Peculiarities of Credit Risk Management in Credit Unions
This artical examines credit risk management with the view based on theoretical and practical aspects in credit unions. The analysis of the scientifical references and empirical investigations showed that quality of the credit portfolio and the level of the delinquent loans depend on economical, leg...
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Інститут економіки промисловості НАН України
2010
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| Назва журналу: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Цитувати: | Peculiarities of Credit Risk Management in Credit Unions / E. Freitakas, V. Rimsiene // Економічний вісник Донбасу. — 2010. — № 4(22). — С. 157-163. — Бібліогр.: 16 назв. — англ. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| _version_ | 1860248874884530176 |
|---|---|
| author | Freitakas, E. Rimsiene, V. |
| author_facet | Freitakas, E. Rimsiene, V. |
| citation_txt | Peculiarities of Credit Risk Management in Credit Unions / E. Freitakas, V. Rimsiene // Економічний вісник Донбасу. — 2010. — № 4(22). — С. 157-163. — Бібліогр.: 16 назв. — англ. |
| collection | DSpace DC |
| container_title | Економічний вісник Донбасу |
| description | This artical examines credit risk management with the view based on theoretical and practical aspects in credit unions. The analysis of the scientifical references and empirical investigations showed that quality of the credit portfolio and the level of the delinquent loans depend on economical, legal and social environment as well as the credit union prudence in lending activities and loan allocating process. Fuzzy goal programming techniques can be efficiently applied in developing sophisticated investment decision making models to provide feasible solutions for credit union portfolio management and credit risk problems. Key words: credin union, credit risk management, fuzzy goal programming.
У статті досліджено теоретичні й практичні аспекти управління кредитним ризиком в кредитних спілках. На основі теоретичних і практичних досліджень обґрунтовано, що якість кредитного портфелю і кількість прострочених кредитів залежать і від економічного, юридичного та соціального середовища, і від обережності та обачності кредитної спілки у процесі надання кредитів. Методи fuzzy goal програмування можуть бути успішно використані при створенні складних моделей управління портфелем кредитів і вирішенні проблем управління кредитними ризиками в кредитних спілках. Ключові слова: кредитна спілка, управління кредитним ризиком, fuzzy goal програмування.
В статье исследуются теоретические и практические аспекты управления кредитным риском в кредитных союзах. На основе теоретических и практических исследований обосновывается, что качество кредитного портфеля и количество просроченных кредитов зависят как от экономической, юридической и социальной среды, так и осторожности и осмотрительности кредитного союза в процессе предоставления кредитов. Методы fuzzy goal програмирования могут быть успешно использованы в создании сложных моделей управления портфелем кредитов и решении проблем управления кредитными рисками в кредитных союзах. Ключевые слова: кредитный союз, управление кредитным риском, fuzzy goal програмирование.
|
| first_indexed | 2025-12-07T18:40:32Z |
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| fulltext |
157
Економічний вісник Донбасу № 4 (22), 2010
Eduardas Freitakas, Rimšienė Vita
Introduction
Currently, economic downturn and financial
markets instability activates discussions about the
difficulties in banking sector. Though, credit unions are
acting in the same retail banking sector as the banks,
they often remain “unnoticed”.
Most of the scientists describe credit unions as
cooperative — autonomous and voluntary organizations
of people, functioning to fulfill the financial needs of its
members. Credit unions are financial-social institutions
trying to strive financial and common bond goals. These
organizations are spread all over the world and serve to
communities in various regions of different economic
development level. In year 2008 there were about 50
thousands credit unions active in 97 world countries,
uniting more than 186 million members [1, p.2]. One of
the main activities of credit union is lending, so credit
risk management becomes the primarily problem of every
credit union. Besides, due to peculiarity of credit union
functions, it is necessary to analyze credit risk in details
and to have a sound mechanism of credit risk
management.
Though variuos authors discuss issues of credit
union management the problem of credit risk management
are not the object of their research.
The object of this research paper is credit risk
management of credit unions investment portfolios.
The goal of the research paper is to analyze the
peculiarities of credit risk management in credit unions
and suggest a model for effective portfolio management.
The methods of the research: analysing the
specialties of the credit risk management in credit unions
were applied methods of sistematical analysis and
synthesis of scientifical references and empirical
investigations.
