Loading...

Credit Score Application and Barriers Faced by Banks in the Credit Sector in Albania

by Valbona Çinaj (Author) Rebeka Ribaj (Author)

Textbook 2017 71 Pages

Business economics - Banking, Stock Exchanges, Insurance, Accounting

Excerpt

Content

Abstract

Credit Information System in Albania

Credit Scoring (CS), as a CIS product, helps to manage lending risks.

Methodology and data used

Banking system, credit risk and challenges in today's crises

Lending to the Albanian banking system.

Lending and Credit Information Systems (CIS)

Credit Registry in Albania and its role

Importance of using credit information systems

Theoretical and empirical treatment for CIS.

The credit information system based on credit scoring models produces credit score

Operation of credit registry in Albania.

Credit Information Systems, (CIS)

Credit Information System and Commercial Banks

The credit information system should be in continuous development

For the effective functioning of credit information systems

The Value of credit information system

Theoretical and empirical treatment for CS

Credit score empirical and deductive

Credit scoring models and credit information system

Meaning of credit score

Credit rating system

Data processing model

Steps to be followed for constructing the credit score model (CSM).

Test Kolmogorov-Smirnov (KS)

Results of the predictive model

Results obtained from the analysis

Conclusions and recommendations

BIBLIOGRAPHY

Abstract

Increased loan availability and competition between lenders have created the need for credit information exchange. The supervisory institutions, the World Bank, the IMF, the Ministry of Economy and Lenders see it as necessary to establish a Credit Information System (CIS) to help manage credit risk. Credit Information System CIS, collects data from various sources and provides credit information to consumers for diversified uses such as to predict their future behavior, also CIS reduces the effect of asymmetric information between borrowers and lenders, facilitating problems Of unfavorable selection and moral hazard, also reduces the monopoly effect of banks' loans by providing incentives for borrowers to repay their loans on time. CIS collects personal information about individuals, their financial records, and alternative data for individuals from a variety of sources called data providers that are usually creditors, lenders, utilities, debt collection agencies, and courts (i.e. Public records etc.). Data providers report their payment experience with the customer in the CIS credit information system. The data provided by data providers, as well as collected by SIK, are then aggregated into the SIK warehouse. Credit punishment is not limited to banks or credit institutions, but also to other institutions such as mobile phone companies, insurance companies, leasing companies, and so on. Credit scoring models are based on statistical techniques, including a variety of data mining techniques, always considering completing according to "there is no general technique known as the best statistical technique in building scoring models". This paper presents the effects that affect the current effect of the Credit Information System (CIS) in the Albanian reality in order to reduce credit installment delays during the credit cycle in the banking sector in Albania. There are a number of problems with bad credit for borrowers, as well as debts on lenders. From a lender's performance analysis one of the main causes is the lack of information exchange in the lending market. Also, the credit information system acts as a mediator and regulator of asymmetric information and also to increase transparency in the lending market. In the interest of all stakeholders in Albania (financial institutions, supervisory institutions, government, consumers, etc.) towards financial stability and economic growth in Albania, CIS becomes increasingly necessary towards the consolidation and maintenance of a sound and sound financial system. Credit scoring as a product of CIS through the application of data mining techniques is a growing trend. The decision tree, basic classification rules, expert systems, and any other techniques obtained outside the mini graph techniques and various hybrid combinations are usable and welcome in the scoring industry in the banking sector due to their explicit acceptance / rejection conditions of applicants. Selected literature addresses the challenges faced by banks' lending practices and the role of the Credit Information System (CIS). The growth in demand for loans has led to the need for more formal and more objective methods (generally known as credit scoring) to help credit providers decide whether to grant loans to a borrower, through technology advancement Computer and exponential database growth.

In some research it is noted that based on information from some countries around the globe, it is concluded that the existence of credit registers is linked to increased lending volume, lending to business, improved access to finance and a more stable banking sector. The same situation is also presented for Albania, according to this paper.

Key words : Credit information system, credit scoring, and credit scoring, credit scoring and credit risk, and data mining.

