3-Project_FIN_955PROJECT_LAST VERSION (1)

UNIVЕRSITY ОF WОLLОNGОNG IN DUBАI
Loan Pricing and Lending
practices of Banks
FIN-955
Studеnts:
Hassan Wahdan – 4670711
Mаzеn Аl Hаkim – 4605445
Irinа Zuykinа – 4606759
Аigеrim Ilсhibаеvа – 4639467
Dubai-2014

1
EXECUTIVE SUMMARY
Nowadays, the importance of studying credit risk arises, because it is one of the dangerous types of risks,
which can be the result of insolvency. Loans pricind plays here very important role. There are a lot of
technics to determine loan pricing; however they were combined into quantitative and qualitative models.
There are a lot of technics to determine loan pricing; however they were combined into quantitative and
qualitative models. The latter is making decision with respect to collected information from borrowers and
market situation in time borrowing. As for market specific factors, they include the business cycle and the
level of interest rate. Quantitative models determine price of loans by specific calculation.
In this paper two strategies of loan pricing will be considered: “Cost-plus Pricing” for countries of MENA
region (UAE, Saudi Arabia and Egypt, within last five years) and “Credit scoring” model on the example of
UAE`s bank. The goal of this project is to recognize main factors that influence loan pricing.
In accordance with “cost-plus” approach the Egypt region has more risky and unstable situation in lending
activity for banks; this followed by increasing interest rates for loans to cover all costs and possible losses.
UAE and Saudi Arabia in terms of Risk Weighted Assets Ratio UAE market is more reliable as they high
rate of capital which can recover losses easily.
The results of ran constructed regression model for these 3 countries have confirmed these calculations and
identifyed the most significant factors affected loan charging.
The “Credit scoring” approach in order identified whither the borrower of the loan is eligible or not and if the
borrower is eligible what interest rate will be applied for him and how much is the amount. This system put
important variables to identify the risk of the borrower and depend on that risk pricing will be applied, as
banks put higher interest rate on the high risk customer.

2
Contents
Introduction ................................................................................................................................................ 3
1. Background and Literature review......................................................................................................... 4
2. “Cost-plus Pricing” model .................................................................................................................... 5
2.1. Observation of reserched countries................................................................................................. 5
2.2. Data and Regression model ........................................................................................................... 9
2.3. Regression analysis: interpretation and testing of variables .............................................................10
2.4. The advantages and disadvantages of using credit scoring model: ...................................................15
3. Credit Scoring Model ..........................................................................................................................16
3.1. The Methodology of this model to input interest rate:.....................................................................16
3.2. The results from applying this methodology: .................................................................................18
3.3. Testing credit scoring results by running regression model: ............................................................18
3.4. The advantages and disadvantages of using credit scoring model: ...................................................19
Conclusion.................................................................................................................................................20
Refferences................................................................................................................................................21
APPENDIX ...............................................................................................................................................23

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Introduction
Credit risk is the risk of default or refuse to return required amount of money. Usually, it is calculated based on
the borrowers' overall ability to repay. Nowadays, the importance of studying credit risk arises, because it is one
of the dangerous types of risks, which can be the result of insolvency. A history about the latter started in 1980
with bank loans to less developed countries, then in 1990 problems with commercial real estate loans as well as
the end of decade with sub quality auto loans and credit cards. Due to history repeats itself; we should not
underestimate value of the credit risk. That why every time credit rating agencies like S&P, Moody’s, Fitch
evaluate overall countries level of riskiness. There special focus is on emergency markets. As we know, it is
economy in progress becoming advanced and differs with high return, as a result, a relatively high growth of
GDP. Therefore, the aim of this paper is to compare calculating credit risks in emergency markets such as the
United Arab Emirates, Saudi Arabiya and Egypt. We begin this project with a glance at types of loans, then
various models, which are used to measure credit risk.
According to the Investopedia (2014), loan is a certain amount of money lending to another party to gain interest
in future. It has been noticed that there are four types of loans that are commercial and industrial, real estate,
individual and others. Commercial and industrial loans (S&I) are used to finance working capital and there
maturity could be short term (few weeks) and long term (eight years and more). Moreover, amount of borrowing
money can be from 100 000 $ up to 10 million $, which is considered as syndicated loans. As for type of
payment there are two options either spot loan or loan commitments, which means to withdraw all at once or to
have specific amount and period of time to convert into cash from an account. Recently, the preference
increasingly is giving to commercial paper in place C&I, because of short term borrowing from money market
mutual funds. The next type of loans is real estate loans, which are based on mortgage with a long term
maturity. Following this individual or consumer loans are provided by financial institutions and oil companies
for auto and personal needs. Because of huge number of consumers and accordingly different ability of
creditworthiness they have usury ceilings. It is maximum rates can be charged from borrowers and depends
from place of residents. And finally, loans that are not relevant to above mentioned types are other loans issued
by government, foreign banks, etc.
There are a lot of technics to determine loan pricing; however they were combined into quantitative and
qualitative models. The latter is making decision with respect to collected information from borrowers and
market situation in time borrowing , in other words, it affected by borrower – specific factors and market
specific factors. To borrower specific factors refer reputation, leverage, volatility of earnings and collateral. As
for market specific factors, they include the business cycle and the level of interest rate. Quantitative models
determine price of loans by specific calculation. There are many approaches to estimate loan’s price, however
there will be analyzed cost – plus pricing and credit scoring models based on above mentioned emergence
markets.

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1. Background and Literature review
This research is based on the studies of authors such as Repullo and Suarez (2004), Ruthenberg and Landskroner
(2008), also Lim, et al. (2014).
The first study is investigated by Repullo and Suarez (2004) that influence of credit risk to the loan pricing
essences of capital requirements, because of the internal ratings based (IRB) approach from Basel II, systematic
risk’s one and only factor is loan default rates. Therefore, the low risk loans’ equilibrium will be equal to lower
than required Basel I, meanwhile the high risk loans’ equilibrium will be exactly the same like Basel I.
Following this, the banks, which are non-specialized, after adopting the IRB approach will prefer to securitize
their high risk portfolios. After computation the level of the social cost of bank default, they confirm the IRB
capital requirements of Basel II. As a result, it is too high charges for risky loans, because of tremendous size of
the suggested cost. The finding of the research is that Basel II ignores net interest income from performing
loans; however it is a buffer attached to the capital versus credit losses.
The second paper is written by Ruthenberg and Landskroner (2008), where have been analyzed impacts of Basel
II and IRB to loans rates for the individual borrowers and corporations. For research they run a scenario, which
the bank in an imperfect competition market is neutral to risk. It has been found that higher quality (lower risky)
customers will be interesting in big banks, meanwhile, lower quality (higher risky) customers will prefer small
banks, because of loan rate reduction (these banks are adopted IRB). As a result, big banks will offer loans to
less risky costumers; however, small banks will be more risky, afterwards insolvent.
Finally, Lim, et al. (2014) has analyzed the importance of timely losses from syndicated loans. They have
proved that after recognized losses on time bank may charge new loans spread higher; they tested this
conception to among series of banks. Despite using another approach to determine the influence by Ball (2001),
the results of both studies are the same.
Taking these points into considerations, in this project we will observe and identify the two strategies of loan
pricing: “Cost-plus Pricing” and “Credit scoring”. The goal of the next parts is to recognize main factors that
influence loan pricing. We will discuss the above mentioned loan pricing models, test them and distinguish
differences between them.

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2. “Cost-plus Pricing” model
In this pricing model, the interest rate charged on loans has four components:
 cost of funds (interest cost on deposits or money market borrowings used to fund the loan);
 cost of servicing the loan (operation expenses: application and payment processing, administrative
costs- wages, salaries and occupancy expense);
 cost of possible defaults of the loans (risk premium);
 adequate return on bank capital (profit margin, return on equity).
Cost of funds is the interest rate that bank pays to depositors for using theirs money to fund loans. The loan rate
charged will be affected by the level of interest rate paid to depositors and on savings; thus the higher interest
rates to depositors, the higher interest rate for the loan and vice versa. In case of loan funding comes from other
sources than deposits, the expenses for these funds will influence the loan pricing as well.
Cost ofservicing the loan is operation expenses. These expenses are associated with providing and maintaining
the loans. It includes marketing, application and screening; provided loans are followed with expenses for
monitoring, collections, statements. Thus all administrative and other operation costs must be included into the
loan price.
Risk premium is evaluated with cost of possible loan default. If loan non-payment occurs the banks must
somehow recover the cost of these losses. Practically, these loan losses are factored into the loan price. Usually,
default probabilities are calculated from historical data of losses. There is the large difference between default
probabilities calculated from historical data and those implied from market prices. Market prices measure
assumes that bank is able to generate higher return providing more risky loans due to higher interest rate.
Retained earnings build the capital which fund loans, cover losses and is needed for business grows; thus
capital requirement imply including some profit margin into loan pricing.
In this paper we will analyse and compare the loans pricing whithin some MENA countries applying cost-plus
pricing model. The banks of UAE, Saudi Arabia and Egypt will be analysed to evaluate the loan pricing
situation. The historical data for the period of 2009-2013 will be observed.
2.1. Observation of reserched countries
At the end of the year 2013, the UAE and Egypt led the number of private equity investments for the MENA
region which accounting for 20%, followed by Lebanon -18% (GulfNews, Jul 15,2015)
In accordance with Moody, a market saturation in MENA region is driving some banks into risky position. Gulf
Arab banks will benefit mostly from spending of public sector in 2015 because a sharp drop in crude oil prices
will negatively affect the region. In addition, the future prospect for banking sector in the wider MENA region is
negative due to high credit risks (Zawya, Dec 10, 2014).
Armed conflicts in many MENA`s countries, including Egypt, can make worse a financial situation there.