The research in the field of the credit risk
management in credit unions is significant as a
background for the futher academic and practical
researches, also it would be ensured to the possibility to
shape future perspectives of the sound credit risk
management mechanism in the context of social aspects.
Conception of the Credit Risk in Credit Unions
World Council of Credit Unions (WOCCU)
compiled PEARLS1 monitoring system. This system
measures assets, liabilities and capital, and recommends
an „ideal“ structure for credit unions. The following
ideal targets are promoted: 95% productive assets
composed of loans (70-80%), and liquid investments
(10-20%); 5% unproductive assets composed of
primarily fixed assets (land, buildings, equipment etc.)
[2, p.6]. The philosophy of credit union ( lot.per se —
in itself) prescribe the direction, how the credit union
should conduct. By depending and giving an opportunity
to borrow, the credit union gets its clients loyalty.
Considering the unique form of such organizations,
there also should be active “credere” (trust) principle
that determines confidence from both (client and credit
union) sides.
Scientists distinguish 3 main principles of rational
crediting: profitability, liquidity and security [3, p.13],
[4, p.266]. It must be pointed out that simultaneous
observing of all 3 basic principles is impossible, because
they contradict and form “triangle” among themselves.
It is shown in figure 1.
The triangle reflects the basic directions of the
commercial bank crediting policy, however, credit
unions are social-financial institucions that do not require
maximum profit, so this triangle could be transformed.
Greater attention should be paid to security and liquidity
dimentions. Crediting activities are not oriented to the
capital gains only. Despite of that in the competitive
environment of financing system aim for minimal profit
is the basis for the survival and the stable functioning.
Credit union should provide liberal credit policy that
meets its mission and goals. So, this enables to draw a
conclusion that credit union should function in a socially
and financially optimal way. However at this point credit
risk becomes the main risk aspect that must be
particularly analyzed.
Usually the conception of credit risk is described
as stochastic dimension, emphasizing the possibility
of potential loss and also paying attention at the point
УДК 336.73:330.131.7
Eduardas Freitakas,
Vilnius University, Kaunas Faculty of Humanities, PhD (Economics),
Rimšienė Vita,
Vilnius University, Kaunas Faculty of Humanities, master,
Lithuania
PECULIARITIES OF CREDIT RISK MANAGEMENT IN CREDIT UNIONS
1 Protection, Effective financial structure, Asset quality, Rates on returns & costs, Liquidity, Signs of growth
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Економічний вісник Донбасу № 4 (22), 2010
Eduardas Freitakas, Rimšienė Vita
of deliberate default. On the other hand credit risk may
be characterized as “loss category”, determined by the
quality of debtor creditworthiness, however generally
it is risk of loss that is necessitated by possibility of
default. The significance of loss might be measured
by interest rate and evaluated by future cash flow. But
adapting this credit risk conception for credit unions
should be taken into consideration the social — reliance
perspective and then the risk of loss might be evaluated
with lower interest rate, because of two reasons; firstly
the profit of crediting is not the premier purpose,
secondly there should be rejected assumptions of
deliberate default. In addition credit unions usually
charge lower interest rates on loans, and this
conditionally decreases the possibility of delinquency.
Besides intercommunication inside the common bond
creates social responsibility and therefore this should
determine better credit repayment rate. Consequently,
analyzing creditworthiness of credit union member’s,
must be paid attention at the features of credit risk
and the uniqueness of credit union as social-financial
institution.
Overview of the Level of the Problem
Investigation
The credit risk of credit’s unions is different because
of several reasons: size, geographical concentration,
liquidity needs and extension of credit only to their
members [5, p.19].
As we have mentioned above credit r isk
management for credit unions is topical. Investment
issues of credit unions require qualified decisions and
are complicated enough, because it needs to combine
different aims: maximization of shareholder’s wealth,
minimization of risk and staying social organization
that distributes resources in order to satisfy the
different needs of credit union’s members. Analyst deal
with large amount of data, complex policy guidelines,
and several other historical factors that need to be
considered for making future investment decisions.
They are required to weight all the data and information
and make the recommendation that puts the credit in
the best spot — balancing the upside opportunity with
minimum possible risk. To accomplish this task,
analysts need flexible professional tools and techniques
for construction of efficient portfolio from available
loan products.
