A Study on Credit Risk Management

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A Study on Credit Risk Management


The history of the banking sector of Bangladesh is fairly short and started with the nationalization of all banks (except the branch of foreign banks) operating in the country immediately after the liberation. As a step towards establishment of the government’s socialist leaning economic policy, all banks were nationalized. The banking sector of Bangladesh has grown over the time under the bank branching system. Structurally, the banking system is composed of institutions: the central bank, commercial banks, specialized banks, development financing institutions (non-banking institutions) and cooperative banks. The banking sector of Bangladesh provides an interesting study regarding various effects of macroeconomic variables and portfolio elements on the profitability of the banks because it went through phases of both financial repression and liberalization. The issue of profitability and the banking sector performance in Bangladesh continues to be a perennial source of discussion among academicians, policy makers and the practitioners. Credit risk decision is crucial for a financial institution like bank.

So here we would like to attempt to study on credit risk management and we have selected our field as United Commercial Bank (UCBL).

Basically we would try

· To assess the credit risk managing behavior of our selected bank UCBL

· To examine the relationship between Mortgages and default risk premium by bank.

The profitability of banks depends on the operational efficiency of the banks no doubt but some scholars in this fields, economists and policy makers believe that the profitability of banks also depends on some external factors as well as internal. These are general expectation of banks with respect to general economic trends, environmental, seasonal and political situations. However the specific expectations are the changes in money supply, capital market and money market trends, export-import, real and service sector production etc. many economists found that the control of expenditures is the prime determinant of the creditworthiness of client. . Mogen (1971) in his study mentioned that the money market conditions, money supply, population growth rate and individual banking habit have effects on the banking performance i.e. it is effecting the credit risk managing behavior and assessing behavior of the bank. These factors determine the degree of operating risk or the quality of the operating earning flows of the banks.

A panorama of UCBL

Sponsored by some dynamic and reputed entrepreneurs and eminent industrialists of the country and also participated by the Government, UCBL started its operation in mid 1983 and has since been able to establish the largest network of 80 branches as on 31. 08. 2001 among the first generation banks in the private sector.

With its firm commitment to the economic development of the country, the Bank has already made a distinct mark in the realm of Private Sector Banking through personalized services, innovative practices, dynamic approach and efficient Management. The Bank, aiming to play a leading role in the economic activities of the country, is firmly engaged in the development of trade, commerce and industry through a creative credit policy.

The Bank closed the year under the recording steady growth. At the end of the year 2001,total assets of the bank stood at T.k1834.82million as against T.k1591.61million registering an increase of 15.26percent.

Total assets included Tk.2416.10 million cash in hand, reserve with Bangladesh Bank and Sonali Bank. Total liquid assets including investment registered an increase of 20.44 percent in the year under review. Total liquid assets including investment stood at Tk.6609.36 million during the year of against Tk.5487.72million in the previous year.

UCBL product and services

  • One Stop Service
  • Time Deposit Scheme
  • Monthly Savings Scheme
  • Deposit Insurance Scheme
  • Inward & Outward Remittances
  • Travelers Cheques
  • Import Finance

Major services

  • Working Capital Finance
  • Loan Syndication
  • Underwriting and Bridge Financing
  • Trade Finance
  • Credit Scheme
  • Locker Service
  • Foreign Currency Deposit A/C
  • NFCD (Non Resident Foreign Currency Deposit Account)

Other services


Mr. Muhammad Sajid-ul-Ha, Managing Director, successfully leads the Management team of the Bank. He is a renowned and dynamic banker with more than three decades of banking experience to his credit Prominent and dynamic banker Mr.Hamidul Huq, is the additional Managing Director of the Bank. Mr. Md.Salauddin Gazi and Mr.Bakhtiar HossainChowdhary are the Deputy Managing Directors of the Bank. The Management is ably supported and assisted by qualified executive and officers.

Capital and Reserve

During the year under report authorized capital of the Bank remained unchanged at Tk.1000.00million and the paid-up capital stood at Tk.230.16million.The reserve fund of the Bank increased by 12.82 percent to Tk. 393.50 million as against Tk.348.78 million in the previous year.