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According to Constantinos Kypreos, Senior Credit Officer at Moody's, non-GCC banks have the low-rated
government securities that reduce levels of banks' credit profiles (Zawya, Dec 10, 2014).
On the contrary, improved activity of business in addition to public sector spending - especially in the UAE and
Saudi Arabia - will support solid GCC lending growth at an average level of around 10% in 2015, reported
Moody (December, 2014)
The Table 1 demonstrates the movement of bank`s non-performing loans in % to total provided gross loanes in
UAE, Saudi Arabia and Egypt within last five years.
It is seen that Egypt is the region with the highest level of loans nonpayment versus Saudi Arabia which level of
nonperform loans decrease till 1,3% in 2013; thus it is less risky region for bank loans` default.
Table 1: Bank`s nonperforming loans to total gross loans (% ),2008-2013
Country Name 2008 2009 2010 2011 2012 2013
Saudi Arabia 1.4 3.3 3 2.2 1.9 1.3
United Arab Emirates 2.3 4.3 5.6 7.2 8.4 8.4
Egypt, Arab Rep. 14.8 13.4 13.6 10.9 9.8 9.5
The table 2 compare the movement of bank capital to assets ratio. Bank`s capital consist of owners` funds,
retained earnings, general and special reserves, provisions, and valuation adjustments. Capital includes tier 1
capital which can absorb losses without a bank being required to cease trading (paid-up shares and common
stock), tier 2 and tier 3 – total regulatory capital, which can absorb losses in the event of a winding-up and so
provides a lesser protection to depositors. Total assets consist of nonfinancial and financial assets. This ratio is
applied to protect depositors and illustrate the stability of bank`s financial systems
It is clearly seen that UAE has the highest Capital to Risk Weighted Assets Ratio.So the UAE`s banks must be
more reliable as it has higher rate of capital which can absorb possible losses. On the contrary, Egypt can be
assist as the most risky for depositors.
0
2
4
6
8
10
12
14
16
2008 2009 2010 2011 2012 2013
Saudi Arabia United Arab Emirates Egypt, Arab Rep.
Sоurcе of information: The Wоrld Data Bаnk, Wоrld Dеvеlорmеnt Indicаtоrs аnd Glоbаl Dеvеlорmеnt Finаncе (2014)

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Table 2: Bank capital to assets ratio (%), 2008-2013
Country Name 2008 2009 2010 2011 2012 2013
Saudi Arabia 10.1 14 14.5 14.2 13.9 13.6
United Arab Emirates 11.8 16 17.7 17.2 16.8 15.2
Egypt, Arab Rep. 5.6 5.5 6.2 6.2 7.2 7
Average information of banking sector in UAE, Saudi Arabia and Egypt, presented in Appendix to this research:
Table 3: Average information (means) of main indicators financial activity, 2009-2013
Sourse of information: Annual financial statements, 2009-2013
Table 4: Average percentages of cost-plus model`s indicators, 2009-2013
0
2
4
6
8
10
12
14
16
18
20
2008 2009 2010 2011 2012 2013
Saudi Arabia United Arab Emirates Egypt, Arab Rep.
Funds
Customer`s
termdeposits
Interest
expenses paid
for deposits
Non interest
expenses
UAE 79,046,858 69.04% 74,104,988 (1,555,341) (1,961,919) (3,685,721) 1,595,612 2,921,682 6
Saudi Arabia 92,325,365 57.99% 127,251,155 (829,540) (3,199,078) (2,760,703) 3,115,266 4,177,817 7
Egypt 26,256,031 39.11% 54,981,146 (2,646,143) (1,181,426) (3,217,200) 767,060 1,686,153 10
Leverage
ratio (%)
Input expenses
NET
INCOME
SPREED NII
Average cost of fund:
Provision for
loans losses
Total LOANS
(Gross)
Loans`
share in
total assets
(%)
Pricing of the loan under
"Cost-plus" model Probability of Default TOTAL costs of fund i ROE
Capital Adequacy
Requirement
UAE 10.74 4.65 3.89 9.28 19.99
Saudi Arabia 10.66 3.17 4.48 13.09 16.56
Egypt 21.29 7.65 10.93 13.01 13.19

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The tables 13, 14 and 15 (Appendix) demonstrate the financial situation in banking sphere and loan assessment
in UAE< Saudi Arabia and Egypt. It is seen that the highest profit margin in average amount of 23% in loan
pricing has Egypt`s banks versus average 10% for banks of UAE and Saudi Arabia. At the same time Egypt
generate the highest level of total cost of funds to gross loans in average 11% versus around 4% for UAE and
Saudi. However it is difficult to judge carefully due to small share of Egypt`s loans in its total assets whitch
compound only 39% comparing with Saudi – 60% and UAE – 69%. The major quantity of Egypt`s banks
generate its profit and costs, especially operating one, from other type of financial activity, such as investment.
Thus, net income is earned by these other business and total costs fund them as well. Because of this, the
calculated highest profit margin in Egypt loan pricing is not adequate.
Egypts banks also have the highest leverage ratio of 10% comparing with 6% of UAE and Saudi leverage. The
leverage ratio – a way of measuring the financial strength of banks. Thus UAE and Saudi has less ratio of debts
to equity that can be assumed as less risky and less costly funding way. The less share of debts funding the loans
the less expenses of payments to depositors and, consequently, less loan costs. It also tell us that , in case of
UAE and Saudi Arabia, the shareholders' equity can largely fulfill a bank's obligations to creditors in the event
of a liquidation than Eqypt`s one. In general, we can assume that Egypt`s banks is not able to generate enough
cash to satisfy its debt obligations. Lenders usually prefer low leverage ratios because the lenders' interests are
better protected in the event of a business decline
The probability of default is also higher in Egypt region. It compound 7,7% versus 3% and 5% for Saudi and
UAE respectively. Thus the problem of non payment loans has higher possibility for Egypts banks which leads
to increasing of loan interest rate for covering of possible losses if default occurs.
UAE has the lowest return on equity of 9% versus 13% for Saudi and Egypt banks. The negative and small ROE
was generated espessialy in the period of financial crisys in 2008-2010 yrs. Fоr thе UАЕ thе cеntrаl еlеmеnt in
thе finаnciаl crisis wаs thе hоusing bubblе affected the mortgages prices. Оvеrаll, UАЕ fаcilitаtеd tо
аррrоximаtеly 60% оf thе рrореrty bооm in thе GCC cоuntriеs, with Dubаi оnly cоntributing tо 47% оf thе tоtаl
аmоng thе GCC nаtiоns. Thе UАЕ`s rеаl еstаtе sеctоr wаs thе mоst аttrаctivе fоr invеstоrs within thе реriоd оf
2003-2007; in thi period of crisis this sеctоr оf UАЕ`s еcоnоmy wаs sеvеrеly аffеctеd: рricеs аnd rеnts fеll by
20-50 % frоm thеir реаks аnd аn еstimаtеd $364 billiоn wоrth оf cоnstructiоn рrоjеcts hаvе bееn рut оn hоld оr
cаncеllеd in thе UАЕ (Аl-Mаsаh Cарitаl Limitеd, 2011).
To recapitulate, we can assume that Egypts region has more risky and unstable situation in lending activity for
banks; this followed by increasing interest rates for loans to cover all costs and possible losses. In case of UAE
and Saudi Arabia, the less costly loans (due to lower cost of fund and leverage) can be more attractive to
borrowers and this attractivness can bring more profitability to banks in these regions and reduce the probability
of losses.

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2.2. Data and Regression model
In this papеr thе changing оf loan pricing and variables which can affect the loan`s interest rate is prеsеntеd as a
rеgrеssiоn mоdеl:
Y = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X4 + ε
Our model is based on studies by D.Ruthenberg and Y.Landskroner (Journal of Banking & Finance, 2008), who
tasted with regression loan pricing in competitive Israel banking market considering capital adequacy rate as
well as it is required under Basel II. The internal rating-based approach of Basel II was focused on the
frequency of bank insolvencies arising from credit losses. The used the probability of default as one of
considered variables. They proposed that objective function of loan pricing is to maximize its expected profits in
short term with respect to its decision variables, amount of loans and deposits. They considered the risk
premium for loans as probability of default relying on historical data of Israel banks.
We will analyze the countries in priority with level of inflation rate which reflects thе аnnuаl реrcеntаgе change
in thе cоst tо thе аvеrаgе cоnsumеr оf аcquiring а bаskеt оf gооds аnd sеrvicеs (it takes value 1,2 and 3
according to inflation level: the country with higher average inflation rate takes higher figure), thus:
1 – UAE
2 – Saudi Arabiya
3 – Egypt
Table 5: Inflation, consumer prices (annual %), 2008-2013
Country Name 2008 2009 2010 2011 2012 2013 Average
United Arab Emirates 12.25 1.56 0.88 0.88 0.70 1.60 2.98
Saudi Arabia 9.87 5.07 5.34 5.82 2.89 3.51 5.42
Egypt, Arab Rep. 18.32 11.76 11.27 10.05 7.12 9.48 11.33
For our researche we constructed the following regression model (1):
Loan IRt = β0 + β1 PDt + β2 CFt + β3 ROEt + β4 CARt + ε (1)
whеrе:
Loan IR t – Loan interest rate at timе t
PDt – Probability of Default (%) at timе t
CFt – Cost of fund (in % of Gross loan) at timе t
ROEt – Return on Equity (%) at timе t
CARt – Capital required (%) at timе t

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In our regressions the variables is measures in % to total gross loans and we expect the following results:
Variable Calculation Expectation ofresults
The Probability of Default
(%)
Provision for loans losses to Total Gross
loans
positively related to loan interest rate
Cost of fund (in % to gross
loans)
{Interest expenses paid for deposit funds +
Operating expenses}/Total Gross loans
ROE Net Income/Equity positively related to loan interest rate
Capital Required
Capital Adequacy Ratio as per the BaselII
including Tier1 and Tier2*
*Under Basel I, the capital requirement applicable to all business loans is 8% (constant). Under the internal
ratings based approach of Basel II, the capital of banks must cover a loan`s losses with defaults probability β. So
for each bank there are different CAR which is highet than 8%, depending on the measure of default probability
We assume that the banks are risk neutral and its objective function is to maximize its expected profits with
respect to its decision variables, amount of loans and deposits (in the short term)
2.3. Regression analysis: interpretation and testing of variables
After applying the historical data of each banks we got the folloving results of model`s variable for each
country.
Table 6: Regression model`s Coefficients
Country Variables β
Std.
Error
T-
statistic Significance R2
Adj.
R2
1. UAE Constant 0.61 1.6595
0.6626 0.6493
Probability of Default (%) 0.91 0.0667 13.7024 significant
Cost of fund (%) 0.96 0.1536 6.2651 significant
ROE (%) 0.17 0.0326 5.1373 significant
Capital required (%) 0.03 0.0816 0.3539 not significant
2. Saudi
Arabia
Constant -2.22 0.6443
0.6861 0.6732
ROE (%) 0.24 0.0185 12.9593 significant
Capital required (%) 0.13 0.0330 3.8412 significant
3. Egypt Constant -0.92 0.8420
0.7382 0.7179
ROE (%) 0.24 0.0167 14.2996 significant
Capital required (%) 0.00 0.0428 -0.0883 not significant
Dependent Variable:Loan interest rate
Source: SPSS (2011), IBM SPSS Statistics for Windows, [Software], Version 19.0. Armonk, NY: IBM Corp.