Most of the studies have identified the credit risk
management system development needs of commercial
banks. There are a few studies which have focused on
credit unions.
There were analyzed credit risk management of
variuous countries from the legal side, that regards
regulatory institutions designated requirements [6,
p.151]. Researches of credit unions risk management
made in Ireland, Lithuania, Poland, USA and Britain
showed that credit risk regulation depends on
economical situation of particular country as well as
credit unions development stage [7, p.71-73]. The
higher stage of development of credit unions system
it was in particular country, the lower juridical and
legitimate regulation influence manifested on credit risk
management of credit unions. For example credit union
systems in Ireland or USA operate in upper
development stage, so there are lower capital adequacy
requirements. In addition, in more advanced credit
union systems there are more liberal requirements of
loan collaterals and loan provisioning/charge schedules
are more flexible [8, p.184-187].
In year 2009 Australian scientists support the
hypothesis that credit unions manage their capital
position by setting a short term target profit rate (return
on assets) which is positively related to asset growth
and which is aimed at gradually removing discrepancies
between the actual and desired capital ratio. Desired
capital ratios vary significantly across credit unions.
There is little evidence of short run adjustments to the
risk of the asset portfolio to achieve a desired capital
position. [9, p.448-450].
However, another research revealed [10, p.271-
279] that credit unions are not useful to pursue short-
term goals. The latter authors applied statistical-
regression analysis and studied changes in Australian
credit union crediting policies. It was observed the
following patterns:
1) Credit unions with a higher proportion of total
revenue in the form of interest on residential loan and
Fig. 1. Crediting principles for credit unions.
Source: extended by authors based on [3 p.13], [4 p.266].
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Економічний вісник Донбасу № 4 (22), 2010
Eduardas Freitakas, Rimšienė Vita
lower proportion of revenues in interest on personal loans
have significantly lower risk and returns.
2) Credit unions that diversify by increasing the
revenue share of transaction fees on loans and deposits,
matched by the reduction in the revenue share of interests
on personal loans, will increase their risk while reducing
returns.
These statements have asymmetric effects: lower
risk and lower returns due to increased interests of
residential loan have a lower decline rate than the
increased risk and reduced returns due to increased
transaction fees[10 p.280].
The results [6, p.155] provide some support
(regression coefficient of the Herfindahl concentration
index is positive and statistically significant) to the
traditional view that diversified credit unions have lower
risk exposure. Besides, credit unions that offer wide range
of loans attracts more members
Credit unions face to geografical concentration
risk that limits the need for evaluation of relative
financial strengths and ability to assess and adequatelly
monitors the risk of the investments [5. p.19].
Concentration of credit risk can be divided [11, p.36]
in following groups:
• A few large credits or big correlation of credit-
related borrower;
• Loans related common risk factors (economic
activity, type of a loan, currency, members etc.)
If the credit portfolio consists of a number of
large and correlated credits (fig. 2, ball 1) then both
credit concentration groups are significant. Conversely,
if there is a large amount of non-correlated loans —
credit concentration risk disappears (fig. 2, ball 2).
Credit institutions try to manage this risk combining
amount of the credit and reliance of credit risk (fig. 2,
ball 3) [12, p.97]. Credit unions provide large amount
of small credits and they have high correlation level
(fig. 2, ball 4), therefore credit concentrations risk
management in credit unions becomes very
complicated. Scientists [13, p.2] offer to analyze and
estimate correlation level between the borrowers.
Managing concentration risk it is also necessary to
analyze such concentration sources as: duration and
collateral of the credit [14, p.21].
Credit risk managers in credit unions should also
take into account the unemployment trends and
business infrastructure in the region. The study [15,
p.93-94] based on the business activity of the
employer/parent organizations, two subgroups of
occupational credit unions were examined — one with
parent organizations in relatively stable industries and
other with parent organizations in relatively unstable
industries:
• Credit unions with relatively unstable parent
organizations invested about 68 percent of their assets in
various types of loans. By comparison, credit unions with
relatively stable parent organizations invested a higher
share of their assets (over 72 percent) in their loan
portfolio. If the loan portfolio is a credit union’s riskiest
way to hold assets, these relative proportions suggest
that credit unions with relatively unstable parent
organizations appear to be adjusting appropriately for
increased risk by being less “loaned up”.