Human Resources

Imbibed with the spirit of building a creative work force, UCB puts in utmost endeavor to take over the challenges of modern banking. Since there is no alternative to training for acquiring the required efficiency and professional excellence, Bank’s Training Institute was throughout the year to impart

training on different aspects of Banking. During 2001, five-in-house training courses were arranged in which ninety officers took part. Moreover a number of executive and officers were sent to Bangladesh Institute of Bank Management (BIBM) and other training agencies. Employee’s performances are regularly evaluated and a good number of them have been promoted as reward and recognition of their good performance. At the end of 2001total number of employees stood at 1828comprising 75 executives, 1061offers and 676 staff.


The Deposit of the Bank registered an increased Tk. 15.00 percent in the year under review. At the close of 2001,total deposits stood at Tk.14245.87 million as against Tk.12387.47 million in the previous year. The deposit mix comprised Tk.4258.09million as demand and Tk.9987.78 million as time deposit. The ratio between demand and time liability was 29.89:70.11. out of the total deposits, Tk.12424.58 million was mobilized from the private sector while the balance Tk.1821.29 million from the public sector. Average deposit per branch was Tk.180.33 million in 2001.


The Bank continued its participation in different credit programmers for financing new industrial projects, working capital, trade finance, international trade etc. Consequently net credit rose to Tk.10941.98million in 2001 from Tk.119.54 million of 2000.Average advance per branch increased to Tk.138.51 million from Tk.9443.87 million of 2000.The credit deposit ratio stood at 0.77:1. Sector wise net advances during the year were as follows:

Sector Tk. In Million

1. Continuous Loan 6873.65

2. Demand Loan 749.64

3. Term Loan (unto 5 years) 1890.10

4. Term Loan (over 5 years) 1323.28

5. Staff Loan 105.31

Total amount of loan 10941.98


At the close of 2001, total investment of the Bank stood at Tk. 2162.92 million in 2001.However, divided amounting to Tk.0.51 million has been received from different companies/institutions against investment in shares during the year under report.

Foreign Trade

During the year 2001, the Bank opened 8761 letters of credit for important worth Tk.13132.90 million compared to 9583 letters of credit worth Tk. 12534.40 million in 2000.The volume of export bill handed by the Bank wasTk.5309.30 million in 2001.


Experienced officials regularly audit and inspect the activities of the Bank throughout the year. During 2001audit and inspection were carried out .Bangladesh Bank Inspection Team also conducted inspection of 25 branches during the year under review. Moreover, they inspected Bank’s activities relating to foreign exchange at Head office and 17A.D branches authorized to deal directly in foreign exchange transactions.

Some ratio to judge bank’s current condition

Net Interest Margin (NIM)

A number of other profit measures are commonly used in banking, which provides further insight into a bank’s financial performance. One of them the most important is NIM that is Net interest Margin, which measure how effectively a corporation utilizes its earning assets in relation to the interest cost of funding.

NIM (%) = (Total interest income – Total interest expense) / Average

Earning assets

YEAR Total interest income Total interest expenses
Average Earning Assets
NIM (%)
1998 258523095 237324232 3820029159 .55
1999 468671807 377744520 6665118388 1.36
2000 1020392051 691594670 12986669244 2.53
2001 1528678923 1168952993 17243093906 2.09

Provision for Loan Losses (PLL)

Each bank provides an estimation of future loan losses as an expense on its income statement. This expense can be related to the volume of loan as follows:

PLL (%) = Provision for loan losses /Total loan & Leases C 100

1999 24731000 2283648484 1.08
2000 51323000 4588087640 1.12
2001 166854000 8044426040 2.07
2002 298730000 9391643297 3.18

Loan Ratio

The loan ratio indicates the extent to which assets are devoted to loan as opposed to other assets, including cash, securities and plant & equipment.

Loan ratio (%) = Net Loan / Assets C 100

1999 2283648484 4000810543 57.08
2000 4588087640 6966336681 65.86
2001 8044426040 13463230033 59.75
2002 9391643297 17865667543 52.57


Interest sensitivity is the responsiveness of liquidity costs and asset returns to changes in the interest rates. The difference between the quantities of interest sensitive assets and liabilities is known as Dollar gap ratio. The gap ratio suggests that bank’s profitability will be affected by the change in the interest rate. Here rate sensitive is defined as short-term assets and liabilities with maturity period of less than one year.

YEAR 1999 2000 2001 2002
Gap Ratio (%) 0.69 -0.89 -6.06 -9.41


· Interest rate sensitive assets are the sum of balances with other banks and financial institution, Loans & advances, Investment at cost.