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Thus, we applied calculated coefficient in our model for each country:
1) UAE:
Loan IR = 0,61 + 0,91PDt + 0,96 CFt + 0,17 ROEt + 0,03 CARt + ε (2)
The parameters of the model can be interpreted as follow:
 If Probability of default increace by 1% the interest rate charged on loans must be increased at 0,91%*
 In case of the Cost of fund rise at 1%, the loan`s interest rate will increase at 0,96%*
 If return on equity increase at 1% the interest rate of loan will increase at 0,17%*
 Increasing of capital adequasy rate at 1% will be followed by loan interest rate rising at 0,03%*
*if all other factors which can influens loan interest rate are constant.
Thе rеsults оf T-tеst оf UAE`s mоdеl (2) shоw thаt аll variables, еxcерt “CAR”, аrе stаtisticаlly significаnt аs
thеy аrе mоrе thаn criticаl роint in t-distributiоn fоr the mоdеl. Thus, we can аssumе with 95% cеrtаinty thаt
thеrе is thе strоng rеlаtiоnshiр bеtwееn Loan Interest rate changing аnd first three vаriаblеs оf thе mоdеl and
thеsе vаriаblеs must significаntly аffеct thе level of interest rate charged on loans in UAE`s banks. The Cost of
Fund has the higher affect on loan pricing in UAE. As CAR was identified as statisticaly insignificant variable
we can ignore it as it doesn’t affectthe amount of loan interest rate. Thе еstimаtеd R2
(tаb.6) for UAE`s model is
еquаl tо 0.8626. Thаt mеаns thаt аll indереndеnt vаriаblеs in thе mоdеl еxрlаin thе loan pricing in mеаsurе оf
86,26% thаt is thе gооd rеsult.
Table 7: Correlation & covariance between UAE model`s Coefficients

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2) Saudi Arabia:
Loan IR = -2,22 + 1,05 PD + 0,96 CF + 0,24 ROE + 0,13 CAR (3)
 If default`s probability increace by 1% the interest rate charged on loans must be increased at 1,05%*
 If Cost of fund rise at 1%, the loan`s interest rate will increase at 0,96%*
 In case of CAR increasing at 1% the loan pricing will rise at 0,13%*
Thе rеsults оf T-tеst оf Saudi Arabia`s mоdеl (2) shоw thаt аll variables, аrе stаtisticаlly significаnt аs thеy аrе
mоrе thаn criticаl роint in t-distributiоn fоr the mоdеl. Thus, we can аssumе with 95% cеrtаinty thаt all vаriаblеs
significаntly аffеct thе loan pricing in Saudi`s banks; the greatest attention is paid to the probability of default,
as its small movement followed by large changing of loan interes rate. Thе еstimаtеd R2
shows аll indереndеnt
vаriаblеs in thе mоdеl еxрlаin thе loan pricing by 97,61%.
Table 8: Correlation & covariance between Saudi Arabia model`s Coefficients

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3) Egypt:
Loan IR = -0,92 + 0,97 PD + 1,06 CF + 0,24 ROE - 0,004 CAR (4)
 If Probability of default increace by 1% the interest rate charged on loans must be increased at 0,97%*
 In case of the Cost of fund rise at 1%, the loan`s interest rate will increase at 1,06%*
 Increasing of capital adequasy rate at 1% will be followed by loan interest rate falling at 0,004%*
Thе rеsults оf T-tеst оf Egypt`s mоdеl (3) shоw thаt аll variables, еxcерt “CAR”, аrе stаtisticаlly significаnt.
CAR`s resusl showed unexpected negative relationship with loan rate, but as it was identifyed as statistically
insignificant we can exclude it from the model as it has no correlation with loan pricing. The Cost of Fund has
the higher affect on loan pricing in Egypt, thus we can assume that depositor`s interest rates are high that makes
loan`s funding expensive. The R2
assess the reliability of loan pricing`s explanation by аll vаriаblеs at 99,82%.
Table 9: Correlation & covariance between Saudi model`s Coefficients

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Table 10: Written off bad loans by type, within the period of2008-2013
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
2008 2009 2010 2011 2012 2013
UAE
Commercial Loans and
Overdrafts
Consumer Loans
Credit Cards
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
2008 2009 2010 2011 2012 2013
Saudi Arabia
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
2008 2009 2010 2011 2012 2013
Egypt

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2.4. The advantages and disadvantages of using credit scoring model:
The advantages:
 Allow analyse the direct and indirect costs of loan`s funds and other expenses related to bank’s
performance which can influence the total pricing, the cost of fund and other factors related to
bank’s performance as loss loan allowance and profit margin.
 Can be used for all personnel and corporate types of loans as calculation all possible expanses and
desirable profit in average.
 It is possible to include in loan price the desirable level of return which will generate the earning
capital.
 It is applicable in large scale of bank`s business.
 Considers opportunities of bank’s performance of cost reducing to make loans` price lower and
more attractive for customers.
The disadvantages:
 It takes a lot of time for correct calculation the total costs which will be included in loan providing
and to do this it require the qualify stuff.
 As it takes much time to calculate correct all costs and requared return, the approval of loans
applications takes a lot of time as well.
 Does not consider personal quality of lender such as gender, age, marital status which can affect the
probability of non-payment or payment delayrice of loan.

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3. Credit Scoring Model
A statistically method used by lenders to assess the borrower’s credit worthiness to know that if that borrower is
likely to repay his debts. It base on a person's credit history, higher score means the borrower has more credit
worth (Investopedia, 2007).
In addition, Lenders use credit scores to determine the credit limits (the amount of loan) at what interest rate. It
is widely used on consumer loans and credit cards. According to the study which made by Einav, Jenkins and
Levin in 2013, they found that applying the credit scoring led to a large increase in bank’s profitability . In
addition, they noticed that more down payment from the customer for car loan with higher income means
getting better credit score for applying on car loan (Einav, Jenkins and Levin, 2013).
We can claim that the factors which used in credit scoring model related to the borrower character and his
financial capacity as (salary or income amount, number of working years, residence place, the liabilities of
borrower, ……..).
3.1. The Methodology of this model to input interest rate:
Here we use it to determine the required interest rate for loans which given to the customers; we have data for
100 customers who take consumer loans from bank established in UAE and these variables which are used in its
credit scoring model:
- Loan amount (LA): it determined after assessing the customer’s ability to pay back the loan. So it consider
his income amount, the total liabilities which customer has recently (The amount of loan which will give to the
customer will reduce or cancel if his monthly income not sufficient to pay back the debt service of his
obligations including the loan which will offer to him. According to that the percentage of debt service to salary
or income should not excess (40%- 50%)).
- Employment sector (ES): it determined as government sector or private sector. The government sector get
higher score than the private sector (Working in government sector has more stability for employees and more
benefits for them as pension and other compensations that employees can get from working for government).
- No. working years (NY): less than 1 year ………. More than 15 years.
- Status (M or S): Married or single (Married people have higher score because considered as more
responsible).
- Nationality (N): India, Egypt, Jordan… for expats or just UAE for citizen.
- Income/ Salary (I): higher monthly salary or income means better score
- Occupation (O): the job of borrower (many of applicants here were employees)
- Residence in UAE (Res): we have (Abu Dhabi, Dubai, Al- Sharjah, Al-Ain)

17
- Gender (M/F): male or Female.
- Age: higher age expected to get higher score but other variable should be considered with this variable
(number of working year).
Note: usually the period of consumer loans is 48 months and it was the same for all customers.
After fitting these data for each customer, we use credit scoring program to get the credit score value for each of
them. Determining the interest rate in credit scoring model considers these following concepts:
-The interest rate are linked to the salary range and credit score value
-The interest rate is floating interest rate equal will be equal to be 1 year EIBOR rate + spread rate
- The setting interest rate differ between Citizens and Expats (other nationalities).
Table 11: Categories for Citizens (according to what set by the studied bank)
Salary for
Citizens /Score
range
10K…..15 K 15 K……20 K 20 K……40K 40 K……50K More than 50K
450…...> 460 No loan No loan No loan No loan 7.00%
460…...> 470 No loan No loan No loan 7.50% 7.00%
470…...> 480 No loan No loan 7.75% 7.50% 7.00%
480…...> 490 No loan No loan 7.75% 7.50% 7.00%
490…...> 500 10.00% 8.50% 7.75% 6.75% 6.50%
500…...> 510 8.50% 8.50% 7.50% 6.75% 6.50%
510…...> 520 8.00% 8.00% 7.50% 6.75% 6.50%
520…...> 530 8.00% 8.00% 7.50% 6.75% 6.50%
530…...> 540 7.50% 7.50% 7.50% 6.75% 6.50%
540…...> 550 7.50% 7.50% 7.00% 6.75% 6.50%
>= 550 6.50% 6.50% 6.50% 6.50% 6.50%
Table 12: Categories for Expats (according to what set by the studied bank)
Salary for
Expats/Score range
5K…..10K 10K…..20K 20K…..40K More than 40K
460…...> 480 16.00% 12.00% 12.00% 12.00%
480…...> 490 12.00% 10.50% 9.70% 9.70%
490….. < 500 12.00% 8.96% 8.96% 8.96%
500…...< 510 12.00% 8.54% 8.54% 8.54%
510…...> 520 12.00% 8.05% 8.05% 8.05%
=> 520 11.00% 6.50% 6.00% 5.50%

18
3.2. The results from applying this methodology:
The credit scoring for each customer and the required interest rate for each of them. In addition, we illustrate the
customers who still made their payments regularity and the customers who face problem on paying back the
debt service (restructuring their loans).
According to the result from analyzing our data (tab.17, Appendix), we found that this model work effectively to
determine the required interest rate for each customers. The non-performing loans as a percentage of the total
activated loans was just around 3%.
Most of these non –performing loans related to events happened with these customers:
 The customers got more consumer loans and credit cards from another banks after getting their loans
from our studied bank (under specific amount of loan the bank is not required to declare).
 Some of them were laid off from the companies where they worked (losing their job).
 The customers which have low salaries are more likely to default when the cost of living increases
dramatically.
Note: the risk of non-declaring the customers obligations will be avoided in the UAE banking system for the
next years ( each bank will required to declare its customers facilities in inquiry central bank system and the
customer’s information should be updated to include all its account changes as the restruction, canceling,
increasing the facilities amounts,…)
3.3. Testing credit scoring results by running regression model:
According to our previous study, the variables which affects in determining the credit scoring for the customers
in our studied bank are: Loan amount (LA), Employment sector (ES), Nationality (U/E), Residence (Res),
Income (I), N. working years (NY), Age, Gender and status (M/S).
Note: In nationality we distinguish between citizens and expats and in employment sector we distinguish
between government sector and private sector.
The variables The coefficients t test The significance
R2
Constant 14.59 31.65 Sig
U/E 3.95 -3.17 Sig
0.59LA 0.00 1.42 Not sig
ES 3.36 3.88 Sig
Adj R2NY 0.45 4.70 Sig
I 4.03 2.53 Sig
M/S 0.00 0.57 Not sig
0.55Res 4.02 -0.28 Not sig
Gender 4.21 1.85 Not sig Sample size
Age 0.36 1.52 Not sig 100
Note: The t(.025, 90) according to t statistic table = 1.962

19
According to our result from regression model we found that 59% of the variation in credit Scoring for customer
is explained by the change in the mentioned variables above.
Furthermore, we found that the impact of (I, U/E, ES, NY) on the credit scoring figure was significant while
the impact of (LA, M/S, Res, Gender, Age) wasn’t significant.
the loan amount wasn’t important in determining interest rate by its self it’s related to the financial capacity of
the borrower ( the income level of borrower and the ratio of debt service to income) according to that when the
borrower has better financial situation he could get more appropriate amount of loan in affordable cost.
Furthermore, when we have higher income with more working period we can claim that the borrower will get
better credit scoring, also working for government means more stability and safety for employees and they
benefit from the pension and other compensations.
3.4. The advantages and disadvantages of using credit scoring model:
The advantages:
- The applications of loans will be approved very quickly (the approval for credit card and consumer loan
take just two days).
- The high accuracy of this model in assessing the credit worthiness of borrower (when the correct
variables are included in this model the possibility of fail is very low).
- The customers who have high score on this model have more opportunities to get more facilities in
cheaper cost.
The disadvantages:
- The cost of establishing this program and it need high skilled employees to work on it.
- It’s used only for consumer loans and credit card while mortgage and commercial loans need financial
skills and analyst judgment.
- It doesn’t consider the cost of fund and other factors related to bank’s performance as loss loan
allowance and profit margin.