• Credit unions with relatively unstable parent
organizations allocated smaller shares of their loan
portfolios to unsecured loans. This appears to be a prudent
decision given the employment volatility of the parent
organization and the greater potential for members to
become unemployed or employed elsewhere. Since the
management and directors of the credit union are likely
to have less information about the “creditworthiness” of
members no longer employed by the credit union’s parent
organization, obtaining collateral for loans is one way of
reducing risks.
• Finally, credit unions with relatively unstable
parent organizations allocated a smaller share of their
loan portfolios to real estate loans. This too appears to
be a risk reducing lending strategy. Credit unions
(particularly small ones) generally have less experience
with this type of loan. Not only do these types of loans
tend to be more costly to administer, they also have
figured prominently in recent credit union insolvency
problems.
Credit union credit risk management problems
involve estimation of desired target levels which cannot
be precisely defined because a condition with a strictly
binding condition has no practical value. Therefore
assigning imprecise target levels to some or all objectives
rather than fixed targets is more reasonable and is possible
in modeling using Fuzzy Goal Programming techniques
(FGP) [5 p.20].
FGP is based on fuzzy logic, not on traditional logic.
Fig. 2. Credit concentration risk
Source: compiled by authors based on [12 p.96]
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Економічний вісник Донбасу № 4 (22), 2010
Eduardas Freitakas, Rimšienė Vita
Traditional logic makes decisions “yes” or “no”, there is
no third resolution. But real world problems have both
quantitative and qualitative assessment, so there is some
kind of uncertainty, to solve these problems helps fuzzy
logic [16 p.81]. FGP applications can provide better
solutions in creating efficient portfolios for credit unions,
it is proposed simple and weighted additive FGP models
for creating and rebalancing efficient portfolios for credit
union portfolio management considering multiple and
conflicting fuzzy objectives.
Fuzzy Goal Programming Model as the Tool of
Credit Risk Management
Fuzzy goal programming model development
requires estimation of the fuzziness in variables such as
resource availability, annual return, and operating costs
related to investment decision problems. Although
associated goals with the variables are defined based on
the best estimation of management, yet they are in fuzzy
sense [5 p.21].
The steps and flowchart (see figure 3) of the
solution procedure for both models can be presented as
follows:
Step 0: calculate credit risk concentration level in
credit union (Herfindahl index);
Step 1: formulate FGP model;
Step 2: to identify the direction of fuzzy type of
goals and specify lower tolerance limits for „ >~ “type goals
and upper tolerance limit for „ >~ “type goals.
Step 3: construct the membership function of the
fuzzy goalsbased on the desired tolerance limits.
Step 4: apply the model. For the simple additive
model go to Step 4.1 or for weighted additive model go
to Step 4.2.
Step 4.1: solve problem, go to Step 5.
Step 4.2: solve problem according to weights for
different goals. If the results do not serve, then solve the
problem using different weighting structure. Go to Step
5. Continue until the solution is satisfactory.
Step 5: if the solution is satisfactory then report
the solution as the best compromised solution in the
Fig. 3. Solution procedure
Source: compiled by authors based on [5 p.18-20]
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Економічний вісник Донбасу № 4 (22), 2010
Eduardas Freitakas, Rimšienė Vita
current decision aking context. Otherwise, go to Step 2
and specify different lower and upper tollerance limits
and continue.
Step 6: calculate newly formed credit risk
concentration level and compare the results.
The following notations will be used in the general
model of the credit union investment problem:
Index: i — index for the loan i ∈ {1,2, ... I};
Variables and parameters:
Xi :amount of the money invested in produc (loan)
type i;
T : total available funds for the investment;
Ii : interest income from product (loan) type i;
II : total interest income from all products;
Oi : average loan operating cost per product (loan) i;
O : total operating cost for all products (loans).