· Interest rate sensitive liabilities are the sum of borrowing from other banks and financial institutions and deposit and other accounts.

Credit Risk Management: Theoretical Aspect

Measurement of Credit Risk

To calibrate the default risk exposure of its credit and investment decisions as well as to assess its credit risk exposure in off-balance-sheet contractual arrangements such as loan commitments, a Financial Institutions manager needs to measure the probability of borrower default. The ability to do this largely depends on the amount of information the Financial Institutions has about the borrower. At the retail level, much of the information needs to be collected internally or purchased from external credit agencies such as American Management Systems. At the wholesale level, these information sources are bolstered by publicly available information such as certified accounting statements, stock and bond prices, and analysts’ reports.

Default Risk Models

Economists, bankers, and analysts have employed many different models to assess the default risk on loans and bonds. These very from the relatively qualitative to the highly quantitative. Further, these models are not mutually exclusive, in that a Financial Institutions manager may use more than one to reach a credit pricing or loan quantity rationing decisions. We analyze a number of these models.

Qualitative models

In the absence of publicly available information on the quality of borrowers, the Financial Institutions manager has to assemble information from private sources – such as credit and deposit files – and /or purchase such information from external sources – such as credit rating agencies.

In general, the amount of information assembled varies with the size of the potential debt exposure and the costs of collection. However, a number of key factors enter into the credit decision. These include:

(1) Borrower – specific factors that are idiosyncratic to the individual borrower

(2) Market – specific factors that have an impact on all borrowers at the time of the credit decision

Borrower Specific Factors


The borrower’s reputation involves the borrowing – lending history of the credit applicant. If, over time, the borrower has established a reputation for prompt and timely repayment, this enhances the applicant’s attractiveness to the Financial Institutions. A long-term customer relationship between a borrower and lender forms an implicit contract regarding borrowing and repayment that extends the formal explicit legal contract on which borrower – lender relationships based.


A borrower’s leverage or capital structure – the ratio of debt to equity – affects the probability of its default. This is because large amounts of debt, such as bonds and loans, increase the borrower’s interest charges and pose a significant claim on its cash flows. As shown in the figure, the relatively low debt – equity ratios may not significantly impact the probability of debt repayment. Yet, beyond some point, the risk of bankruptcy increases, as done the probability of some loss of interest or principal for the lender.

Volatility of Earnings

As with leverage, a highly volatile earnings stream increases the probability that the borrower cannot meet fixed interest and principle charges for any given capital structure. Consequently, newer firms, or firms in high – tech industries with a high earnings variance over time, are less attractive credit risks than those with long and more stable earnings histories.


Of default

0 D / E* Leverage (Debt – Equity ratio)Collateral

As discussed earlier, a key future in any lending and long – pricing decision is the degree of collateral or assets backing the security of the loan. Many loans and bonds are backed by specific assets should a borrower default on repayment obligations. Mortgage bonds give the bondholder first claim to some specific piece of property of the borrower, normal machinery or buildings, debentures give a bondholder a more general and more risky claim to the borrower’s assets.

Market Specific Factor

The Business Cycle

The position of the economy in the business cycle phase is enormously important to a Financial Institutions in assessing the probability of borrower default. For example, during recessions, firms in the consumer durable goods sector that produce autos, refrigerators, or houses do relatively badly compared to those in the non-durable goods sector producing tobacco and foods.

The Level of Interest Rates

High interest rates indicate restrictive monetary policy actions by the Federal Reserve. Financial Institutions not only find funds to finance their lending decisions scarcer and more expensive but also must recognize that high interest rates are correlated with higher credit risk in general.

So far, we have delineated just a few of the qualitative borrower and economy – specific factors an Financial Institutions manager may take into account in deciding on the probability of default on any long or bond. Rather than letting such factors enter into the decision process in a purely subjective fashion, the Financial Institutions manager may weight these factors in a more objective or quantitative manner.

Credit Scoring Models

Credit scoring models use data on observed borrower characteristics either to calculate the probability of default or to sort borrowers into different default risk classes. By selecting and combining different economic and financial borrower characteristics, an FI manager may be able to:

01. Numerically establish factor, which is important explaining default risk.

02. Evaluate the relative degree or importance of these factors.

03. Improve the pricing of default risk.

04. Be better able to screen out bad loan applicants.

05. Be in a better position to calculate any reserves needed to meet expected future loan losses.

Credit scoring models include these four broad types:

· Linear probability models.