20
Conclusion
As banks diversifying their risk into different products including personal, commercial mortgage and auto loan,
pricing in loans is extremely important to penetrate the market correctly and achieve the target profit required
from the top management in the bank.
At the end of the project and after analyzing data shown above applied on MENA area specifically on 3
countries which are Egypt, UAE and Saudi Arabia we found that in terms of Risk Weighted Assets Ratio UAE
market is more reliable as they high rate of capital which can recover losses easily, on other side Egypt is more
risky for deposits moreover Egypt has the highest cost of fund, highest leverage and highest expectancy of
default comparing to the other 2 countries
This is the reason of high interest rates of Egyptian banks which can reach to more than 20% in the personal
loan product comparing with UAE and KSA (for these countries the average loan interest rate is equal to 10%).
In addition to that we ran the regression model for all analyzed countries to check the measure of influencing on
loan`s interest rate by components of “Cost-plus” model (cost of fund, probability of default, return on equity
and, capital required) using the historical data of more than 15 banks of the selected 3 countries. The results of
constructed regression model have confirmed our calculations of loan pricing using "Cost-plus" model: it is
shown that the most significant factor for Egyptian banks is the cost of fund - as the slightest change in total
expenditures, referred to loan`s funding, entails a strong loan price change. Thus, Egyptian banks have to pay
attention mostly to this factor of its business and try look for opportunities to reduce these costs to make the
loans more applicable and attractive. For UAE and Saudi Arabia the probability of default plays more
significant role in loan pricing calculation. However the “Cost-plus” model does not consider very deeply
possible reasons of default. For this case the “Credit-scoring” model can be applied.
Moreover, with the increase number of loans defaulters in banks, some of those banks have developed a credit
scoring system in order to identify whither the borrower of the loan is eligible or not and if the borrower is
eligible what interest rate will be applied for him and how much is the amount.
This scoring system put important variables to identify the risk of the borrower and depend on that risk pricing
will be applied, as banks put higher interest rate on the high risk customer
Finally banks has to solve the difficult equation between loan pricing, risk, cost of fund and percentage of
default in order to reach to the maximum profit possible and to expand their market to wide range of customers
with attractive loan pricing and profitable in the same way.

21
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[Accessed 12 Dec. 2014]

23
APPENDIX
Table 13: Data of UAE`s banks of loan pricing and other financial indicators under the annual financial statements, 2009-2013
Funds
Customer`s
term
deposits
Interest
expenses
paidfor
deposit
funds
Non
interest
expenses
(Average
operating
expenses)
TOTAL
costs of fund
TOTAL
costs of fund
in % of
Gross Loans
A
(Gross
Loans/A)
1 2 (CF) = 1 + 2
(CF/Gross
Loans)
(PL) (NI)
(CF+PL+NI)/
Gross
Loans
PL/Gross
Loans
Interest Inc
- Nonint.Inc
CAR
Gross
Loans*CAR
(Opt.inc) (E) (NI/E) (L) (L/E)
Dummy-1 X2 Y X1 X4 X3
Emirates NBD
AED’000 2013 223,328,180 342,061,275 65.29% 9,643,395 195,271,203 (2,587,952) (8,907,297) (11,495,249) 5.15% (17,338,010) 3,256,366 14.37% 7.76% 7,055,443 19.64% 43,861,655 11,856,179 41,710,787 7.81% 300,345,963 7.20
2012 201,375,072 308,296,351 65.32% 9,236,309 176,318,158 (2,977,163) (7,758,160) (10,735,323) 5.33% (14,509,232) 2,554,019 13.80% 7.21% 6,259,146 20.64% 41,563,815 10,217,297 36,452,307 7.01% 271,797,775 7.46
2011 188,299,266 284,613,386 66.16% 10,283,230 154,013,407 (3,568,218) (8,485,698) (12,053,916) 6.40% (11,484,232) 2,483,483 13.82% 6.10% 6,715,012 20.53% 38,657,839 9,930,102 34,934,774 7.11% 249,632,332 7.15
2010 184,485,347 286,078,324 64.49% 11,288,438 159,872,602 (4,922,924) (6,243,256) (11,166,180) 6.05% (7,481,734) 2,339,183 11.38% 4.06% 6,365,514 19.76% 36,454,305 9,721,487 33,655,770 6.95% 252,328,734 7.50
2009 200,112,010 281,576,482 71.07% 11,972,238 157,976,541 (5,205,173) (7,249,776) (12,454,949) 6.22% (5,409,321) 3,342,532 10.60% 2.70% 6,767,065 20.80% 41,623,298 10,847,777 31,876,499 10.49% 249,605,838 7.83
SUM 997,599,875 1,502,625,818 52,423,610 843,451,911 (19,261,430) (38,644,187) (57,905,617) 29.16% (56,222,529) 13,975,583 63.97% 27.83% 33,162,180 101.37% 202,160,911 52,572,842 178,630,137 39.36% 1,323,710,642 37.13
AVERAGE 199,519,975 300,525,164 66.46% 10,484,722 168,690,382 (3,852,286) (7,728,837) (11,581,123) 5.83% (11,244,506) 2,795,117 12.79% 5.57% 6,632,436 20.27% 40,432,182 10,514,568 35,726,027 7.87% 264,742,128 7.43
Union National Bank
AED’000 2013 62,498,929 87,546,063 71.39% 3,338,321 65,087,812 (1,067,440) (854,296) (1,921,736) 3.07% (2,450,394) 1,747,589 9.79% 3.92% 2,270,881 19.90% 12,437,287 3,211,691 15,191,773 11.50% 72,208,418 4.75
2012 59,519,964 87,137,951 68.31% 3,572,535 63,438,125 (1,383,967) (786,790) (2,170,757) 3.65% (2,176,313) 1,602,387 10.00% 3.66% 2,188,568 23.20% 13,808,632 3,090,963 13,976,733 11.46% 73,016,960 5.22
2011 59,213,827 82,468,800 71.80% 3,420,665 60,314,700 (1,352,341) (728,708) (2,081,049) 3.51% (1,632,516) 1,500,365 8.81% 2.76% 2,068,324 21.90% 12,967,828 2,837,805 12,925,120 11.61% 69,400,959 5.37
2010 57,756,354 81,779,759 70.62% 3,342,749 57,941,257 (1,699,168) (714,975) (2,414,143) 4.18% (1,183,298) 1,350,046 8.57% 2.05% 1,643,581 20.10% 11,609,027 2,554,826 11,781,218 11.46% 69,848,860 5.93
2009 51,580,147 75,725,621 68.11% 3,367,069 51,279,409 (1,998,990) (648,640) (2,647,630) 5.13% (811,552) 1,157,564 8.95% 1.57% 1,368,079 20.70% 10,677,090 2,118,268 10,667,622 10.85% 65,057,999 6.10
SUM 290,569,221 414,658,194 17,041,339 298,061,303 (7,501,906) (3,733,409) (11,235,315) 19.55% (8,254,073) 7,357,951 46.11% 13.96% 9,539,433 105.80% 61,499,864 13,813,553 64,542,466 56.89% 349,533,196 27.37
AVERAGE 58,113,844 82,931,639 70.05% 3,408,268 59,612,261 (1,500,381) (746,682) (2,247,063) 3.91% (1,650,815) 1,471,590 9.22% 2.79% 1,907,887 21.16% 12,299,973 2,762,711 12,908,493 11.38% 69,906,639 5.47
ADCB
AED’000 2013 138,538,617 183,142,536 75.65% 6,519,957 115,427,708 (1,551,605) (2,358,186) (3,909,791) 2.82% (6,889,947) 3,619,644 10.41% 4.97% 4,968,352 21.21% 29,384,041 7,319,619 24,176,598 14.97% 158,321,226 6.55
2012 129,659,015 180,795,723 71.72% 7,469,680 109,216,925 (2,356,370) (2,069,264) (4,425,634) 3.41% (6,463,720) 2,810,335 10.57% 4.99% 5,113,310 23.05% 29,886,403 6,595,148 24,269,789 11.58% 156,088,134 6.43
2011 130,466,613 183,725,630 71.01% 7,365,090 109,887,477 (2,817,299) (2,063,225) (4,880,524) 3.74% (5,711,876) 3,045,111 10.45% 4.38% 4,547,791 22.51% 29,368,035 6,069,412 22,072,006 13.80% 161,648,107 7.32
2010 129,068,307 178,271,194 72.40% 7,158,894 106,134,185 (3,507,961) (1,648,982) (5,156,943) 4.00% (6,296,437) 390,615 9.18% 4.88% 3,650,933 16.65% 21,489,873 4,999,606 19,564,787 2.00% 158,697,846 8.11
2009 120,842,549 160,208,778 75.43% 6,897,823 86,299,957 (3,516,903) (5,292,374) (8,809,277) 7.29% (4,232,257) (512,799) 10.37% 3.50% 3,380,920 17.10% 20,664,076 4,782,806 19,021,032 -2.70% 141,118,659 7.42
SUM 648,575,101 886,143,861 35,411,444 526,966,252 (13,750,138) (13,432,031) (27,182,169) 21.26% (29,594,237) 9,352,906 50.97% 22.72% 21,661,306 100.52% 130,792,427 29,766,591 109,104,212 39.65% 775,873,972 35.83
AVERAGE 129,715,020 177,228,772 73.24% 7,082,289 105,393,250 (2,750,028) (2,686,406) (5,436,434) 4.25% (5,918,847) 1,870,581 10.19% 4.54% 4,332,261 20.10% 26,158,485 5,953,318 21,820,842 7.93% 155,174,794 7.17
UnitedArabBank
AED’000 2013 15,573,416 21,549,756 72.27% 962,531 15,034,837 (171,972) (318,552) (490,524) 3.15% (136,859) 263,802 5.72% 0.88% 790,559 15.90% 2,476,173 1,039,495 2,481,531 10.63% 19,068,225 7.68
2012 11,059,680 15,615,867 70.82% 720,691 10,094,283 (153,642) (233,605) (387,247) 3.50% (136,859) 164,056 6.22% 1.24% 567,049 19.20% 2,123,459 765,028 2,247,875 7.30% 13,367,992 5.95
2011 7,947,549 10,832,095 73.37% 509,968 7,343,110 (76,108) (180,806) (256,914) 3.23% (95,781) 330,154 8.59% 1.21% 433,860 20.30% 1,613,352 581,494 2,031,025 16.26% 8,801,070 4.33
2010 5,644,774 7,742,069 72.91% 412,070 4,258,551 (60,797) (162,922) (223,719) 3.96% (136,859) 308,001 11.84% 2.42% 351,273 20.40% 1,151,534 490,221 1,847,959 16.67% 5,894,110 3.19
2009 4,919,992 6,994,927 70.34% 421,762 82,680,240 (97,300) (155,314) (252,614) 5.13% (133,572) 280,778 13.56% 2.71% 324,462 18.90% 929,878 470,588 14,368,767 1.95% 95,819,053 6.67
SUM 45,145,411 62,734,714 3,027,022 119,411,021 (559,819) (1,051,199) (1,611,018) 18.98% (639,930) 1,346,791 45.94% 8.46% 2,467,203 94.70% 8,294,397 3,346,826 22,977,157 52.81% 142,950,450 27.82
AVERAGE 9,029,082 12,546,943 71.94% 605,404 23,882,204 (111,964) (210,240) (322,204) 3.80% (127,986) 269,358 9.19% 1.69% 493,441 18.94% 1,658,879 669,365 4,595,431 10.56% 28,590,090 5.56
Commercial Bank of Dubai
AED’000 2013 33,117,608 44,476,191 74.46% 1,766,533 30,942,680 (317,864) (627,606) (945,470) 2.85% (2,830,223) 1,010,235 14.45% 8.55% 1,448,669 18.96% 6,279,098 2,032,894 7,216,389 14.00% 37,259,802 5.16
2012 29,575,447 39,297,769 75.26% 1,675,351 28,051,989 (343,076) (572,199) (915,275) 3.09% (2,373,837) 858,852 14.03% 8.03% 1,332,275 23.23% 6,870,376 1,857,947 6,797,007 12.64% 32,487,185 4.78
2011 27,974,091 38,241,320 73.15% 1,768,712 28,423,430 (427,335) (564,771) (992,106) 3.55% (1,779,334) 822,100 12.85% 6.36% 1,341,377 24.49% 6,850,855 1,857,219 6,321,613 13.00% 31,919,707 5.05
2010 28,420,490 38,508,704 73.80% 1,968,464 29,209,662 (582,986) (543,003) (1,125,989) 3.96% (1,255,281) 820,589 11.27% 4.42% 1,385,478 23.16% 6,582,185 1,890,043 5,878,941 13.96% 32,629,763 5.55
2009 29,114,333 36,783,052 79.15% 1,338,642 27,928,454 (720,831) (535,297) (1,256,128) 4.31% (737,510) 803,345 9.61% 2.53% 617,811 20.86% 6,073,250 5,960,109 5,349,960 15.02% 31,433,092 5.88
SUM 148,201,969 197,307,036 8,517,702 144,556,215 (2,392,092) (2,842,876) (5,234,968) 17.77% (8,976,185) 4,315,121 62.20% 29.88% 6,125,610 110.70% 32,655,765 13,598,212 31,563,910 68.61% 165,729,549 26.42
AVERAGE 29,640,394 39,461,407 75.17% 1,703,540 28,911,243 (478,418) (568,575) (1,046,994) 3.55% (1,795,237) 863,024 12.44% 5.98% 1,225,122 22.14% 6,531,153 2,719,642 6,312,782 13.72% 33,145,910 5.28
Bank Name
Capital
Required
Total
Operating
income
Equity
(controlled)
ROE
NET
INCOME
Interest
income
received
Loans`
share in
total assets
(% )
Total Assets
Total
LOANS
(Gross)
Total
liabilities
Leverage
ratio
(%)
Pricing of
the loan
under
"Cost-plus"
model
Probability
of Default
SPREED
NII
Capital
Adequacy
Requirement
(Tier 1 and
Tier 2)
Input expenses
Provision
for loans
losses
Yrs