Fuzzy goal constraints. Fuzzy goal constraints are
defined in the formulations of the general mathematical
model of the credit union portfolio investment problem
as follows:
1. Total Investment: The fuzzy goal equation for
available funds can be expressed as:
TX
I
i
i <∑
=
~
1
;
2. Investment Income: The fuzzy goal equation
for investment income can be defined as:
IIXI
I
i
ii >∑
=
~
1
;
3. Operating Costs: the goal equation for operating
costs of investment can be defined as:
OXO
I
i
ii <∑
=
~
1
;
4. Management of Limits of variuos investment
products:continious monitoring of the limits of different
investment types is one of the most challenging task for
credit unions. These limits for product types can be
defined mathematically as follows:
a) Cash and Money Market Securities
(Products): to ensure liquidity, a minimum amount is
required to be invested in short term sequrities such as
Fed funds or money market funds. The goal equation
can be defined as follows:
TaX
nc
cc
c %
1
≥∑
=
;
Where {c1, c2, ..., cn} are the cash and money market
fund investments and {c1, c2, ..., cn}⊆ {1,2, ... I}.
b) Home Mortgages products: Historically,
home mortgage loan products have been the safest
investment in comparison to other secured loans
because the value of a house appreciate and loan
balance decreases with the passage of time. Therefore,
the total investment in this category must be at least
certain percentage af all available funds. The goal
constraints can be expressed as:
∑∑
==
≥
I
i
i
h
hh
h XbX
n
1
%
1
;
Where {h1, h2, ..., hn} are the home mortgage loans
and {h1, h2, ..., hn}⊆ {1,2, ... I}.
c) Personal Loan Products: Personal loan products
are usually unsecured loans with a higher interest rate to
compansate for higher risk, and therefore, shuold have
strict limits on this category of loans to minimize risk.
The goal constraints can be expressed as:
∑∑
==
≤
I
i
i
p
pp
p XcX
n
1
%
1
;
Where {p1, p2, ..., pn} are the personal loans and
{p1, p2, ..., pn}⊆ {1,2, ... I}.
c) Small Bussiness Loan Products: small business
loans usually are medium terms (3-5 years). The goal
constraints can be expressed as:
∑∑
==
≥
I
i
i
s
ss
s XdX
n
1
%
1
;
Where {s1, s2, ..., sn} are the small business loans
and {s1, s2, ..., sn}⊆ {1,2, ... I}.
e) Agricultural Loan Products: agricultural loans
have parallel risk as small business risk. The goal
constraints can be expressed as:
∑∑
==
≥
I
i
i
z
zz
z XeX
n
1
%
1
;
Where{z1, z2, ..., zn} are agricultural loans and {z1,
z2, ..., zn}⊆ {1,2, ... I}.
e) Other Loan Products: this group of the loans
include loans for education, loans for work trips etc.
The goal constraints can be expressed as:
f)
∑∑
==
≤
I
i
i
k
kk
k XfX
n
1
%
1
;
where{k1, k2, ..., kn} other loans and {k1, k2, ...,
kn}⊆ {1,2, ... I}.
Similar to the above constraints or limitations, other
limitations can be included for the model formulation. At
this moment this model is in the stage of testing . The
LINGO (version 10) software has been used to run these
models.
We may conclude, that credit risk management
162
Економічний вісник Донбасу № 4 (22), 2010
Eduardas Freitakas, Rimšienė Vita
depends on the goals and the strategy of the credit union,
crediting policy aspects, macroekonomic situation,
regulation and social environment. Therefore, dealing with
problems of the credit risk, decision maker should rely
not only on traditional credit risk management models,
bul also integrate other areas of science and rely on new
methodologies.
Conclusions
• Credit unions as financial intermediation provides
both financial and social functions, therefore, credit risk
management becomes complex and complicated process.
At this pont of view credit risk management becomes
unique- balansing needs of the credit unions members
with minimum posible risk.
• The analysis of theoretical and empirical
literature seeking to reveal the pecularities oc credit
risk mangement enabled to draw the following
conclutions; Firstly, level of the credit risk depends
on the single credit union policy guidelines (lending
policy and priorities, composition of the credit
portfolio, amounts of the operating income etc).
Secoundly, credit unions making investment decisions
should involve the estimation the concentration of
credit risk in order to escape high delinquencies and
foreclosure of loans. Thirdly, funds allocating process
are very sophisticated and risk mangement systems
should take into consideration a wide range of
features related to l iquidi ty needs, geografic
concentrations, unemployment trends and business
infrastructure in the region etc. All these aspect should
be stated in the corporate r isks management
strategies.