Credit Information System in Albania

In 1998, the World Bank studied the feasibility of the Credit Registry at the Bank of Albania. In 2000, the Albanian Banking Association negotiated with the Greek Credit Bureau to build a CIS. The agreement was abandoned when data were required to be kept in Greece, raising the issue of Albania's sovereignty. In 2006, the Albanian Association of Banks activated an initiative to establish a Credit Information System (CIS) as a private company in Albania, with an investment of more than 700,000 Euros. None of the above variants were accepted. The first variant brought about sovereignty as a Greek initiative, while the second variant was very expensive. Banks' demand for a credit registry and the commitment of the Bank of Albania to the IMF led to the realization of this project through technical assistance from the IMF. The Credit Registry at the Bank of Albania started operating on 2 January 2008, in the form of a public credit registry. Banks and IFJBs have access to information (customer exposure in the system and their performance regarding loan installment payments) about their applicants, but only after obtaining consent (through signing the bank authorization statement Or IFJB, to request a report from the Credit Registry by the Central bank of Albania.by the applicant of the bank. The existing privacy law for consumer discovery for any data kept in a government database is valid for the Bank of Albania.

In addition to the necessary but insufficient data provided by the census register and the lack of CIS, the following question is added.

What other data are used by credit institutions in Albania to recognize their customers?

Banks are more inclined to finance well-known customers, for whom more data can be found, because they are better suited to engaging in lending "relationships", a type of funding based primarily on " Soft "stuff collected by credit officers on a continuous, personalized basis, through direct contacts with individuals and businesses and with the local community in which they live / work. These are seen as ways of helping clients recognize loan applicants in the absence of other data that can be mastered at national and inclusive levels. Referring to the development of the banking system and the economy in Albania, a year-on-year importance is given to the establishment of the highest standards to demonstrate the effects of market expansion and successful restructuring. The gap in obtaining accurate data leads to inaccurate information and decision making while changing the lending business environment changes every day. Consequently, the creation of a Credit Information System (CIS) will be able to collect and analyze the data needed to achieve the necessary standards in the lending sector for individuals and businesses.

Credit Scoring (CS), as a CIS product, helps to manage lending risks.

It is based on an algorithm that foresees the applicant's classification in the future as the bad and good credit risk by recognizing the profile of the entity that belongs to a homogenous population mass. The algorithm derives the use of a multivariate analysis technique that allows identification of profile characteristics and sets the weights for each borrower by determining the status of whether it is good or bad. A loan is a transaction where the individual / business receives a sum of money and commits to repay the same amount at a later date, in one or more installments, adding interest repayment. Some subjects (individuals / businesses) have taken credit, some are refused others will apply in the future. One feature of the subjects is homogeneity and is the result of the needs of entities for financial services. Some do not repay the loan, some do not pay the installments for a long period of time becoming subject to bailiffs and, as a result, in most cases, financial institutions lose business effectiveness with the clients and as a result, these loans are returned to bad credit. On the other hand, the possibility of new loans is limited.

Thus, we conclude that the importance of the CS and good data management by public or private agencies or offices such as the CIS becomes a very important service, with a direct impact on banking sector health by reducing the risk of Lending. Banks use information on their customers' behavior so that they can predict future behavior.

Likewise, the importance of scoring of credit lies in several main pillars such as:

1. All customers are given the opportunity to create their own credit history and use it as a lending asset (credit availability) using various financial services.
2. State institutions / legal entities become more informed about the level of risk of their partners as well as prospective clients, to seek more information about their behavior of repayment of financial obligations.
3. Banks and financial institutions are protected by their bad partners / clients.

As far as banking activity is concerned, performance is related to the creation of an added value, an optimal ratio between costs and benefits. Being imposed by the capitalization of investments in new technologies, this implicitly leads to increased risks, and thus, the relationship between performance and risk has become very significant nowadays. The study will be of benefit to various players in the financial industry, including regulatory bodies, credit officers, investors, bank clients and the general public. The staff of financial institutions will be able to focus on selecting customer quality to carry out feasibility analysis for potential borrowers for credit delivery. The government may use this research especially in credit institutions to moderate lending policies such as the level of customer access to a direct impact on socio-economic and life-longitude consumer standards, which make up the majority of the developing countries’.

The main purpose is to identify the problems in the lending process by Albanian credit institutions and to give recommendations on the possibility of addressing some of them through CS report as CIS product for credit risk management as the main risk for each bank or institution Financial sector in Albania as well as anywhere in the world in order to maintain financial stability and economic growth.