24
Tab.13: UAE Continued
Sourse of information: UAE banks` annual financial statements,2009-2013
Funds
Customer`s
term
deposits
Interest
expenses
paidfor
deposit
funds
Non
interest
expenses
(Average
operating
expenses)
TOTAL
costs of fund
TOTAL
costs of fund
in % of
Gross Loans
A
(Gross
Loans/A)
1 2 (CF) = 1 + 2
(CF/Gross
Loans)
(PL) (NI)
(CF+PL+NI)/
Gross
Loans
PL/Gross
Loans
Interest Inc
- Nonint.Inc
CAR
Gross
Loans*CAR
Dummy-1 X2 Y X1 X4 X3
FGB
AED’000 2013 130,846,610 195,032,370 67.09% 7,868,599 137,953,532 (1,875,037) (1,766,052) (3,641,089) 2.78% (3,905,091) 4,801,970 9.44% 2.98% 5,993,562 18.00% 23,552,390 8,420,561 31,230,948 15.38% 163,261,899 5.23
2012 118,396,230 175,033,609 67.64% 7,644,488 119,304,634 (2,124,104) (1,425,895) (3,549,999) 3.00% (3,751,751) 4,170,862 9.69% 3.17% 5,520,384 21.00% 24,863,208 7,269,771 29,348,210 14.21% 145,170,519 4.95
2011 108,341,454 157,480,337 68.80% 7,073,337 103,473,733 (1,994,446) (1,224,036) (3,218,482) 2.97% (3,621,655) 3,705,755 9.73% 3.34% 5,078,891 21.00% 22,751,705 6,482,882 26,651,428 13.90% 130,713,221 4.90
2010 98,922,799 140,758,004 70.28% 6,578,936 98,741,936 (2,321,737) (1,121,548) (3,443,285) 3.48% (3,294,783) 3,544,349 10.39% 3.33% 4,257,199 23.00% 22,752,244 6,304,984 24,126,372 14.69% 116,126,856 4.81
2009 92,915,667 125,472,543 74.05% 6,489,973 86,421,906 (2,656,241) (1,080,583) (3,736,824) 4.02% (2,529,782) 3,312,965 10.31% 2.72% 3,833,732 22.00% 20,441,447 6,164,014 22,902,768 14.47% 102,569,775 4.48
SUM 549,422,760 793,776,863 35,655,333 545,895,741 (10,971,565) (6,618,114) (17,589,679) 16.25% (17,103,062) 19,535,901 49.57% 15.55% 24,683,768 105.00% 114,360,994 34,642,212 134,259,726 72.65% 657,842,270 24.37
AVERAGE 109,884,552 158,755,373 69.57% 7,131,067 109,179,148 (2,194,313) (1,323,623) (3,517,936) 3.25% (3,420,612) 3,907,180 9.91% 3.11% 4,936,754 21.00% 22,872,199 6,928,442 26,851,945 14.53% 131,568,454 4.87
Emirates Islamic Bank (PJSC)
AED’000 2013 21,683,210 39,768,966 54.52% 1,185,077 28,892,862 (662,933) (662,933) 3.06% (3,028,881) 139,488 17.67% 13.97% 1,185,077 15.96% 3,460,640 719,356 4,157,505 3.36% 35,611,461 8.57
2012 19,825,471 37,263,760 53.20% 760,873 25,673,184 (429,001) (429,001) 2.16% (2,468,360) 81,112 15.02% 12.45% 760,873 12.24% 2,426,638 582,689 2,578,748 3.15% 34,641,736 13.43
2011 14,919,162 21,483,795 69.44% 699,951 17,125,152 (455,179) (455,179) 3.05% (1,351,554) (448,552) 9.10% 9.06% 699,951 18.53% 2,764,521 490,909 2,434,702 -18.42% 19,005,709 7.81
2010 16,024,384 32,746,515 48.93% 919,883 24,222,865 (395,812) (395,812) 2.47% (822,333) 59,340 7.97% 5.13% 919,883 18.00% 2,884,389 731,654 2,836,735 2.09% 29,819,339 10.51
2009 14,673,473 25,289,639 58.02% 1,053,600 19,418,087 (402,428) (402,428) 2.74% (539,074) 130,794 7.31% 3.67% 1,053,600 17.14% 2,515,033 454,534 2,780,498 4.70% 22,416,778 8.06
SUM 87,125,700 156,552,675 4,619,384 115,332,150 - (2,345,353) (2,345,353) 13.48% (8,210,202) (37,818) 57.08% 44.28% 4,619,384 81.87% 14,051,221 2,979,142 14,788,188 -5.13% 141,495,023 48.38
AVERAGE 17,425,140 31,310,535 56.83% 923,877 23,066,430 (469,071) (469,071) 2.70% (1,642,040) (7,564) 11.42% 8.86% 923,877 16.37% 2,810,244 595,828 2,957,638 -1.03% 28,299,005 9.68
TOTAL AVERAGE 79,046,858 114,679,976 69.04% 4,477,024 74,104,988 (1,555,341) (1,961,919) (3,517,261) 3.90% (3,685,721) 1,595,612 10.74% 4.65% 2,921,682 20.00% 16,109,017 4,306,268 15,881,880 9.28% 101,632,431 6.50
Bank Name
Capital
Required
Total
Operating
income
Equity
(controlled)
ROE
NET
INCOME
Interest
income
received
Loans`
share in
total assets
(%)
Total Assets
Total
LOANS
(Gross)
Total
liabilities
Leverage
ratio
(%)
Pricing of
the loan
under
"Cost-plus"
model
Probability
of Default
SPREED
NII
Capital
Adequacy
Requirement
(Tier 1 and
Tier 2)
Input expenses
Provision
for loans
losses
Yrs

