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Credit Risk Management in Banking

Info: 14933 words (60 pages) Dissertation
Published: 9th Nov 2021

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Tagged: FinanceBanking


The future of banking will undoubtedly rest on risk management dynamics. Only those banks that have efficient risk management system will survive in the market in the long run. The major cause of serious banking problems over the years continues to be directly related to lax credit standards for borrowers and counterparties, poor portfolio risk management, or a lack of attention to deterioration in the credit standing of a bank’s counterparties.

Credit risk is the oldest and biggest risk that bank, by virtue of its very nature of business, inherits. This has however, acquired a greater significance in the recent past for various reasons. There have been many traditional approaches to measure credit risk like logit, linear probability model but with passage of time new approaches have been developed like the Credit+, KMV Model.

Basel I Accord was introduced in 1988 to have a framework for regulatory capital for banks but the “one size fit all” approach led to a shift, to a new and comprehensive approach -Basel II which adopts a three pillar approach to risk management. Banks use a number of techniques to mitigate the credit risks to which they are exposed. RBI has prescribed adoption of comprehensive approach for the purpose of CRM which allows fuller offset of security of collateral against exposures by effectively reducing the exposure amount by the value ascribed to the collateral.

In this study, a leading nationalized bank is taken to study the steps taken by the bank to implement the Basel-II Accord and the entire framework developed for credit risk management. The bank under the study uses the credit scoring method to evaluate the credit risk involved in various loans/advances. The bank has set up special software to evaluate each case under various parameters and a monitoring system to continuously track each asset’s performance in accordance with the evaluation parameters.


1.1 Rationale

Credit Risk Management in today’s deregulated market is a big challenge. Increased market volatility has brought with it the need for smart analysis and specialized applications in managing credit risk. A well defined policy framework is needed to help the operating staff identify the risk-event, assign a probability to each, quantify the likely loss, assess the acceptability of the exposure, price the risk and monitor them right to the point where they are paid off.

Generally, Banks in India evaluate a proposal through the traditional tools of project financing, computing maximum permissible limits, assessing management capabilities and prescribing a ceiling for an industry exposure. As banks move in to a new high powered world of financial operations and trading, with new risks, the need is felt for more sophisticated and versatile instruments for risk assessment, monitoring and controlling risk exposures. It is, therefore, time that banks managements equip them fully to grapple with the demands of creating tools and systems capable of assessing, monitoring and controlling risk exposures in a more scientific manner.

According to an estimate, Credit Risk takes about 70% and 30% remaining is shared between the other two primary risks, namely Market risk (change in the market price and operational risk i.e., failure of internal controls, etc.). Quality borrowers (Tier-I borrowers) were able to access the capital market directly without going through the debt route. Hence, the credit route is now more open to lesser mortals (Tier-II borrowers). With margin levels going down, banks are unable to absorb the level of loan losses. Even in banks which regularly fine-tune credit policies and streamline credit processes, it is a real challenge for credit risk managers to correctly identify pockets of risk concentration, quantify extent of risk carried, identify opportunities for diversification and balance the risk-return trade-off in their credit portfolio. The management of banks should strive to embrace the notion of ‘uncertainty and risk’ in their balance sheet and instill the need for approaching credit administration from a ‘risk-perspective’ across the system by placing well drafted strategies in the hands of the operating staff with due material support for its successful implementation.

There is a need for Strategic approach to Credit Risk Management (CRM) in Indian Commercial Banks, particularly in view of;

(1) Higher NPAs level in comparison with global benchmark

(2) RBI’ s stipulation about dividend distribution by the banks

(3) Revised NPAs level and CAR norms

(4) New Basel Capital Accord (Basel -II) revolution


  • To understand the conceptual framework for credit risk
  • To understand credit risk under the Basel II Accord
  • To analyze the credit risk management practices in a Leading Nationalised Bank


Research Design: In order to have more comprehensive definition of the problem and to become familiar with the problems, an extensive literature survey was done to collect secondary data for the location of the various variables, probably contemporary issues and the clarity of concepts.

Data Collection Techniques: The data collection technique used is interviewing. Data has been collected from both primary and secondary sources.

Primary Data: is collected by making personal visits to the bank.

Secondary Data: The details have been collected from research papers, working papers, white papers published by various agencies like ICRA, FICCI, IBA etc; articles from the internet and various journals.


* Merton (1974) has applied options pricing model as a technology to evaluate the credit risk of enterprise, it has been drawn a lot of attention from western academic and business circles.Merton’s Model is the theoretical foundation of structural models. Merton’s model is not only based on a strict and comprehensive theory but also used market information stock price as an important variance toevaluate the credit risk.This makes credit risk to be a real-time monitored at a much higher frequency.This advantage has made it widely applied by the academic and business circle for a long time.