• Quality of the credit portfolio and the level of the
delinquent loans depend on economical, political, legal
and social environment as well as the credit union
prudence in lending activities and loan allocating process.
So, credit unions in decision making process should rely
not only on traditional credit risk management models,
bul also integrate other areas of science and rely on new
methodologies.
• Fuzzy goal programming techniques can be
efficiently applied in developing sophisticated investment
decision making models to provide feasible solutions for
credit union portfolio management problems for
constructing efficient portfolios regarding specific credit
union credit risk and social environment.
• FGP models have considerably promise in terms
of control, flexibility and real world applicability over
the traditional models, providing investment planning
and management tools and techniques for credit
unions. FGP models can be easily integrated to the
existing risk analysis and management models
available for credit unions for rebalancing of
investment portfolio based on economic conditions,
demand of products and risk factors more often.
These models are flexible enough to be extended to
handle large sizes of portfolio for example portfolio
of whole credit unions sector.
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e 7 e c 5 c e 3 1 e 2 3 % 4 0 s e s s i o n m g r 11 0 & b d a t a = J
n N p d G U 9 Z W h v c 3 Q t b G l 2 Z Q % 3 d % 3 d # d b =
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163
Економічний вісник Донбасу № 4 (22), 2010
Eduardas Freitakas, Rimšienė Vita
Services Research. Vol.27, nr.3 p.259-281. <http://
www.springerlink.com/content/l2487g007p42v718/
fulltext.pdf>. 11. Deutsche Bundesbank (2006).
Concentration risk in credit portfolios. Monthly Report,
June, p. 35-53. <http://www.bundesbank.de/download/
v o l k s w i r t s c h a f t / m b a / 2 0 0 6 /
200606mba_en_concentration.pdf>. 12. Valvonis
Vytautas (2007). Kreditų koncentracijos rizikos
vertinimas ir valdymas. Ekonomika Nr.77 p.94-113. ISSN
1392-1258. <http://www.leidykla.eu/fileadmin/
Ekonomika/77/str7.pdf>. 13. Wilcox James A. (2006).
Performance Divergence of Large and Small Credit
Unions. Iš FRBSF Economic Letter, August p.1-3. <http:/
/ w e b . e b s c o h o s t . c o m / e h o s t /
pdf?vid=9&hid=13&sid=9c1f6c04-f35f-402f-8165-
e7ec5ce31e23%40sessionmgr110 > ISSN 0890-927X.
14. Frame W. Scott, Karels Gordon V., McClatchey,
Christine. (2001). The Effect of the Common Bond and
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9955-25-495-9.
Freitakas, E., Rimš ienė , V. Peculiarities of
Credit Risk Management in Credit Unions
This artical examines credit risk management with
the view based on theoretical and practical aspects in
credit unions. The analysis of the scientifical references
and empirical investigations showed that quality of the
credit portfolio and the level of the delinquent loans depend
on economical, legal and social environment as well as
the credit union prudence in lending activities and loan
allocating process. Fuzzy goal programming techniques
can be efficiently applied in developing sophisticated
investment decision making models to provide feasible
solutions for credit union portfolio management and credit
risk problems.
Key words: credin union, credit risk management,
fuzzy goal programming.
Фрайтакас Е., Рімшиєне В. Особливості уп-
равління кредитним ризиком у кредитних спілках
У статті досліджено теоретичні й практичні ас-
пекти управління кредитним ризиком в кредитних
спілках. На основі теоретичних і практичних дослі-
джень обґрунтовано, що якість кредитного портфелю
і кількість прострочених кредитів залежать і від еко-
номічного, юридичного та соціального середовища, і
від обережності та обачності кредитної спілки у про-
цесі надання кредитів. Методи fuzzy goal програму-
вання можуть бути успішно використані при створенні
складних моделей управління портфелем кредитів і ви-
рішенні проблем управління кредитними ризиками в
кредитних спілках.
Ключові слова: кредитна спілка, управління кре-
дитним ризиком, fuzzy goal програмування.