25
Table 14: Data of Saudi Arabia`s banks of loan pricing and other financial indicators under the annual financial statements, 2009-2013
Funds
Customer`s
term deposits
Interest
expenses
paidfor
deposit
funds
Non interest
expenses
(Average
operating
expenses)
TOTAL
costs of fund
TOTAL
costs of
fundin %
of Gross
Loans
A
(Gross
Loans/A)
1 2 (CF) = 1 + 2
(CF/Gross
Loans)
(PL) (NI)
(CF+PL+NI)/
Gross
Loans
PL/Gross
Loans
Interest Inc
- Nonint.Inc
CAR
Gross
Loans*CAR
X2 Y X1 X4 X3
National commercial Bank of Saudi
SAR’000 2013 192,529,219 377,280,334 51.03% 11,725,818 300,601,675 (1,713,488) (6,637,341) (8,350,829) 4.34% (4,842,182) 7,988,976 11.00% 2.52% 10,012,330 17.10% 32,922,496 14,862,943 40,933,907 19.52% 334,744,154 8.18
2012 170,516,318 345,259,703 49.39% 11,096,187 273,530,090 (2,136,605) (6,661,423) (8,798,028) 5.16% (7,055,129) 6,613,326 13.18% 4.14% 8,959,582 17.50% 29,840,356 13,508,911 37,703,631 17.54% 305,855,558 8.11
2011 141,306,127 301,198,161 46.91% 10,185,103 239,457,558 (1,603,677) (5,805,036) (7,408,713) 5.24% (6,016,631) 6,106,119 13.82% 4.26% 8,581,426 18.20% 25,717,715 12,138,394 34,165,218 17.87% 265,612,907 7.77
2010 131,634,097 282,371,992 46.62% 9,711,254 229,160,181 (1,561,452) (6,633,499) (8,194,951) 6.23% (6,037,006) 4,803,404 14.46% 4.59% 8,149,802 18.00% 23,694,137 11,667,256 31,272,258 15.36% 249,515,299 7.98
2009 116,780,976 257,452,175 45.36% 10,372,154 202,582,508 (2,326,509) (11,478,644) (13,805,153) 11.82% (4,623,336) 4,121,359 19.31% 3.96% 8,045,645 19.30% 22,538,728 4,250,328 29,271,087 14.08% 226,592,016 7.74
SUM 752,766,737 1,563,562,365 53,090,516 1,245,332,012 (9,341,731) (37,215,943) (46,557,674) (28,574,284) 29,633,184 71.77% 43,748,785 134,713,433 56,427,832 173,346,101 1,382,319,934
AVERAGE 150,553,347 312,712,473 47.86% 10,618,103 249,066,402 (1,868,346) (7,443,189) (9,311,535) 6.56% (5,714,857) 5,926,637 14.35% 3.89% 8,749,757 18.02% 26,942,687 11,285,566 34,669,220 16.87% 276,463,987 7.96
The Saudi British Bank
SAR’000 2013 108,373,599 177,302,200 61.12% 4,386,138 138,961,470 (666,842) (2,164,744) (2,831,586) 2.61% (2,258,669) 3,773,810 8.18% 2.08% 3,719,296 17.32% 18,770,307 5,815,384 22,832,799 16.53% 154,469,401 6.77
2012 98,511,690 156,652,337 62.89% 3,999,985 120,433,716 (735,885) (2,037,397) (2,773,282) 2.82% (2,413,384) 3,240,316 8.55% 2.45% 3,264,100 15.69% 15,456,484 5,166,483 20,065,507 16.15% 136,586,830 6.81
2011 86,892,010 138,657,505 62.67% 3,515,880 105,576,542 (493,905) (2,074,321) (2,568,226) 2.96% (2,080,723) 2,888,435 8.67% 2.39% 3,021,975 14.70% 12,773,125 4,898,591 17,166,201 16.83% 121,491,304 7.08
2010 76,862,958 125,372,866 61.31% 3,724,908 94,672,855 (481,865) (2,997,343) (3,479,208) 4.53% (2,614,472) 1,883,152 10.38% 3.40% 3,243,043 14.17% 10,891,481 4,839,421 15,171,947 12.41% 110,200,919 7.26
2009 78,156,943 126,837,962 61.62% 4,573,599 89,186,861 (1,136,857) (3,174,050) (4,310,907) 5.52% (1,775,344) 2,032,277 10.39% 2.27% 3,436,742 12.76% 9,972,826 5,160,279 13,045,289 15.58% 113,792,673 8.72
SUM 448,797,200 724,822,870 20,200,510 548,831,444 (3,515,354) (12,447,855) (15,963,209) (11,142,592) 13,817,990 46.17% 16,685,156 67,864,224 25,880,158 88,281,743 636,541,127
AVERAGE 89,759,440 144,964,574 61.92% 4,040,102 109,766,289 (703,071) (2,489,571) (3,192,642) 3.69% (2,228,518) 2,763,598 9.23% 2.52% 3,337,031 14.93% 13,572,845 5,176,032 17,656,349 15.50% 127,308,225 7.33
Samba
SAR’000 2012 107,904,841 199,224,139 54.16% 4,768,156 148,736,368 (494,776) (2,361,995) (2,856,771) 2.65% (3,118,796) 4,332,096 9.55% 2.89% 4,273,380 20.00% 21,580,968 6,694,091 31,636,867 13.69% 167,485,352 5.29
2011 92,550,190 192,773,890 48.01% 4,774,598 137,256,864 (466,130) (2,257,518) (2,723,648) 2.94% (3,438,761) 4,304,849 11.31% 3.72% 4,308,468 19.20% 17,769,636 6,562,367 28,129,903 15.30% 164,516,528 5.85
2010 83,957,826 187,415,840 44.80% 5,194,654 133,462,964 (658,193) (2,468,394) (3,126,587) 3.72% (3,707,001) 4,432,106 13.42% 4.42% 4,536,461 18.90% 15,868,029 6,900,500 25,429,682 17.43% 161,812,954 6.36
2009 87,522,353 185,518,269 47.18% 6,351,394 147,128,762 (1,281,881) (2,556,296) (3,838,177) 4.39% (3,375,830) 4,553,344 13.44% 3.86% 5,069,513 17.10% 14,966,322 7,109,640 22,310,078 20.41% 163,016,622 7.31
SUM 371,935,210 764,932,138 21,088,802 566,584,958 (2,900,980) (9,644,203) (12,545,183) (13,640,388) 17,622,395 47.73% 18,187,822 70,184,956 27,266,598 107,506,530 656,831,456
AVERAGE 92,983,803 191,233,035 48.54% 5,272,201 141,646,240 (725,245) (2,411,051) (3,136,296) 3.42% (3,410,097) 4,405,599 11.93% 3.72% 4,546,956 18.80% 17,546,239 6,816,650 26,876,633 16.71% 164,207,864 6.20
ArabNational Bank.
SAR’000 2013 90,510,146 137,935,424 65.62% 3,944,901 106,372,732 (570,002) (2,620,889) (3,190,891) 3.53% (2,054,040) 2,525,143 8.58% 2.27% 3,374,899 16.00% 14,481,623 5,109,545 19,080,454 13.23% 118,747,010 6.22
2012 89,027,336 136,639,276 65.16% 3,748,063 107,560,443 (487,634) (2,413,925) (2,901,559) 3.26% (2,698,728) 2,371,025 8.95% 3.03% 3,260,429 14.77% 13,149,338 4,756,821 17,804,275 13.32% 118,729,689 6.67
2011 75,448,667 117,574,305 64.17% 3,463,490 87,858,815 (282,523) (2,392,955) (2,675,478) 3.55% (2,604,897) 2,170,675 9.88% 3.45% 3,180,967 16.52% 12,464,120 4,541,462 16,624,060 13.06% 100,844,780 6.07
2010 68,397,440 116,034,765 58.95% 3,454,343 84,198,613 (296,790) (2,608,879) (2,905,669) 4.25% (2,194,489) 1,907,502 10.25% 3.21% 3,157,553 16.95% 11,593,366 4,503,781 15,290,771 12.47% 100,638,081 6.58
2009 68,268,453 110,297,320 61.89% 4,234,487 82,680,240 (778,204) (2,128,048) (2,906,252) 4.26% (1,457,420) 2,367,012 9.86% 2.13% 3,456,283 6.26% 4,273,605 4,493,459 14,368,767 16.47% 95,819,053 6.67
SUM 391,652,042 618,481,090 18,845,284 468,670,843 (2,415,153) (12,164,696) (14,579,849) (11,009,574) 11,341,357 47.52% 16,430,131 55,962,052 23,405,068 83,168,327 534,778,613
AVERAGE 78,330,408 123,696,218 63.16% 3,769,057 93,734,169 (483,031) (2,432,939) (2,915,970) 3.77% (2,201,915) 2,268,271 9.50% 2.82% 3,286,026 14.10% 11,192,410 4,681,014 16,633,665 13.71% 106,955,723 6.44
RiyadBank.
SAR’000 2013 133,122,252 205,246,479 64.86% 5,517,436 153,199,880 (820,436) (3,183,701) (4,004,137) 3.01% (1,931,695) 3,947,105 7.42% 1.45% 4,697,000 17.10% 22,763,905 7,074,022 33,870,324 11.65% 171,376,155 5.06
2012 120,012,346 190,180,838 63.10% 5,163,301 146,214,567 (781,830) (3,399,634) (4,181,464) 3.48% (2,541,692) 3,466,049 8.49% 2.12% 4,381,471 17.70% 21,242,185 6,786,265 31,963,510 10.84% 158,217,328 4.95
2011 114,971,308 180,887,390 63.56% 4,915,363 139,822,500 (718,329) (3,171,869) (3,890,198) 3.38% (1,998,544) 3,149,353 7.86% 1.74% 4,197,034 17.10% 19,660,094 6,321,222 30,158,355 10.44% 150,729,035 5.00
2010 108,323,093 173,556,430 62.41% 4,872,527 126,945,459 (730,740) (3,155,825) (3,886,565) 3.59% (2,288,353) 2,824,627 8.31% 2.11% 4,141,787 18.30% 19,823,126 5,980,452 29,233,218 9.66% 144,323,212 4.94
2009 108,280,561 176,399,258 61.38% 5,814,294 125,278,106 (1,467,108) (2,929,624) (4,396,732) 4.06% (1,765,948) 3,030,485 8.49% 1.63% 4,347,186 18.20% 19,707,062 5,960,109 28,235,444 10.73% 148,163,814 5.25
SUM 584,709,560 926,270,395 26,282,921 691,460,512 (4,518,443) (15,840,653) (20,359,096) (10,526,232) 16,417,619 40.57% 21,764,478 103,196,372 32,122,070 153,460,851 772,809,544
AVERAGE 116,941,912 185,254,079 63.06% 5,256,584 138,292,102 (903,689) (3,168,131) (4,071,819) 3.50% (2,105,246) 3,283,524 8.11% 1.81% 4,352,896 17.68% 20,639,274 6,424,414 30,692,170 10.67% 154,561,909 5.04
AlJazira
SAR’000 2013 35,656,186 59,976,408 59.45% 1,645,129 48,082,525 (422,182) (1,187,660) (1,609,842) 4.51% (661,427) 650,636 8.19% 1.86% 1,222,947 15.01% 5,351,994 1,839,307 5,728,545 11.36% 54,247,863 9.47
2012 31,274,552 50,781,402 61.59% 1,262,507 40,675,290 (311,624) (1,097,096) (1,408,720) 4.50% (1,377,770) 500,480 10.51% 4.41% 950,883 15.67% 4,900,722 1,597,576 5,011,853 9.99% 45,769,549 9.13
2011 24,517,895 33,961,387 72.19% 968,116 31,158,531 (186,653) (905,187) (1,091,840) 4.45% (1,210,444) 302,911 10.63% 4.94% 781,463 17.40% 4,266,114 1,208,098 4,732,537 6.40% 33,961,387 7.18
2010 19,828,506 33,018,221 60.05% 868,346 27,344,918 (151,093) (1,126,491) (1,277,584) 6.44% (1,124,064) 28,575 12.26% 5.67% 717,253 15.72% 3,117,041 1,155,066 4,515,518 0.63% 28,212,539 6.25
2009 16,297,701 29,976,604 54.37% 961,241 22,142,476 (293,460) (1,143,482) (1,436,942) 8.82% (793,607) 27,554 13.86% 4.87% 667,781 17.73% 2,889,582 1,171,036 4,485,867 0.61% 25,282,270 5.64
SUM 127,574,840 207,714,022 5,705,339 169,403,740 (1,365,012) (5,459,916) (6,824,928) (5,167,312) 1,510,156 55.44% 4,340,327 20,525,453 6,971,083 24,474,320 187,473,608
AVERAGE 25,514,968 41,542,804 61.53% 1,141,068 33,880,748 (273,002) (1,091,983) (1,364,986) 5.75% (1,033,462) 302,031 11.09% 4.35% 868,065 16.31% 4,105,091 1,394,217 4,894,864 5.80% 37,494,722 7.53
TOTAL AVERAGE 92,325,365 165,716,651 57.99% 5,007,358 127,251,155 (829,540) (3,199,078) (4,028,619) 4.48% (2,760,703) 3,115,266 10.66% 3.17% 4,177,817 16.56% 15,601,603 5,933,545 21,732,340 13.09% 143,819,113 6.77
Dummy-2
Total
liabilities
Leverage
ratio
(%)
Provision
for loans
losses
Input expenses
Capital
Adequacy
Requirem
ent
(Tier1 &
Tier2)
Capital
Required
Total
Operating
income
Equity
(controlled)
ROE
Interest
income
received
NET
INCOME
Pricing of
the loan
under
"Cost-
plus" model
Probability
of Default
SPREED
NII
Bank
Name
Yrs
Total
LOANS
(Gross)
Total Assets
Loans`
share in
total
assets
(% )