· Logit model.

· Probit models.

· Linear discriminant analysis

Next we take a brief look at each of these models and their major strengths and weaknesses

Linear Probability Model

The linear probability model uses past data, such as accounting ratios, as inputs into a model to explain repayment experience on old loans.

Briefly, we divide old loans ( i ) into two observational groups, those that defaulted (Zi = 1) and those that did not default (Zi = 0). We estimate the model by linear regression of this form:

Zi = S bjXij + error

Where bj is the estimated importance of the j ith variable (leverage) in explaining past repayment experience.

Cumulative probability of default

E (Zi)



While this technique is straight forward as long as current information on the Xij is available for the borrower, its major weakness is that the estimated probabilities of default can often lie outside the interval 0 to 1.

The Logit Model

The logit model constrains the cumulative probability of default on a loan to lie between 0 and 1 and assumes the probability of default to be logistically distributed according to the functional form:


F(Zi) = ¾¾¾¾

1 + e-z

Where e stands for exponential, F(Zi) is the cumulative probability of default on the loan, and Zi is estimated by regression in a similar fashion to the linear probability model. Basically, we can estimate a projected value for Zi for a prospective borrower from a regression model I the same fashion as the linear probability model.

The Probit Model

The Probit model also constrains the projected probability of default to lie between 0 and 1, but differs from the logit model in assuming that the probability of default has a (cumulative) normal distribution rather than the logistic function.

Linear Discriminant Models

While linear probability, logit models, and probit models all estimate or project a value for the expected probability of default, should a loan be made, discriminant models divide borrowers into high or low default risk classes contingent on their observed characteristics (Xj).

Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5

Altman’s discriminant function takes the form:

Where, X1 = Working capital / total asset ratio

X2 = Retained earnings / total asset ratio

X3 = Earnings before interest and tax / total asset ratio

X4 = Market value of equity / book value of long-term debt ratio

X5 = Sales / total assets ratio

The higher the value of Z, the lower the default risk classification of the borrower. Thus, low r negative values of Z may be evidence of the borrower being a member of a relatively high default risk class.

Suppose that the financial ratios of a potential borrowing firm took the following values

X1 = .2

X2 = 0

X3 = -.20

X4 = .10

X5 = 2.0

The ratio X2 is zero and X3 is negative, indicating that the firm has had negative earnings or losses in recent periods. Also, X4 indicates the borrower is highly leveraged. However, the working capital ratio (X1) and the sales by asset ratio (X5) indicate the firm is reasonably liquid and is maintaining its sales volume. The Z score provide an overall score and indicator of the borrower’s credit risk since it combines and weights these five factors accordingly to their past importance in explaining borrower default. For the borrower in question:

Z = 1.2(.2) + 1.4(0) + 3.3(-.20) + .06(.10) + 1.0(2.0)

Z = 0.24 + 0 – .66 + 0.06 + 2.0

Z = 1.64

According to Altman’s credit scoring model, any firm with a Z score of less than 1.81 should be placed in the high default risk region. Thus the FI should not make a loan to this borrower until it improves its earnings.

Term Structure derivation of Credit Risk

One market – based method of assessing credit risk exposure and default probability is to analyze the risk premiums inherent in the current structure of yields on corporate debt or loans to similar risk – rated borrowers. Rating agencies categorize corporate bond issuers into seven major classes according to perceived credit quality. The first four quality ratings is AAA, AA, A and BBB. Which is indicating investment quality borrowers. The rest three-quality rating is BB, B and CCC. These three classes are known as high – yield or junk bonds. Different quality ratings are reflected in the degree to which corporate bond yields exceed those implied by the Treasury (credit risk – free) yield curve.

Treasury Strips and Zero – coupon Corporate Bonds

Bonds that are created or issued bearing no coupons and only a face value to be paid on maturity. Treasury strips and zero – coupon corporate are single – payment discount bonds, it may be possible to extract required credit risk premium and implied probabilities of default from actual market data on interest rates. That is, the spreads between risk – free deep – discount bonds issued by the Treasury and deep – discount bonds issued by corporate borrowers of differing quality may reflect perceived credit risk exposure of corporate borrowers for single payments at different times in the future.