In response to this goal and market demand in the field of credit, this study aims to use data exploration techniques for building a model that provides good creditors, probability of opportunity for bad creditors who have already received a loan. The created model is the result of a combination of grouping and classification techniques. The research questions on the reality of second tier banks will help to better structure with the aim of assisting in a realistic assessment of the Albanian lending reality, the functioning and problems faced by banks during the lending process and Future action plans for the best management of disadvantaged situations. Consequently, it was sensible to carry out interviews to understand the role of such lending systems in banks operating in the Albanian banking system and other practices pursued by banks, in the conditions when the need for credit is growing (or stopped) with Quick rates and the return of these loans or bad loans are currently increasing. Minimizing the problematic of these bad loans has become a significant mission for the health of the Albanian banking system and the Albanian economy and is set as one of the most priority tasks to be monitored and to deliver as soon as possible. The government in these situations together with the Bank of Albania is taking steps to improve the banking lending system in several directions: improving the credit registry (by improving some parameters for better calculations and for a greater reach of customers. Within the capacity of considering a long-term plan a working group at the Prime Ministry is working to structure the legal and procedural basis for the implementation of a "Data Bureau" credit information agency in 2017, which will be the solution to Real problems related to the lending system in Albania. This Credit Information Agency will be a powerful tool in minimizing these loans in the future.

The work on this paper is based on key research questions, which focus on combining data exploration techniques to improve the efficiency of the models used to predict creditworthiness. This question addresses the practical use of data exploration techniques by providing an optimal solution to the credit validation process. Referring to the reality of Albanian credit and the procedures on how this process is realized, this paper raises the following research questions: How can we help the lending sector, in the process of forecasting good customers by using the credit information system and credit punishing as a product of this credit information system?

The answer to this question will be detailed in this paper, beginning with a theoretical approach to the data exploration techniques and their application in the field of customer behavior analysis, to proceed further with the explanation of the process functioning of a credit information system and completed in a credit scoring model.

To answer the main question, the work in this dissertation will be based on several issues that are:

How can a credit information system be defined, its functioning in improving the reduction of bad credit in the lending system?

How can banks improve their lending process using this forecasting model?

How can we differentiate or measure consumer validity?

How can we apply a model to anticipate good customers and automatically reject bad customers? How can we come up with predictive techniques to segmentize customers from their behavior to use for different application policies or strategies?

How can the pooling and classification techniques be combined to build an efficient forecasting model under the specific conditions of a micro bank?

Which of the representative algorithms of the different categories of pooling methods (sharing methods) performs better in combination with the classification methods for building a prediction model?

What is the path used for recognizing individuals or businesses SMEs (what kind of data in the absence of a national and more comprehensive credit information system exists today in Albania?

Is it possible in the Albanian reality to create a SIK or a data bureau?

Can all consumers benefit from being regular in payments related to state / private entities?

Do banks use programs or software systems for customer or business assessment?

Are the data collected correctly and is it credible to generate different models in assisting banks for decision-making on granting loans?

Does the second-tier banks and the central bank have a panacea, an objective set by management boards to enable the parent bank to create or borrow from rating models for risk-minimizing clients in the banking sector?

What is the path used for recognizing individuals or businesses SMEs (what kind of missing data at national and inclusive level?

How the Credit Registry has resulted. Created in 2008 at the Bank of Albania and problems arising from the analysis for addressing its improvement?

Methodology and data used

The methodology used is based on the alternation of primary and secondary data. Primary data are provided through completed questionnaires in the banking system. These questionnaires (databases and surveys) are completed in the largest banks in the country, which account for over 80% of the banking market. Questionnaires were filled by bank staff in the main cities of Albania such as Tirana, Durrës, Elbasan, Fier, Vlora, Korça and Shkodra. A good part of the information is also provided through interviews with academics, financial and banking experts, and managers of second tier banks, employees and former Central Bank employees. Above, these were the sources in collecting primary information, which was then used for the realization of the econometric model. While secondary information has served different literature, for which it has been researched at the National Library, at the University of Tirana at the Faculty of Economics and at the European University of Tirana library. Also served by the annual reports of various level banks Second, Bank of Albania's annual reports, economic bulletins, and various academic books on the field of study. Another inexhaustible source of information was the electronic libraries of various Western universities, the various electronic addresses of international financial institutions, as well as official information about the banking system. Academic works such as scientific research, conferences or lectures on the field of research have complemented the secondary resource framework. So we are based on:

First, through the distribution of structured and semi-structured surveys / questionnaires Structured, when the two targeted target groups are:

A) Customers and Customers

B) Second level Banks in the country, as well as

Second, organizing focus groups with individual bankers.