Other Structural Models try to refine the original Merton Framework by removing one or more of unrealistic assumptions.

* Black and Cox (1976) postulate that defaults occur as soon as firm’s asset value falls below a certain threshold. In contrast to the Merton approach, default can occur at any time. The paper by Black and Cox (1976) is the first of the so-called First Passage Models (FPM). First passage models specify default as the first time the firm’s asset value hits a lower barrier, allowing default to take place at any time. When the default barrier is exogenously fixed, as in Black and Cox (1976) and Longstaff and Schwartz (1995), it acts as a safety covenant to protect bondholders. Black and Cox introduce the possibility of more complex capital structures, with subordinated debt.

* Geske (1977) introduces interest-paying debt to the Merton model.

* Vasicek (1984) introduces the distinction between short and long term liabilities which now represents a distinctive feature of the KMV model.

Under these models, all the relevant credit risk elements, including default and recovery at default, are a function of the structural characteristics of the firm: asset levels, asset volatility (business risk) and leverage (financial risk).

* Kim, Ramaswamy and Sundaresan (1993) have suggested an alternative approach which still adopts the original Merton framework as far as the default process is concerned but, at the same time, removes one of the unrealistic assumptions of the Merton model; namely, that default can occur only at maturity of the debt when the firm’s assets are no longer sufficient to cover debt obligations. Instead, it is assumed that default may occur anytime between the issuance and maturity of the debt and that default is triggered when the value of the firm’s assets reaches a lower threshold level. In this model, the RR in the event of default is exogenous and independent from the firm’s asset value. It is generally defined as a fixed ratio of the outstanding debt value and is therefore independent from the PD.

The attempt to overcome the shortcomings of structural-form models gave rise to reduced-form models. Unlike structural-form models, reduced-form models do not condition default on the value of the firm, and parameters related to the firm’s value need not be estimated to implement them.

* Jarrow and Turnbull (1995) assumed that, at default, a bond would have a market value equal to an exogenously specified fraction of an otherwise equivalent default-free bond.

* Duffie and Singleton (1999) followed with a model that, when market value at default (i.e. RR) is exogenously specified, allows for closed-form solutions for the term-structure of credit spreads.

* Zhou (2001) attempt to combine the advantages of structural-form models – a clear economic mechanism behind the default process, and the ones of reduced-

form models – unpredictability of default. This model links RRs to the firm value at default so that the variation in RRs is endogenously generated and the correlation between RRs and credit ratings reported first in Altman (1989) and Gupton, Gates and Carty (2000) is justified.

Lately portfolio view on credit losses has emerged by recognising that changes in credit quality tend to comove over the business cycle and that one can diversify part of the credit risk by a clever composition of the loan portfolio across regions, industries and countries. Thus in order to assess the credit risk of a loan portfolio, a bank must not only investigate the creditworthiness of its customers, but also identify the concentration risks and possible comovements of risk factors in the portfolio.

* CreditMetrics by Gupton et al (1997) was publicized in 1997 by JP Morgan. Its methodology is based on probability of moving from one credit quality to another within a given time horizon (credit migration analysis). The estimation of the portfolio Value-at-Risk due to Credit (Credit-VaR) through CreditMetrics A rating system with probabilities of migrating from one credit quality to another over a given time horizon (transition matrix) is the key component of the credit-VaR proposed by JP Morgan. The specified credit risk horizon is usually one year. A rating system with probabilities of migrating from one credit quality to another over a given time horizon (transition matrix) is the key component of the credit-VaR proposed by JP Morgan. The specified credit risk horizon is usually one year.

* (Sy, 2007), states that the primary cause of credit default is loan delinquency due to insufficient liquidity or cash flow to service debt obligations. In the case of unsecured loans, we assume delinquency is a necessary and sufficient condition. In the case of collateralized loans, delinquency is a necessary, but not sufficient condition, because the borrower may be able to refinance the loan from positive equity or net assets to prevent default. In general, for secured loans, both delinquency and insolvency are assumed necessary and sufficient for credit default.



Credit risk is risk due to uncertainty in a counterparty’s (also called an obligor’s or credit’s) ability to meet its obligations. Because there are many types of counterparties—from individuals to sovereign governments—and many different types of obligations—from auto loans to derivatives transactions—credit risk takes many forms. Institutions manage it in different ways.