Probability of Default on a One – Period Debt Instrument

Assume that the FI requires an expected return on a one – year corporate debt security at least equal to the risk – free return on Treasury bonds of one year’s maturity.

Let p be the probability that the corporate debt, both principal and interest will be repaid in full, therefore 1 – p is the probability of default. By denoting the promised return on the one – year corporate security as 1 + k and on the credit risk – free Treasury security as 1 + i, the FI manager would just be indifferent between corporate and Treasury security when:

p (1 + k) = 1 + i

The FI manager would set the expected return on the loan to equal the risk – free rate in the following manner:

y (1 + k) ´ (1 – p) + p (1 + k) = 1 + i

The new term here is y (1 + k) ´ (1 – p) this is the payoff the FI expects to get if the borrower defaults.

As might be expected, if the loan has collateral backing such that y>0, the required risk premium on the loan would be less for any given default risk probability (1 – p). Collateral requirements are a method of controlling default risk, they act as a direct substitute for risk premiums is setting required loan rates. To see this solve for the risk premium F and between k and i.

k – i = F = (1 + i) / (y + p – py) – (1 + i)

Probability of Default on a Multi period Debt Instrument

In this analysis to derive the credit risk or default probabilities occurring in the market for longer – term loans or bonds. For the simple one – period loan or bond, the probability of default (1 – p) was,

1 – p = 1 – [1 + i / 1 + k ]

Suppose that the FI managers wanted to find out the probability of default on a two – year bond. To do this, the manager must estimate the probability that the bond would default in the second year conditional on the probability that it does not default in the first year. The probability that a bond would default in any one year is clearly conditional on the fact that the default hasn’t occurred earlier.

Managerial Default Probability

The probability that a borrower will default in any given year. The probability that a bond would default in any one year is marginal default probability for that year.

Cumulative Default Probability

The probability that a borrower will default over a specific multiyear period. Yield curves are rising for both Treasury issues and corporate issues. We want to extract from these yield curves the market’s expectations of the multi period default rates for corporate borrowers classified in the grade B rating class.

No Arbitrage and Forward Rate

The condition of no arbitrage by investors requires that the return on buying and holding the two – year Treasury discount bond to maturity should just equal the expected return from investing in the current one – year discount T – bonds at the end of the first year at the expected one – year forward rate.

(1 + i2)² = (1 + i?) (1 + f?)

Mortality Rate Models:

Financial Institutions managers analyze the historic or past default risk experience, the mortality rates, of bonds and loans of a similar quality.

If p1 is the probability of a grade B bond or loan surviving the first year of its issue; thus 1- p1 is the marginal mortality rate or the probability of the bond or loan dying or defaulting in the first year of the issue.

If p2 is the probability of a grade B bond or loan surviving the first year of its issue; thus 1- p2 is the marginal mortality rate or the probability of the bond of loan dying or defaulting in the second year of the issue.

Thus, for each grade of corporate borrower quality, a marginal mortality rate (MMR) curve can show the historical default rate experience of bond sin any specific quality class in each year after issue on the bond or loan.

The above figure shows that as grade B bonds age, their probability of dying in each successive year increases. In reality, any shape to the mortality curve is possible. It is possible that MMRs can be flat, decline over time, or show a more complex functional form. These marginal mortality rates can be estimated from actual data on bond and loan defaults. Specially, for grade B quality bonds.

Marginal Mortality Rates:

MMR1 = (Value Grade B default in year 1)

(Value Grade B outstanding in year 1)

MMR2 = (Value Grade B default in year 2)

(Value Grade B outstanding in year 2)

Cumulative Mortality Rate

The probability of a bond or loan dying (defaulting) over a given multiyear period.

Limitations of the Mortality Model:

  • It produces historic or backward looking measures.
  • Future default probabilities ten to be highly sensitive to the period over which the FI managers calculate the MMRs.
  • The estimates tend to be sensitive to the number of issues and the relative size of issues in each investment grade.

RAROC Models

A popular model to evaluate credit risk based on market data is the RAROC model. RAROC- risk adjusted return on capital- was pioneered by Bankers Trust and has now been adopted by virtually all the large banks.

Under the RAROC model, the actual or promised annual cash flow on a loan (such as net interest and fees) is compared with the loan’s risk.