Third, interviews with supervisory bodies and questionnaires were based on best international principles / practices. The questionnaires and focus group guides included aspects related to the problems of the domestic credit sector, customer knowledge of products Banking services, bank transparency with customers, the functioning of the credit registry, with the rise in the number of bad loans at the national level. Knowing and using an instrument such as scoring of the loan to assist the loan officer as well as reducing the risk of non-return on credit or the growth of bad credit.

Secondary information consisted in collecting literature, recognizing the concept of credit scoring or credit score financial / banking area, studying the legal and institutional framework for the protection of the financial consumer in the world.

Both types of information, both primary and secondary, have served in the analysis conducted in this study and have helped the methodology used to see what the banking system's problems in Albania are and how we can improve the situation.

As noted in the introduction to this paper, the methodology used includes four main tools:

1. Literature, scientific, contemporary literature on international reality and Albanian credit. Facing the problems of increasing the number of bad loans at this time of crisis where this impact is more dominant.
2. Questionnaires with bank / database clients
3. Integration of information and generalization gained from worldwide and Albanian legislation on the instruments used and their functioning.
4. Survey for senior employees of the banking and financial sector in Albania. One of the most significant conclusions of this paper is the growing interest in improving the lending role and the reflection of the change in the use of scoring credit as an important. For the banking sector the product obtained from a SIK (recommended) is created as the experience of the most developed countries. The research methodology requires the collection of relevant data from the specified documents and the compilation of databases to analyze the material and to achieve a more complete and historical understanding of the objects set.

Banking system, credit risk and challenges in today's crises

Banking is a practice, business or profession almost as old as human existence itself. But in literature it can be deeply rooted in the existence of the Florentine bankers in the Renaissance days. It has emerged from the Stone Age - in the Victorian era - and so far in technology development, ATMs, credit cards, debit and online banking in the Google era.

The origin of the bank's name derives from the Italian "banco" table, used during the Renaissance by Florentine bankers who use it to make their transactions on a table covered with a green color tablecloth. The word credit comes from the Latin word believe - "to believe" because of the credibility of the consumer in the promise of repayment at a measurable value -"tobelieve."It has been said that consumer credit dates back to Babylon's time about 3000 years ago, from the Middle Ages to the present day, the meaning of consumer credit, lending to the massive consumer market is something that has become a dominant phenomenon. Credit risk has always been a matter of concern not only to bankers but to the entire business world because if the obligations are not fulfilled in time, these other risks are also affected by the other partners involved in the business. The term "commercial bank" is used to distinguish it from an investment bank. During the time of depression and after the 1929 stock market crash, the American Congress demanded that commercial banks only engage in banking activities (accepting deposits and lending, as well as other service-based payments), while banks Investments were limited to capital markets. This division today is no longer mandatory.

Credit risk is often associated with the main function of a bank. The degree of bank activities, their relatively low profit margins often combined with a high leverage, makes the area of risk assessment and control, a vital function for each bank. Credit is defined as a belief in a person's ability and desire to pay back the money that is offered to him at a later time. Another way of determining the loan may be a consideration for the likelihood of a person or company that will pay the liabilities at a certain point in time. Banks produce loans to support commercial agricultural products as well as service enterprises. These in turn generate jobs, increase purchasing power and in this way grow growth and generate savings. So starting from this point of view bank failure, especially in such places, damages the entire social structure across the country. Such experiences have seen Latin and Asian countries that have a potential for rapid global influence. Banks produce financial products and services to customers, while managing a number of multiple risks that are related to liquidity, capital adequacy, loans, and interest. Typically, they try to optimize their return risk. Risk management and benefit management are closely related to one another. Taking over risk is a fundamental requirement for future profitability. In other words, today's risk can be seen as a reality tomorrow. Thus, banks cannot live without managing these risks. Through many different dangers, credit risk has a potential for a "social" impact due to the large number of people who may be affected, such as borrowers, business owners, employees in these businesses, and so on. Today in the world the impact that a bank's failure can have a wider effect than the local one and depending on its magnitude and its integration with the markets the bank's failure can have a global effect. In order to regulate effective management of credit risk exposure, a bank needs today a sophisticated system based on analytical tools for measuring, monitoring and managing risk control. Building models and adapting them to groups and sharing the weights that are thought to be predominant by various statistical forecasting methods is very tangible today and gives an impact on a country's economy. We have the most developed and used and commonly applied CS approaches in the banking sector where credit applications are evaluated, focusing on a retail segment for loans because we have had a rapid increase in the volume of these loans in these Years. Log analysis has been identified as the most used for CS methods in the banking sector. Although other non-parametric methods are comprehensive in terms of recognition. Other methods have the potential for application in the countries in transition as well as in various countries hit by the financial crisis from time to time. The 2008 global financial crisis in the US is a complex and multi-faceted process. The main causes of this crisis may be abnormal crediting in the US, which is related to an inappropriate risk management practice by financial institutions. The distribution of this crisis in the world can be explained by the fact of changing and investing in world savings in the US, as these investments moved along various asset livelihoods by producing artificially inflated assets. Where did this crisis arise? ?