Фрайтакас Э., Римшиене В. Особенности уп-
равления кредитным риском в кредитных союзах
В статье исследуются теоретические и практи-
ческие аспекты управления кредитным риском в кре-
дитных союзах. На основе теоретических и практи-
ческих исследований обосновывается, что качество
кредитного портфеля и количество просроченных
кредитов зависят как от экономической, юридичес-
кой и социальной среды, так и осторожности и ос-
мотрительности кредитного союза в процессе пре-
доставления кредитов. Методы fuzzy goal програми-
рования могут быть успешно использованы в созда-
нии сложных моделей управления портфелем кре-
дитов и решении проблем управления кредитными
рисками в кредитных союзах.
Ключевые слова: кредитный союз, управление
кредитным риском, fuzzy goal програмирование.
Received by the editors: 22.10.2010
and final form in 01.12.2010
|
| id | nasplib_isofts_kiev_ua-123456789-24019 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 1817-3772 |
| language | English |
| last_indexed | 2025-12-07T18:40:32Z |
| publishDate | 2010 |
| publisher | Інститут економіки промисловості НАН України |
| record_format | dspace |
| spelling | Freitakas, E. Rimsiene, V. 2011-07-08T11:56:13Z 2011-07-08T11:56:13Z 2010 Peculiarities of Credit Risk Management in Credit Unions / E. Freitakas, V. Rimsiene // Економічний вісник Донбасу. — 2010. — № 4(22). — С. 157-163. — Бібліогр.: 16 назв. — англ. 1817-3772 https://nasplib.isofts.kiev.ua/handle/123456789/24019 336.73:330.131.7 This artical examines credit risk management with the view based on theoretical and practical aspects in credit unions. The analysis of the scientifical references and empirical investigations showed that quality of the credit portfolio and the level of the delinquent loans depend on economical, legal and social environment as well as the credit union prudence in lending activities and loan allocating process. Fuzzy goal programming techniques can be efficiently applied in developing sophisticated investment decision making models to provide feasible solutions for credit union portfolio management and credit risk problems. Key words: credin union, credit risk management, fuzzy goal programming. У статті досліджено теоретичні й практичні аспекти управління кредитним ризиком в кредитних спілках. На основі теоретичних і практичних досліджень обґрунтовано, що якість кредитного портфелю і кількість прострочених кредитів залежать і від економічного, юридичного та соціального середовища, і від обережності та обачності кредитної спілки у процесі надання кредитів. Методи fuzzy goal програмування можуть бути успішно використані при створенні складних моделей управління портфелем кредитів і вирішенні проблем управління кредитними ризиками в кредитних спілках. Ключові слова: кредитна спілка, управління кредитним ризиком, fuzzy goal програмування. В статье исследуются теоретические и практические аспекты управления кредитным риском в кредитных союзах. На основе теоретических и практических исследований обосновывается, что качество кредитного портфеля и количество просроченных кредитов зависят как от экономической, юридической и социальной среды, так и осторожности и осмотрительности кредитного союза в процессе предоставления кредитов. Методы fuzzy goal програмирования могут быть успешно использованы в создании сложных моделей управления портфелем кредитов и решении проблем управления кредитными рисками в кредитных союзах. Ключевые слова: кредитный союз, управление кредитным риском, fuzzy goal програмирование. en Інститут економіки промисловості НАН України Економічний вісник Донбасу Finance Peculiarities of Credit Risk Management in Credit Unions Особливості управління кредитним ризиком у кредитних спілках Особенности управления кредитным риском в кредитных союзах Article published earlier |
| spellingShingle | Peculiarities of Credit Risk Management in Credit Unions Freitakas, E. Rimsiene, V. Finance |
| title | Peculiarities of Credit Risk Management in Credit Unions |
| title_alt | Особливості управління кредитним ризиком у кредитних спілках Особенности управления кредитным риском в кредитных союзах |
| title_full | Peculiarities of Credit Risk Management in Credit Unions |
| title_fullStr | Peculiarities of Credit Risk Management in Credit Unions |
| title_full_unstemmed | Peculiarities of Credit Risk Management in Credit Unions |
| title_short | Peculiarities of Credit Risk Management in Credit Unions |
| title_sort | peculiarities of credit risk management in credit unions |
| topic | Finance |
| topic_facet | Finance |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/24019 |
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