RAROC = one-year income on loan

Loan (asset) risk or Risk capital (?L)

A loan is approved only if sufficiently high relative to a benchmark cost of capital for the bank. Alternatively, if the RAROC on an existing loan falls below a bank’s RAROC benchmark, the lending officer should seek to adjust the loan’s terms to make it profitable.

?L= -DL X LX (?R/1+R)

?L = Dollar capital risk exposure or loss amount

-DL = Duration of the loan

L = Risk amount or loan size

?R = is an estimate of the worst change in credit risk premiums for

the loan class over the past year.

The ?R in RAROC equation equals:

?R= Max. [? (Ri – RG) > O]

Where, ? (Ri – RG) is the change in the yield spread between corporate bonds of credit rating class i (Ri) and matched duration treasury bonds (RG) over the last year.

In order to consider only the worst-case scenario, the maximum change in yield spread in chosen, as opposed to the average change.

Option Models

Employ option-pricing methods to evaluate the option to default. Used by many of the largest banks to monitor credit risk. KMV Corporation markets this model quite widely.

Theoretical Framework:

Following the pioneering work of Merton, Black and Scholes an others, it is now recognized that firms, which raise funds either by issuing bonds or increasing its bank loans, hold a very valuable default or repayment option.

That is, if a borrower’s investment projects fail, it has the option of defaulting on its debt repayment and turning any remaining assets over to the debt holder.

On the other hand, if things go well, the borrower can keep most of the upside returns on asset investments after the promised principal and interest on the debt have been paid.

Applying Option Valuation Model

Merton has shown that in the context of the preceding options framework, it is quite straightforward to express the marker value of a risky loan made by a lender to a borrower.

Theoretically, this model is an elegant tool for extracting premiums and default probabilities; it also had important conceptual implications regarding which variables to focus on in credit risk evaluation.


  • The assumption- continuously traded claim on the assets of the borrower- is difficult to accept in many cases.
  • The value of option-based premiums is extremely sensitive to errors made in measuring.
  • Lending Risk Analysis – UCBL

The credit quality of many Financial Institutions’ (FI’s) lending and investment decisions is always a great deal of attention. In the 1980s, there were tremendous problems with bank loans to less developed countries as well as thrift and bank residential and farm mortgage loans. Many banks as well as financial institutions in our country also suffer from this credit quality problem. Basically this type of problems would be faced by the financial organizations if they failed to measure the credit risk of the customers accurately. Its natural all banks and FI would use a standard format of performence evaluation of the customers for their loan screening process. But inspite of this, the planning, timing, decision making and application procedure might bring different result.

Our main target of this report is to evaluate the credit risk as well as lending risk analysis of a publicly owned financial institution. As mentioned earlier, in this regard we have selected UCBL (United Commercial Bank Limited) to evaluate their lending risk analysis so that we might get a practical dilemma from theoretical point of view.

Basically this chapter will contain the details procedure of lending risk analysis of UCBL. To make it understandable and clear the standard format, which is frequently used to measure and to assess the risk for different perspective, are attached here. Then its explanation and practical courses of actions have also been mentioned. It would help us to sketch the real condition of credit risk measurement of financial institutions.

Lending risk analysis form

Company name:


Industry name:


Group name (if company is a part of group) Organizing Branch/ office
Current exposure Nature of land Amount

To this customer

Current exposure (total) to this group

Taka ………………

Application for Increase to

New facility existing facility

Why this analysis conducted

Renewal of Delinquent

Existing facility customer

Risk category Good Acceptable Marginable Poor




Type of facility sought Amount
Purpose Type of facility recommended

Type Amount

Loan category

Voluntarily given

By the bank


Part of Government

Scheme …………………………

(Name of scheme)

Directed by

Individual ………………… ……………………………………………….

(Name of person directing the loan)

Level of approval required


Date customer made request

Date analysis


Originating officer Recommendation

Accept Decline Amount

Date recommendation made
Recommendation officer(s)
Approving officer Decision Accept Decline Amount Date decision made
Officer authorizing disbursement Date disbursement


Date loan


According to LRA form we find that at first it is mandatory to mention the customer’s (company’s) name, address, belonging industry, current exposure of land as well as fixed assets. Then we have to select the risk category of business and security. It shows a preliminary screening of the customer’s risk class. Loan category and level of requirement is also consistent here. Loan approving and originating officer has a great responsibility and commitment here. So it is also mentioned here with the disbursement date and recommendation. No doubt this form carries a great important for credit quality evaluation, subjective judgments be exercised cautiously to make it more accurate and qualitative one.