Everything started with the real estate crisis in the US. The crisis spread in other forms of assets and affected not only mortgage companies and investment banks, but also in international banks. The global liquidity crisis, coupled with a withdrawal of deposits from banks exposed negatively to the crisis, and later increased the anxiety for a spread of the epidemic of deposits on a global scale. The collapse of structured investment products like collateralized debt bonds changed the global liquidity distribution to the securities market, causing a price swing in this part. Banking risks and financial crisis in the banking system peaked in September 2008, where many funds were relocated to risk-free securities when it declared bankruptcy and the US banking system faced this crisis.

The world economy continues to be in the midst of the crisis, affecting both advanced and emerging economies. All major advanced economies declined, while developing countries slowed down suddenly. Low-growth countries are exposed to the current downturn. More global than in previous events, as they are more integrated than previously with the world economy through trade, foreign investment and remittances. The impacts of the crisis considerably for these countries appear through the diminished demand of their exports. Experts point out that the global economic crisis has had a relatively moderate impact in Albania, because the country is partially integrated with world markets and still does not have a capital market. Observers expect it to be still difficult to get credit because of not Credit repayment or a very high level of bad credit in Albania, and foreign investors will face higher fiscal burdens. Public investment, largely supported through foreign debt, will increase in cost. Experts point out that one of the major consequences of the global crisis will be a decline in remittances by Albanians living mainly in Italy and Greece. Given these facts, business in many sectors will have a difficult time to increase investment and employment. In order to be better informed of the challenge encounter challenges are always drawn that need to be taken into consideration so as not to be unprepared. Under such conditions, we list some of the reasons, impacts and lessons of this crisis:

A) The risk assessment methods used so far have not been the best, starting from where we are. The factual risk associated with the various financial products turns out to be higher than the estimated one. Consequently, the rate of return from these financial products is not high enough to offset even the greatest risk. Need to use methods for realistic assessment of different risks, taking into account their interaction.

B) Another lesson we draw from this crisis is the global banking systems. From this crisis, the most profitable and non-affected by the crisis were the universal banks (with high diversification of both assets and resources) and traditional or "primitive" banks.

C) Who benefited from the crisis and who could not cope with the crisis?

(1) Those who are the main initiators of this crisis: managers, bankers, banking and financial intermediaries who created different products on stock markets with the promise of not real benefits to financiers by providing for themselves a benefit to Immediate in the capital invested by others.
(2) Strong banks which over the years re-invested their earnings for themselves and others.
(3) Strong banks, which at an incredible speed, realized the acquisition of other competitive banks at a symbolic price, thus strengthening their position in the banking market in order to be the first in the future after the crisis.

Lending to the Albanian banking system.

The first decade of transition, focused on stabilization reforms of liberalization, creating suitable conditions for the growth of the banking system. Until the early 2000s, the banking system in Albania was considered inactive in lending. The strong entry of foreign banks, either as "New Entrants" or through the privatization of state-owned banks, led to an increase in lending to the economy, leading to the beginning of a credit boom in the middle of the second decade of transition in the economy.