26
Table 15: Data of Egypt`s banks of loan pricing and other financial indicators under the annual financial statements , 2009-2013
Sourse of information: Egypt` annual financial statements,2009-2013
Funds
Customer`s
term
deposits
Interest
expenses
paidfor
deposit
funds
Non
interest
expenses
(Average
operating
expenses)
TOTAL
costs of
fund
TOTAL
costs of
fund in %
of Gross
Loans
A
(Gross
Loans/A)
1 2 (CF) = 1 + 2
(CF/Gross
Loans)
(PL) (NI)
(CF+PL+NI)
/ Gross
Loans
PL/Gros
s Loans
Interest Inc
- Nonint.Inc
CAR
Gross
Loans*CAR
X2 Y X1 X4 X3
Ahli unitedbank
US$ `000 2013 17,994,279 32,651,893 55.11% 1,093,547 22,028,457 (380,298) (287,654) (667,952) 3.71% (688,597) 624,243 11.01% 3.83% 713,249 16.20% 2,915,073 958,329 3,148,824 19.82% 29,086,790 9.24
2012 16,580,273 29,872,574 55.50% 1,070,638 18,769,744 (434,265) (267,178) (701,443) 4.23% (608,054) 377,735 10.18% 3.67% 636,373 15.60% 2,586,523 848,706 2,776,209 13.61% 26,711,067 9.62
2011 16,046,376 28,329,762 56.64% 973,936 17,345,034 (407,009) (273,234) (680,243) 4.24% (549,786) 335,813 9.76% 3.43% 566,927 16.00% 2,567,420 842,112 2,911,141 11.54% 25,418,621 8.73
2010 14,910,281 26,457,461 56.36% 893,498 14,835,796 (384,724) (253,415) (638,139) 4.28% (432,568) 292,199 9.14% 2.90% 508,774 14.10% 2,102,350 754,669 2,752,175 10.62% 23,705,286 8.61
2009 13,664,194 23,573,983 57.96% 934,283 13,241,266 (467,698) (237,850) (705,548) 5.16% (364,195) 226,086 9.48% 2.67% 466,585 20.80% 2,842,152 696,386 2,213,523 10.21% 20,992,552 9.48
SUM 79,195,403 140,885,673 4,965,902 86,220,297 (2,073,994) (1,319,331) (3,393,325) (2,643,200) 1,856,076 2,891,908 13,013,518 4,100,202 13,801,872 125,914,316
AVERAGE 15,839,081 28,177,135 56.31% 993,180 17,244,059 (414,799) (263,866) (678,665) 4.33% (528,640) 371,215 9.91% 3.30% 578,382 16.54% 2,602,704 820,040 2,760,374 13.16% 25,182,863 9.14
Commercial International Bank (CIB)
EGP`000 2013 4,186,567 113,751,995 36.80% 9,520,697 96,845,683 (4,466,949) (2,046,697) (6,513,646) 15.56% (2,864,251) 3,006,376 29.58% 6.84% 5,053,748 13.55% 5,672,799 767,392 11,959,712 25.14% 101,744,868 8.51
2012 41,877,182 93,956,544 44.57% 7,859,312 78,729,121 (3,945,685) (1,783,034) (5,728,720) 13.68% (1,930,521) 2,226,990 23.61% 4.61% 3,913,626 15.71% 6,578,905 574,575 10,764,528 20.69% 83,144,496 7.72
2011 42,933,133 85,534,176 50.19% 5,470,991 71,467,935 (2,781,039) (1,647,129) (4,428,169) 10.31% (1,457,359) 1,614,228 17.47% 3.39% 2,689,951 13.78% 5,916,186 499,179 8,740,078 18.47% 76,747,741 8.78
2010 36,716,652 75,425,434 48.68% 4,525,478 63,364,177 (2,267,786) (1,592,193) (3,859,979) 10.51% (1,257,882) 2,022,009 19.45% 3.43% 2,257,691 16.92% 6,212,458 874,837 8,566,937 23.60% 66,811,531 7.80
2009 28,981,189 64,124,698 45.20% 4,032,639 54,648,654 (2,002,606) (1,268,653) (3,271,260) 11.29% (1,677,505) 1,709,767 22.98% 5.79% 2,030,032 17.10% 4,955,783 619,095 6,996,056 24.44% 57,083,034 8.16
SUM 192,373,831 432,792,846 31,409,116 365,055,572 (15,464,067) (8,337,708) (23,801,775) (9,187,519) 10,579,372 15,945,049 29,336,131 3,335,078 47,027,311 385,531,670
AVERAGE 38,474,766 86,558,569 45.09% 6,281,823 73,011,114 (3,092,813) (1,667,542) (4,760,355) 12.27% (1,837,504) 2,115,874 22.62% 4.81% 3,189,009 15.41% 5,867,226 667,016 9,405,462 22.47% 77,106,334 8.19
HSBC Egypt
EGP`000 2013 19,922,910 58,581,998 34.01% 3,904,499 49,317,549 (1,471,122) (1,683,137) (3,154,259) 15.83% (938,178) 890,826 25.01% 4.71% 2,433,377 16.10% 3,207,589 184,560 4,940,656 18.03% 53,641,342 10.86
2012 20,306,718 53,944,451 37.64% 3,671,213 47,237,707 (1,475,899) (1,153,573) (2,629,472) 12.95% (683,335) 1,418,839 23.30% 3.37% 2,195,314 12.67% 2,572,861 330,608 4,399,891 32.25% 49,544,560 11.26
2011 20,068,344 48,309,210 41.54% 3,173,698 42,195,945 (1,359,521) (1,128,640) (2,488,161) 12.40% (627,473) 1,119,959 21.11% 3.13% 1,814,177 12.23% 2,454,358 339,916 4,213,379 26.58% 44,095,831 10.47
2010 17,395,516 45,112,318 38.56% 2,578,478 39,754,474 (1,224,156) (960,227) (2,184,383) 12.56% (492,653) 330,154 17.29% 2.83% 1,354,322 12.56% 2,184,877 387,038 3,606,634 9.15% 41,505,684 11.51
2009 14,214,752 36,114,722 39.36% 2,338,638 31,551,059 (1,093,185) (584,142) (1,677,327) 11.80% (233,423) 308,001 15.61% 1.64% 1,245,453 12.77% 1,815,224 763,537 3,481,218 8.85% 32,633,504 9.37
SUM 91,908,240 242,062,699 15,666,526 210,056,734 (6,623,883) (5,509,719) (12,133,602) (2,975,062) 4,067,779 9,042,643 12,234,909 2,005,659 20,641,778 221,420,921
AVERAGE 18,381,648 48,412,540 38.22% 3,133,305 42,011,347 (1,324,777) (1,101,944) (2,426,720) 13.11% (595,012) 813,556 20.46% 3.13% 1,808,529 13.27% 2,446,982 401,132 4,128,356 18.97% 44,284,184 10.69
Bank of Alexandria
EGP`000 2013 22,502,484 40,920,267 54.99% 3,777,824 33,924,373 (1,731,512) (1,585,237) (3,316,749) 14.74% (2,401,020) 661,114 28.35% 10.67% 2,046,312 15.43% 3,472,133 117,485 4,540,179 14.56% 36,380,088 8.01
2012 22,474,919 41,112,114 54.67% 3,809,709 33,466,748 (1,748,308) (1,642,384) (3,390,692) 15.09% (2,554,732) 625,065 29.23% 11.37% 2,061,401 14.35% 3,225,151 247,082 4,290,805 14.57% 36,821,309 8.58
2011 22,205,242 37,811,218 58.73% 3,022,318 30,781,126 (1,528,944) (1,479,840) (3,008,784) 13.55% (2,323,683) 332,779 25.51% 10.46% 1,493,374 16.38% 3,637,219 136,614 3,709,107 8.97% 34,102,111 9.19
2010 20,565,738 37,218,376 55.26% 2,516,721 27,613,744 (1,253,813) (957,093) (2,210,906) 10.75% (1,979,773) 656,503 23.57% 9.63% 1,262,908 14.27% 2,934,731 143,725 3,678,511 17.85% 33,539,865 9.12
2009 15,242,847 31,426,138 48.50% 2,240,813 25,287,763 (2,000,966) (357,521) (2,358,487) 15.47% (1,932,878) 406,550 30.82% 12.68% 239,847 20.86% 3,179,658 281,221 2,328,093 17.46% 29,098,045 12.50
SUM 102,991,230 188,488,113 15,367,385 151,073,754 (8,263,543) (6,022,075) (14,285,618) (11,192,086) 2,682,011 7,103,842 16,448,891 926,127 18,546,695 169,941,418
AVERAGE 20,598,246 37,697,623 54.43% 3,073,477 30,214,751 (1,652,709) (1,204,415) (2,857,124) 13.92% (2,238,417) 536,402 27.50% 10.96% 1,420,768 16.26% 3,289,778 185,225 3,709,339 14.68% 33,988,284 9.48
Misr S.A.E.
EGP`000 2013 57,126,513 218,160,711 26.19% 16,347,625 188,833,818 (10,759,245) (4,452,934) (15,212,179) 26.63% (8,135,400) 1,160,632 42.90% 14.24% 5,588,380 13.53% 7,729,217 1,233,711 14,474,978 8.02% 203,685,733 14.07
2012 52,055,600 187,842,754 27.71% 13,027,218 162,523,605 (9,055,521) (3,018,373) (12,073,894) 23.19% (8,330,586) 708,863 40.56% 16.00% 3,971,697 13.33% 6,939,011 629,280 12,299,668 5.76% 175,543,086 14.27
2011 55,470,984 177,450,400 31.26% 10,767,311 154,474,764 (8,629,042) (2,159,292) (10,788,334) 19.45% (9,778,502) 515,382 38.01% 17.63% 2,138,269 13.03% 7,227,869 1,145,238 7,037,463 7.32% 170,412,937 24.22
2010 65,415,748 178,930,206 36.56% 9,145,634 144,482,502 (7,875,854) (1,121,548) (8,997,402) 13.75% (23,670,169) 509,658 50.72% 36.18% 1,269,780 13.28% 8,687,211 1,021,900 6,869,548 7.42% 172,060,658 25.05
2009 64,887,342 153,504,341 42.27% 8,939,886 131,732,185 (7,993,004) (2,320,376) (10,313,380) 15.89% (17,386,274) 164,974 42.94% 26.79% 946,882 22.00% 14,275,215 1,427,857 6,955,155 2.37% 146,549,186 21.07
SUM 294,956,187 915,888,412 58,227,674 782,046,874 (44,312,666) (13,072,523) (57,385,189) (67,300,931) 3,059,509 13,915,008 44,858,524 5,457,986 47,636,812 868,251,600
AVERAGE 58,991,237 183,177,682 32.80% 11,645,535 156,409,375 (8,862,533) (2,614,505) (11,477,038) 19.78% (13,460,186) 611,902 43.03% 22.17% 2,783,002 15.03% 8,971,705 1,091,597 9,527,362 6.18% 173,650,320 19.74
TOTAL AVERAGE 26,256,031 66,210,957 39.11% 4,332,297 54,981,146 (2,646,143) (1,181,426) (3,827,569) 10.93% (3,217,200) 767,060 21.30% 7.65% 1,686,153 0.00% 3,996,275 545,691 5,091,533 13.01% 61,071,032 9.87
Dummy-3
Provision
for loans
losses
Input expenses
Total
liabilities
Leverage
ratio (%)
Capital
Adequacy
Requireme
nt (Tier 1
and Tier 2)
Capital
Required
Total
Operating
income
Equity
(controlled)
ROE
Interest
income
received
NET
INCOME
Pricing of
the loan
under
"Cost-
plus"
model
Probabil
ity of
Default
SPREED
NII
Bank
Name
Yrs
Total
LOANS
(Gross)
Total Assets
Loans`
share in
total
assets
(% )