Lending risk

When you have completed pages 3 to 15 of the loan analysis form, copy your answers to the questions into the grid below. Then write the score corresponding to the answers in the rightmost column and total all the scores.

Lending Risk Risk level

Low Average High excursive



Supplies risk

What is the risk of failure due to description in the supply of inputs?


Score 1.5 5 4.5 12



Sales risk

What is the risk of failure due to description in the supply of inputs?


Score 1.5 5 4.5 12



Performance risk

What is the risk that the Company’s position is so weak that it cannot perform well enough to repay the loan, given expected external conditions?


Score 1.5 5 4.5 12




Resilience risk

What is the risk of failure due to lack of resilience to unexpected external conditions?


Score 1.5 5 4.5 12



Management competence risks What is the risk of failure due to lack of management competence?    

Score 1.5 5 4.5 12

Management Integrity risks What is the risk of failure due to lack of management Integrity?    

Score 1.5 5 4.5 12

Total business risk score 18
Security control risk What is the risk that the bank fails to realize the security?    

Score 1.5 5 4.5 12



Security cover risks What is the risk that the realized security value is less than the exposure?    

Score 1.5 5 4.5 12

Total security risk score 20
Good risk Acceptable Risk Marginable Risk Poor Risk
Business Risk 13.19 20.26


27.34 Over 34
1 1 1 1
Good risk Acceptable Risk Marginable Risk Poor Risk
Security (.201) (.15) (.14.0) .0 1-10 Over 10

Select Overall Risk from Matrix

1 2 3 4


After getting the preliminary information based on the lending risk analysis form, it is needed to evaluate risks in specific sector. Business risk is very much important here. It includes industry risk as well as company risk. While analyzing industry risk, supplier’s risk is considered as the risk of failure due to disruption in the supply of inputs. The risk of failure due to disruption to sales is also an important factor.

Company risk includes company position risk and management risk. Under company risk performance evaluation covers mostly positioning the company whether it is so weak that it cannot perform well enough to repay the loan, given expected external conditions. The risk of failure due to lack of resilience to unexpected condition should also be considered. While evaluating management risk, management compensation risk and management integrity risk is determined on subjective judgments.

Security control risk and security cover risk would be faced when the bank fails to realize the security. After analyzing these factors, scoring is done to get a complete figure of risk assessment

According to our hypothetical analysis we found that total business risk score is 18, total security risk score is 20. Lastly the overall risk from matrix shows marginal position of the customer.

Supplies risk

Cost item % Of total costs What is the risk of disruption?

Better than Average Worse


Raw materials


Number of days production lost is past 12 months due to strikes? ………………….
Independent power supply Dependent power supply

Power supply

Explain any significant risks of disruption to production
Low Average High Excessive

What is the risk of failure due to disruption in the

Supply of input?

The risk from supplier’s point of view is a very important factor in case of lending risk analysis. Suppliers risk means the risk of failure due to disruption in the supply of inputs. These inputs include raw materials, equipment power, premises and others. What is the contribution of these items in total cost i.e. the percentage of total cost? Then risk measurement is ranked in any of three categories consisting of better than average, average and worse than average. Then the comments on these ranking fulfils the risk assessment in this regard.

Sales risk

Industry growth

Give industry size figures for the latest 3 years that are available.

Estimated total industry turnover

Strong weak no small large

growth growth change decline decline

Over the next few years, what is the most likely trend in industry turnover?

Support your answer:

Competitive pressure: Obtain performance data for two major competitors

Major competitor I …………………………… Market share. ………………%


Performance Year

Less about the faster

Fast same

This competitor is growing than our customer.

What prevents customers from switching to this competitor?

Major competitor II ……………………………… Market share. ………………%


Performance Year

Less about the faster

Fast same

This competitor is growing than our customer.

What prevents customers from switching to this competitor?

Sales are the main earning generating power of a company. Not only as the main source of cash generation but also as an important factor for customer screening process sales risk must be measured. So sales risk measurement is the most crucial part of lending risk analysis.

Sales risk (continued)

Barriers to entry

Difficult Average Easy

How easy is it for new competitors to enter this industry?

What barriers prevent new competitors from entering this industry?

Regulatory changes

Low Average High

What is the risk that changes in regulations will damage sales?

Explain your answer.