At the end of 2011, the Albanian banking sector continued to consist of 16 fully private-owned commercial banks, with about 92 percent of total assets invested in foreign capital. Banking system assets grew at a higher rate in 2011, both nominal terms (by 13.1 percent) and as a contribution to Gross Domestic Product (reaching 85 percent), which indicates a stronger activity Intermediaries of the banking sector. During 2011, deposits had a significant increase of 13.1 percent, mainly as a result of household deposits growth, reflecting customer orientation towards saving and strengthening their confidence in the banking sector. Despite the challenges of the domestic demand for lending and tightening lending conditions, the banks' loan portfolio in 2011 grew by 15.3 percent, upward trend over the quarters. The increase was mainly due to the provision of loans to the private sector, and was more apparent in loans denominated in domestic currency. The deposit-to-deposit ratio at end 2011 stood at about 61 percent, indicating that the banking system is less dependent on external financing and has sufficient capacity to develop. The maturity structure of new loans issued in 2011 indicates that banks are mainly focused on shortterm lending (63 percent of total new loans) and reflects the need of the domestic economy for immediate liquidity. The level of non-performing loans reached 18.8 percent in December 2011, influenced not only by difficulties in some sectors of the economy but also by the slowdown in credit growth rates in the last 2-3 years. As a result of the growth of non-performing loans, and in accordance with the Bank of Albania's normative criteria, banks increased their provisions, which has significantly affected the net result of banking activity and the system's profitability indicators, which were lower Compared to the previous quarter, despite the net result of the system for the whole year being positive.

Banking activity in 2011 was expanded and reorganized with the opening of new branches and agencies and the closure of some existing ones.

Referring to the statistics of the third quarter of 2014 from the statistical data published by the Bank of Albania, the indicator of loans to customers by banks is presented according to the following chart:

Abbildung in dieser Leseprobe nicht enthalten

Chart 1. Loans to customers by banks. Central Bank of Albania

Credit continued to increase, marking a positive trend.

Abbildung in dieser Leseprobe nicht enthalten

Chart 2. Total Gross Credit. Central Bank of Albania

According to the sectors of the economy, the sector with the lowest credit quality is' Real estate, rent, etc. The ratio of non-performing loans to total credit to this sector is 47.43%.

Abbildung in dieser Leseprobe nicht enthalten

Chart 3 NPL Sector (%).Central Bank of Albania

It should be noted that the weight of credit to this sector is very low to the total credit. Among the weighted sectors, the indicator appears worse in construction loans, reaching 43.67%.

Abbildung in dieser Leseprobe nicht enthalten

Chart 4. The weight of each sector in the loan portfolio, Central Bank of Albania

The new 86% loan belongs to business and 14% to households. The most credited sector continues to be the sector of 'Trade, Vehicle Repair and Household items' (34.17% of new business loans).

Abbildung in dieser Leseprobe nicht enthalten

Chart 5. New Credit Weight to Business by Sector, Central Bank of Albania

Lending and Credit Information Systems (CIS)

The decision-making process for granting loans is considered a continuous process. Companies or individuals apply for a loan and after the loan is approved, followed by signing the contract and disbursing the loan. Credit is the most traditional service provided by banks for their clients, is the core of banking activity. What is also evidenced by the interviewees for this paper is that:

"Credit controls our lives today"; Limits or expands our financial stability. Increases or decreases the quality of life; Open or close the doors for employment and promotion opportunities, affecting our income; Limits or expands purchasing power.

With continued development and changes in the credit industry, credit products are playing an increasingly important role in the economy. Economic globalization and the newly created Internet service channels offer consumers the opportunity to seek and solve their lending problems without regional restrictions and time limits. Due to this trend, the creditor should now be ready, willing and in Able to provide loans to businesses in other countries around the world. Credit institutions are facing a drastic competition all over the world. Increased demand and increased competition resulting from a new economic environment offer new opportunities but also push new demand for institution lending. The role of technology is very important in credit management. As the volume of loans increases, the number of bad loans has an upward trend. Financial institutions should invest considerable resources to develop efficient and sophisticated tools to assess and control credit risks. CSM based technology includes techniques that are now called data mining techniques referring to technology development. Statistical methods such as linear and logistic regression, linear programming, and decision tree are used for the development of CS systems.

[...]

Details

Pages
71
Year
2017
ISBN (eBook)
9783668560031
ISBN (Book)
9783668560048
File size
1.3 MB
Language
English
Catalog Number
v376119
Grade
Tags
credit information system credit scoring credit risk data mining albania

Authors

Share

Previous

Title: Credit Score Application and Barriers Faced by Banks in the Credit Sector in Albania