Although credit losses naturally fluctuate over time and with economic conditions, there is (ceteris paribus) a statistically measured, long-run average loss level. The losses can be divided into two categories i.e. expected losses (EL) and unexpected losses (UL).

EL is based on three parameters:

  1. €  The likelihood that default will take place over a specified time horizon (probability of default or PD)
  2.  € The amount owned by the counterparty at the moment of default (exposure at default or EAD)
  3. €  The fraction of the exposure, net of any recoveries, which will be lost following a default event (loss given default or LGD).


EL can be aggregated at various different levels (e.g. individual loan or entire credit portfolio), although it is typically calculated at the transaction level; it is normally mentioned either as an absolute amount or as a percentage of transaction size. It is also both customer- and facility-specific, since two different loans to the same customer can have a very different EL due to differences in EAD and/or LGD.

It is important to note that EL (or, for that matter, credit quality) does not by itself constitute risk; if losses always equaled their expected levels, then there would be no uncertainty. Instead, EL should be viewed as an anticipated “cost of doing business” and should therefore be incorporated in loan pricing and ex ante provisioning. Credit risk, in fact, arises from variations in the actual loss levels, which give rise to the so-called unexpected loss (UL). Statistically speaking, UL is simply the standard deviation of EL.

UL= σ (EL) = σ (PD*EAD*LGD)

Once the bank- level credit loss distribution is constructed, credit economic capital is simply determined by the bank’s tolerance for credit risk, i.e. the bank needs to decide how much capital it wants to hold in order to avoid insolvency because of unexpected credit losses over the next year. A safer bank must have sufficient capital to withstand losses that are larger and rarer, i.e. they extend further out in the loss distribution tail. In practice, therefore, the choice of confidence interval in the loss distribution corresponds to the bank’s target credit rating (and related default probability) for its own debt. As Figure below shows, economic capital is the difference between EL and the selected confidence interval at the tail of the loss distribution; it is equal to a multiple K (often referred to as the capital multiplier) of the standard deviation of EL (i.e. UL).

The shape of the loss distribution can vary considerably depending on product type and borrower credit quality. For example, high quality (low PD) borrowers tend to have proportionally less EL per unit of capital charged, meaning that K is higher and the shape of their loss distribution is more skewed (and vice versa).

Credit risk may be in the following forms:

  • In case of the direct lending
  • In case of the guarantees and the letter of the credit
  • In case of the treasury operations
  • In case of the securities trading businesses
  • In case of the cross border exposure

2.2 The need for Credit Risk Rating

The need for Credit Risk Rating has arisen due to the following:

1. With dismantling of State control, deregulation, globalisation and allowing things to shape on the basis of market conditions, Indian Industry and Indian Banking face new risks and challenges. Competition results in the survival of the fittest. It is therefore necessary to identify these risks, measure them, monitor and control them.

2. It provides a basis for Credit Risk Pricing i.e. fixation of rate of interest on lending to different borrowers based on their credit risk rating thereby balancing Risk & Reward for the Bank.

3. The Basel Accord and consequent Reserve Bank of India guidelines requires that the level of capital required to be maintained by the Bank will be in proportion to the risk of the loan in Bank’s Books for measurement of which proper Credit Risk Rating system is necessary.

4. The credit risk rating can be a Risk Management tool for prospecting fresh borrowers in addition to monitoring the weaker parameters and taking remedial action.

The types of Risks Captured in the Bank’s Credit Risk Rating Model

The Credit Risk Rating Model provides a framework to evaluate the risk emanating from following main risk categorizes/risk areas:

  • Industry risk
  • Business risk
  • Financial risk
  • Management risk
  • Facility risk
  • Project risk


In recent years, a revolution is brewing in risk as it is both managed and measured. There are seven reasons as to why certain surge in interest:

1. Structural increase in bankruptcies

Although the most recent recession hit at different time in different countries, most statistics show a significant increase in bankruptcies, compared to prior recession. To the extent that there has been a permanent or structural increase in bankruptcies worldwide- due to increase in the global competition- accurate credit analysis become even more important today than in past.

2. Disintermediation

As capital markets have expanded and become accessible to small and mid sized firms, the firms or borrowers “left behind” to raise funds from banks and other traditional financial institutions (FIs) are likely to be smaller and to have weaker credit ratings. Capital market growth has produced “a winner’s” curse effect on the portfolios of traditional FIs.

3. More Competitive Margins

Almost paradoxically, despite the decline in the average quality of loans, interest margins or spreads, especially in wholesale loan markets have become very thin. In short, the risk-return trade off from lending has gotten worse. A number of reasons can be cited, but an important factor has been the enhanced competition for low quality borrowers especially from finance companies, much of whose lending activity has been concentrated at the higher risk/lower quality end of the market.

