industry affiliations . Therefore, we can state the following: the use of this kind of model by mechanically transferring it to our conditions becomes a powerful risk factor for credit assessment - what is called model risk in risk management. A very striking example of the risks associated with the use of statistical scoring models is given in one of the works devoted to studying the effectiveness of scoring models1. It states that the set of variables that form the score may change over time and that the “border” between the analyzed groups may not be linear (as shown in Fig. 3), but have a significantly more complex shape that cannot be described the simplest formula such as the Altman model. The authors of this article examined several mathematical approaches for constructing scoring, where 31 financial ratios were used as risk factors, characterizing various aspects of the company’s financial condition. They analyzed 11 scoring models developed in the period from 1931 to 1996, for the construction of which three mathematical approaches were used: discriminant analysis, logit model and genetic algorithms. The authors of the article showed two main points. The first is associated with the fact of changes in the composition of risk factors in the scoring model: it changes depending on time - the sooner before the future bankruptcy the scoring system needs to “see” it, the more variables must be taken into account in the model. The second is due to the fact that the boundary between classes of borrowers is nonlinear: the accuracy of estimates obtained using scoring based on genetic algorithms (they generate a nonlinear boundary) is significantly higher than that of models based on discriminant analysis (it generates a linear boundary). True, the first approach requires on average three times more variables than the second.
Consideration of the problem of credit risk management will not be complete if we do not touch upon the problems and possible ways to solve them when designing a credit risk management system not only for individuals, but also for legal entities. We will do this using the example of assessing the creditworthiness of small and medium-sized businesses. The purpose of this section of the article is to show that the proposed composition and architecture are preserved during the transition from lending to individuals to lending to small and medium-sized businesses. Proof of this fact will allow us to say that the composition of modules of the credit risk management system we propose and its functional architecture are universal.
Obviously, the first problem that we will encounter when moving to measuring the credit risk of small and medium-sized enterprises is the fact that sufficient factual material has not been accumulated for this type of borrower. To maintain the universality of our proposed functional architecture and the composition of the modules that form the system for managing the transactional part of credit risk, we propose to slightly expand the logical scoring model for SMEs. The proposed extension is represented by the block diagram in Fig. 6.
Figure 6
The essence of the changes in the logical structure of the scoring model is that the composition of algorithms traditional for solving this problem (decision trees, logistic regression, etc.) is expanding, and we propose to include SD-modeling algorithms in the model. The reasons why we offer this solution are as follows:
— in the absence of statistical data, a traditional set of algorithms turns out to be simply useless due to the inability to implement model training;
— for making lending decisions in the case of small and medium-sized businesses, the borrower’s creditworthiness is influenced by a significantly larger range of variables than for individuals;
— it is a widely known fact that the financial statements of enterprises in the Russian economy often very poorly reflect the real state of affairs in business due to the fact that they are very distorted due to tax “optimization”;
— the scoring model should reflect the industry specifics of SMEs.
In particular, in the context of the second statement, we can talk about the need to take into account not only financial information in the scoring model for SMEs. It is very important, due to scale effects, to assess the economic environment of the enterprises being scored. It is important to take into account environmental variables such as supply and demand in scoring models. With their variations, the cash flow of an enterprise and all its financial indicators, on which scoring models for legal entities are usually based, can undergo sharp changes. In addition, the state of enterprises, again due to their small scale, may also be affected by management features. Therefore, it is extremely important to be able to take into account the quality of enterprise management. And if it is not difficult to evaluate in numerical form the state of supply and demand in the industry to which the enterprise belongs, then assessing the quality of enterprise management is a non-trivial task. This requires that the model be able to consume expert information. The inclusion of an SD modeling block in the logical structure of the scoring model for SMEs allows us to adequately take into account all of the above requirements. To implement any SD model, as is known, as a first step it is necessary to draw up a cognitive map. A cognitive map is a diagram of causal influences in the form of a directed graph. The nodes of this graph represent variables that are included by the analyst in describing the creditworthiness of the enterprise, and the edges represent the causal influences of the variables on each other. In Fig. Figure 7 shows a cognitive map of a hypothetical forest industry enterprise that is engaged in timber harvesting and processing.
Such a change in the logical structure of the scoring model provides not only a solution to the four problems we listed earlier, but also the ability to dynamically assess the creditworthiness of SMEs. The use of the SD model in the scoring model allows us to generate the data that is missing for constructing statistical scoring. By directly taking into account the volume, timing and type of loans (Fig. 7), the model allows you to generate a “credit graveyard”, defining a state of default as the inability to repay the current debt within, say, three months. By varying the input indicators of the model, such as demand, supply, quality of management, loan parameters, in the process of SD modeling we obtain different trajectories for the enterprise’s cash flow, and therefore different conditions for the borrower to default (with different combinations of input parameters of the model, we have and various financial indicators). Having thus generated an artificial “credit cemetery”, we can apply traditional scoring algorithms in the form of the same logistic regression on the specified statistics in a standard way. But, in addition, we get a significant benefit from changing the logical structure of the model due to the fact that such a model allows us not only to solve the problem of lack of statistical data, which is important for the training mode, but also significantly expands the scoring functionality in the operating mode of the scoring model.
Let us explain what is new from the expansion of the logical structure for the last mode. Firstly, we can generate arbitrarily large data samples, which ensures the accuracy of statistical learning models that will take into account the industry-specific business of the SMEs being financed. Due to the absence of restrictions on the volume of generated artificial data on defaults of SMEs (which in real life never exists in the required quantity), we take into account industry specificity in the structure of the cognitive map: trading enterprises are characterized by rapid capital turnover and low fixed assets, while manufacturing enterprises are characterized by large values of fixed assets and a slow turnover rate. By using expert information in the cognitive map, we can model the impact of management quality on cash flows, and when using macroeconomic statistics, the impact of variations in supply and demand on the sales volumes of the company under evaluation. And finally, we can model the company’s cash flows over time using SD models in the scoring structure, which will allow us to more rationally formulate a payment schedule. In addition, the dynamism of the resulting assessments will allow us to naturally, within the same model, implement not only applicative, but also behavioral scoring, if during the loan servicing period the assessed enterprise suddenly has problems repaying its debt. This structure of the scoring model will allow us, when problems arise, to effectively assess the prospects for repaying the debt that has arisen and make more informed management decisions in relation to such a borrower.
Figure 7
Instead of a conclusion: what else is needed for a “quiet” life in the credit business?
Due to the fact that credit risk is structured hierarchically (has three levels - from transactional to allocation), to manage credit risks we will need two more sets of models. The first will serve the purposes of managing portfolio credit risk, and the purpose of the second is to support management decisions regarding the allocation of credit capital by region of the bank’s presence and by products sold. Moreover, even at the transactional level, to manage the credit risks of borrowers, it is not enough to simply classify the input flow of borrowers into “good” and “bad”. A number of other functions are needed, without which the use of scoring will not give satisfactory results. These two topics will be discussed later.
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