27
Table 16: Data for the UAE bank’s customers who have consumer’s loans
Customer. N LA ES NY M/S N I O Res M/F Ag
e
001157850342 138,000 G 15 M India 8,000 Employee Al M 40
005678394563 200,000 O 10 M India 15,000 Employee D M 36
003234112466 75,000 O 8 S India 6,000 Employee Sh M 30
009793423212 110,000 O 8 S India 9,000 Employee D M 48
002341199632 1,250,000 O 23 S UAE 33,000 Accountant D M 48
005664345214 1,000,000 O 16 S UAE 30,000 Employee D F 36
001211235743 455,000 O 6 M India 24,500 Manager D M 35
007325622134 970,000 G 22 M UAE 23,000 Employee Sh M 52
005353466122 50,000 G 6 S India 5,000 Employee Al M 34
002346747356 800,000 G 9 S UAE 25,000 Accountant Sh M 40
009214863251 185,000 G 15 M India 16,500 Employee D M 38
003151612834 400,000 O 10 S India 9,000 Employee Al M 52
004613485163 175,000 G 5 S Philippines 10,000 Employee D F 32
001178335263 1,117,000 O 22 S UAE 20,000 Employee D M 44
004635224598 1,000,000 G 20 S UAE 18,500 Employee Ab M 35
004672945621 200,000 O 10 M India 10,000 Employee Al M 50
003416135183 1,300,000 O 5 M UAE 25,500 Employee D M 42
004952933422 820,000 G 4 M UAE 40,000 Lawyer Ab M 33
006492756932 200,000 O 6 M India 15,500 Engineer D M 35
005446766755 1,200,000 O 10 S India 45,500 Manager D M 38
003353567392 180,000 O 2 S Jordan 17,000 Employee Sh M 34
001132215489 200,000 O 0.75 M India 11,500 Accountant D M 50
004567423421 200,000 O 1.5 M India 11,500 Engineer Al M 40
008753873812 250,000 G 6 M India 13,000 Nurse D F 45
001235964543 200,000 O 1.5 M India 11,500 Accountant Ab M 40
002032389766 285,000 G 5 M UAE 27,000 Employee D M 24
005345438632 160,000 G 11 S India 9,000 Employee Sh F 34
001212231429 250,000 O 6 S Egypt 15,500 Teacher D M 38
001241563643 125,000 G 1.5 S India 7,500 Employee Al M 42
005634639292 387,000 O 6.5 M India 27,500 Employee D M 39
008241462340 1,500,000 G 17 S UAE 24,000 Employee D F 44
006734829321 70,000 O 0.75 M India 8,500 Employee Al M 30
009503452372 122,000 O 10 S India 9,000 Engineer Sh F 40
009379235129 150,000 G 15 S India 8,000 Employee Sh M 40
003256262210 1,750,000 G 18 M UAE 40,000 Accountant Ab M 38
004563242198 1,075,000 O 16 M UAE 30,000 Employee D F 36
007234792102 455,000 O 6 M India 30,000 Manager D M 40
005259267234 900,000 G 18 S UAE 40,000 Engineer Ab M 52
006296223952 800,000 G 11 M UAE 30,000 Accountant D M 36
001185329995 700,000 O 10 S India 15,000 Teacher Al M 50
004581954654 1,300,000 O 10 M UAE 35,500 Engineer D M 42
002935672914 820,000 G 5 M UAE 25,000 Employee Sh F 33
004539345020 150,000 G 9 S India 15,500 Engineer D M 35

28
005363232312 150,000 O 4.5 M Philippines 12,000 Employee Sh M 35
006351459215 200,000 O 4 M India 20,000 Teacher D M 50
004125125672 150,000 O 4 M Egypt 11,500 Employee Al M 40
002352892134 800,000 G 10 S India 30,000 Doctor D F 45
007648924346 500,000 G 6 S India 16,000 Employee Al M 34
001289234642 200,000 O 4.5 M India 14,000 Accountant Ab M 40
004537862342 400,000 G 4 S UAE 20,000 Employee D M 24
005325004202 400,000 O 6 M India 15,000 Teacher Al M 30
009431234851 160,000 G 2 S India 9,000 Employee D M 28
002323985212 1,117,000 O 8 S UAE 25,000 Employee Ab M 40
002145342342 1,200,000 G 15 M UAE 20,000 Employee Sh M 42
004534523429 350,000 G 5 S India 12,000 Employee D F 34
006543623123 200,000 O 4 S Jordan 15,500 Employee D M 38
002345662312 950,000 O 8 M India 30,500 Doctor D M 39
00523523991 1,700,000 G 9 M UAE 30,000 Accountant D F 44
00432344432 250,000 O 8 S India 16,000 Teacher D F 40
00975223412 300,000 G 6 M Jordan 16,500 Employee D M 35
00235922545 900,000 G 14.5 S UAE 23,000 Employee Sh M 42
00348212112 185,000 G 15 S India 16,500 Employee D M 40
00763223541 700,000 O 10 S India 15,000 Employee Ab M 52
00886786889 300,000 G 5 S Egypt 18,000 Teacher D M 32
00875883425 1,117,000 O 14 M UAE 28,000 Employee Al M 44
00987892134 850,000 G 13 M UAE 25,000 Employee Ab M 42
00864386723 260,000 O 11 M India 10,000 Employee Al F 34
00983218967 150,000 O 12 S Uzbekistan 30,000 Accountant D F 34
00764545769 1,500,000 G 13 M UAE 24,000 Employee D F 40
00525235287 175,000 O 5 S India 9,500 Teacher Ab F 40
00243974351 75,000 O 8 M India 7,000 Employee Al M 30
00693797512 180,000 O 16 M Philippines 20,000 Accountant Sh F 45
00476324244 1,750,000 O 23 M UAE 40,000 Manager D M 38
00753432927 1,075,000 O 16 S UAE 30,000 Doctor Ab F 36
00632434765 1,000,000 G 3 M India 12,000 Accountant D M 26
00866388634 700,000 G 5 S UAE 45,000 Doctor D M 30

29
Table 17: Results ofcredit scoring for each customer and the required interest rate for each ofthem
Customer N Loan amount Salary / Income Score rate Interest rate Status of loan
001157850342 138,000 8,000 530 11.00% Performed
005678394563 200,000 15,000 512 8.05% Performed
003234112466 75,000 6,000 509 12.00% Non-Performed
009793423212 110,000 9,000 499 12.00% Performed
002341199632 1,250,000 33,000 540 7.00% Performed
005664345214 1,000,000 30,000 539 7.50% Performed
001211235743 455,000 24,500 523 6.00% Performed
007325622134 970,000 23,000 533 6.00% Performed
005353466122 50,000 5,000 513 12.00% Performed
002346747356 800,000 25,000 509 7.50% Performed
009214863251 185,000 16,500 547 6.50% Performed
003151612834 400,000 9,000 496 12.00% Performed
004613485163 175,000 10,000 478 16.00% Performed
001178335263 1,117,000 20,000 502 8.50% Performed
004635224598 1,000,000 18,500 523 8.50% Performed
003452346230 350,000 22,000 522 6.00% Performed
004672945621 200,000 10,000 495 12.00% Performed
003416135183 1,300,000 25,500 506 7.50% Performed
004952933422 820,000 40,000 529 6.75% Performed
006492756932 200,000 15,500 523 6.50% Performed
005446766755 1,200,000 45,500 530 6.00% Performed
003353567392 180,000 17,000 483 10.50% Performed
001132215489 200,000 11,500 461 12.00% Performed
004567423421 200,000 11,500 518 8.05% Performed
008753873812 250,000 13,000 494 8.96% Performed
004593293412 50,000 5,000 542 11.00% Non-Performed
001235964543 200,000 11,500 497 8.96% Performed
002032389766 285,000 27,000 499 7.75% Performed
002255606012 1,210,000 57,000 479 6.50% Performed
005345438632 160,000 9,000 522 11.00% Performed
005632524350 120,000 9,500 506 12.00% Performed
001212231429 250,000 15,500 495 8.96% Performed
001241563643 125,000 7,500 500 12.00% Performed
005634639292 387,000 27,500 515 8.05% Performed
008241462340 1,500,000 24,000 577 6.50% Performed
006734829321 70,000 8,500 504 12.00% Performed
009503452372 122,000 9,000 503 12.00% Performed
009379235129 150,000 8,000 505 8.54% Performed
001239875634 200,000 13,000 495 8.96% Performed
005320545040 90,000 6,000 513 12.00% Performed
005132532055 110,000 18,000 529 6.50% Performed
003256262210 1,750,000 40,000 528 6.75% Performed
004563242198 1,075,000 30,000 550 7.00% Performed
007234792102 455,000 30,000 502 7.50% Performed
005259267234 900,000 40,000 546 6.75% Performed
006262320245 70,000 9,000 529 11.00% Performed
006296223952 800,000 30,000 527 7.50% Performed
002349875763 350,000 25,000 512 8.05% Performed

30
001185329995 700,000 15,000 544 6.50% Performed
004581954654 1,300,000 35,500 519 7.50% Performed
002935672914 820,000 25,000 518 8.05% Performed
004539345020 150,000 15,500 547 6.50% Performed
004300534611 1,200,000 45,500 536 5.50% Performed
005363232312 150,000 12,000 491 12.00% Performed
006351459215 200,000 20,000 524 6.50% Performed
004125125672 150,000 11,500 462 16.00% Non-Performed
002352892134 800,000 30,000 544 6.00% Performed
007648924346 500,000 16,000 508 8.54% Performed
001289234642 200,000 14,000 516 8.05% Performed
004537862342 400,000 20,000 491 8.96% Performed
008522349223 1,210,000 56,000 530 5.50% Performed
009573923412 300,000 16,500 522 6.50% Performed
005325004202 400,000 15,000 505 8.54% Performed
009431234851 160,000 9,000 500 12.00% Performed
002323985212 1,117,000 25,000 507 7.50% Performed
002145342342 1,200,000 20,000 557 6.50% Performed
004534523429 350,000 12,000 508 8.54% Performed
007668823412 470,000 9,500 499 12.00% Non-Performed
006543623123 200,000 15,500 478 12.00% Performed
005345224128 150,000 7,000 523 11.00% Performed
002345662312 950,000 30,500 523 6.00% Performed
00523523991 1,700,000 30,000 543 7.00% Performed
00353235221 100,000 8,500 497 12.00% Performed
00432344432 250,000 16,000 516 8.05% Performed
00975223412 300,000 16,500 493 8.96% Performed
00239874134 400,000 15,000 491 8.96% Performed
00429751605 260,000 10,000 475 16.00% Performed
00452900342 1,050,000 25,000 525 7.50% Performed
00235922545 900,000 23,000 565 6.50% Performed
00348212112 185,000 16,500 530 6.50% Performed
00763223541 700,000 15,000 491 8.96% Performed
00886786889 300,000 18,000 495 8.96% Performed
00875883425 1,117,000 28,000 534 7.50% Performed
00987892134 850,000 25,000 541 7.50% Performed
00864386723 260,000 10,000 501 8.54% Performed
00459797126 190,000 12,500 495 8.96% Performed
00983218967 150,000 30,000 462 12.00% Performed
00865425632 150,000 8,000 508 12.00% Performed
00232389981 750,000 27,500 505 8.54% Performed
00764545769 1,500,000 24,000 553 7.00% Performed
00325792195 80,000 8,500 504 12.00% Performed
00525235287 175,000 9,500 511 8.05% Performed
00435454139 400,000 18,000 536 6.50% Performed
00232233567 350,000 15,000 501 8.96% Performed
00243974351 75,000 7,000 509 12.00% Performed
00693797512 180,000 20,000 517 8.05% Performed
00476324244 1,750,000 40,000 560 6.50% Performed
00753432927 1,075,000 30,000 586 6.50% Performed
00632434765 1,000,000 12,000 489 10.50% Performed
00866388634 700,000 45,000 526 6.50% Performed

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