4. Declining and Volatile Values of Collateral

Concurrent with the recent Asian and Russian debt crisis in well developed countries such as Switzerland and Japan have shown that property and real assets value are very hard to predict, and to realize through liquidation. The weaker (and more uncertain) collateral values are, the riskier the lending is likely to be. Indeed the current concerns about deflation worldwide have been accentuated the concerns about the value of real assets such as property and other physical assets.

5. The Growth Of Off- Balance Sheet Derivatives

In many of the very large U.S. banks, the notional value of the off-balance-sheet exposure to instruments such as over-the-counter (OTC) swaps and forwards is more than 10 times the size of their loan books. Indeed the growth in credit risk off the balance sheet was one of the main reasons for the introduction, by the Bank for International Settlements (BIS), of risk based capital requirements in 1993. Under the BIS system, the banks have to hold a capital requirement based on the mark- to- market current values of each OTC Derivative contract plus an add on for potential future exposure.

6. Technology

Advances in computer systems and related advances in information technology have given banks and FIs the opportunity to test high powered modeling techniques. A survey conducted by International Swaps and Derivatives Association and the Institute of International Finance in 2000 found that survey participants (consisting of 25 commercial banks from 10 countries, with varying size and specialties) used commercial and internal databases to assess the credit risk on rated and unrated commercial, retail and mortgage loans.

7. The BIS Risk-Based Capital Requirements

Despite the importance of above six reasons, probably the greatest incentive for banks to develop new credit risk models has been dissatisfaction with the BIS and central banks’ post-1992 imposition of capital requirements on loans. The current BIS approach has been described as a ‘one size fits all’ policy, irrespective of the size of loan, its maturity, and most importantly, the credit quality of the borrowing party. Much of the current interest in fine tuning credit risk measurement models has been fueled by the proposed BIS New Capital Accord (or so Called BIS II) which would more closely link capital charges to the credit risk exposure to retail, commercial, sovereign and interbank credits.

Chapter 3: Credit Risk Approaches and Pricing



Credit Scoring Models use data on observed borrower characteristics 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, a bank manager may be able to numerically establish which factors are important in explaining default risk, evaluate the relative degree or importance of these factors, improve the pricing of default risk, be better able to screen out bad loan applicants and be in a better position to calculate any reserve needed to meet expected future loan losses.

To employ credit scoring model in this manner, the manager must identify objective economic and financial measures of risk for any particular class of borrower. For consumer debt, the objective characteristics in a credit -scoring model might include income, assets, age occupation and location. For corporate debt, financial ratios such as debt-equity ratio are usually key factors. After data are identified, a statistical technique quantifies or scores the default risk probability or default risk classification.

Credit scoring models include three broad types: (1) linear probability models, (2) logit model and (3) linear discriminant model.


The linear probability model uses past data, such as accounting ratios, as inputs into a model to explain repayment experience on old loans. The relative importance of the factors used in explaining the past repayment performance then forecasts repayment probabilities on new loans; that is can be used for assessing the probability of repayment.

Briefly we divide old loans (i) into two observational groups; those that defaulted (Zi = 1) and those that did not default (Zi = 0). Then we relate these observations by linear regression to s set of j casual variables (Xij) that reflects quantative information about the ith borrower, such as leverage or earnings. We estimate the model by linear regression of:

Zi = ΣβjXij + error

Where βj is the estimated importance of the jth variable in explaining past repayment experience. If we then take these estimated βjs and multiply them by the observed Xij for a prospective borrower, we can derive an expected value of Zi for the probability of repayment on the loan.


The objective of the typical credit – or loan review model is to replicate judgments made by loan officers, credit managers or bank examiners. If an accurate model could be developed, then it could be used as a tool for reviewing and classifying future credit risks. Chesser (1974) developed a model to predict noncompliance with the customer’s original loan arrangement, where non-compliance is defined to include not only default but any workout that may have been arranged resulting in a settlement of the loan less favorable to the tender than the original agreement.

Chesser’s model, which was based on a technique called logit analysis, consisted of the following six variables.

X1 = (Cash + Marketable Securities)/Total Assets

X2 = Net Sales/(Cash + Marketable Securities)

X3 = EBIT/Total Assets

X4 = Total Debt/Total Assets

X5 = Total Assets/ Net Worth

X6 = Working Capital/Net Sales

The estimated coefficients, including an intercept term, are

Y = -2.0434 -5.24X1 + 0.0053X2 – 6.6507X3 + 4.4009X4 – 0.0791X5 – 0.1020X6

Chesser’s classification rule for above equation is If P> 50, assign to the non compliance group and If P≤50, assign to the compliance group.


While linear probability and logit models project a value foe the expected probability of default if a loan is made, discriminant models divide borrowers into high or default risk classes contingent on their observed characteristic (X).

Altman’s Z-score model is an application of multivariate Discriminant analysis in credit risk modeling. Financial ratios measuring probability, liquidity and solvency appeared to have significant discriminating power to separate the firm that fails to service its debt from the firms that do not. These ratios are weighted to produce a measure (credit risk score) that can be used as a metric to differentiate the bad firms from the set of good ones.

Discriminant analysis is a multivariate statistical technique that analyzes a set of variables in order to differentiate two or more groups by minimizing the within-group variance and maximizing the between group variance simultaneously. Variables taken were:

X1::Working Capital/ Total Asset

X2: Retained Earning/ Total Asset

X3: Earning before interest and taxes/ Total Asset

X4: Market value of equity/ Book value of total Liabilities

X5: Sales/Total Asset

The original Z-score model was revised and modified several times in order to find the scoring model more specific to a particular class of firm. These resulted in the private firm’s Z-score model, non manufacturers’ Z-score model and Emerging Market Scoring (EMS) model.

3.2 New Approaches


One market based method of assessing credit risk exposure and default probabilities is to analyze the risk premium inherent in the current structure of yields on corporate debt or loans to similar risk-rated borrowers. Rating agencies categorize corporate bond issuers into at least seven major classes according to perceived credit quality. The first four ratings – AAA, AA, A and BBB – indicate investment quality borrowers.


Rather than extracting expected default rates from the current term structure of interest rates, the FI manager may analyze the historic or past default experience the mortality rates, of bonds and loans of a similar quality. Here p1is the probability of a grade B bond 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 while p2 is the probability of the loan surviving in the second year and that it has not defaulted in the first year, 1-p2 is the marginal mortality rate for the second year. Thus, for each grade of corporate buyer quality, a marginal mortality rate (MMR) curve can show the historical default rate in any specific quality class in each year after issue.


Based on a banks risk-bearing capacity and its risk strategy, it is thus necessary — bearing in mind the banks strategic orientation — to find a method for the efficient allocation of capital to the banks individual siness areas, i.e. to define indicators that are suitable for balancing risk and return in a sensible manner. Indicators fulfilling this requirement are often referred to as risk adjusted performance measures (RAPM).

RARORAC (risk adjusted return on risk adjusted capital, usually abbreviated as the most commonly found forms are RORAC (return on risk adjusted capital),

Net income is taken to mean income minus refinancing cost, operating cost, and expected losses. It should now be the banks goal to maximize a RAPM indicator for the bank as a whole, e.g. RORAC, taking into account the correlation between individual transactions. Certain constraints such as volume restrictions due to a potential lack of liquidity and the maintenance of solvency based on economic and regulatory capital have to be observed in reaching this goal. From an organizational point of view, value and risk management should therefore be linked as closely as possible at all organizational levels.


KMV Corporation has developed a credit risk model that uses information on the stock prices and the capital structure of the firm to estimate its default probability. The starting point of the model is the proposition that a firm will default only if its asset value falls below a certain level, which is function of its liability. It estimates the asset value of the firm and its asset volatility from the market value of equity and the debt structure in the option theoretic framework. The resultant probability is called Expected default Frequency (EDF). In summary, EDF is calculated in the following three steps:

i) Estimation of asset value and volatility from the equity value and volatility of equity return.

ii) Calculation of distance from default

iii) Calculation of expected default frequency


It provides a method for estimating the distribution of the value of the assets n a portfolio subject to change in the credit quality of individual borrower. A portfolio consists of different stand-alone assets, defined by a stream of future cash flows. Each asset has a distribution over the possible range of future rating class. Starting from its initial rating, an asset may end up in ay one of the possible rating categories. Each rating category has a different credit spread, which will be used to discount the future cash flows. Moreover, the assets are correlated among themselves depending on the industry they belong to. It is assumed that the asset returns are normally distributed and change in the asset returns causes the change in the rating category in future. Finally, the simulation technique is used to estimate the value distribution of the assets. A number of scenario are generated from a multivariate normal distribution, which is defined by the appropriate credit spread, the future value of asset is estimated.


CreditRisk+, introduced by Credit Suisse Financial Products (CSFP), is a model of default risk. Each asset has only two possible end-of-period states: default and non-default. In the event of default, the lender recovers a fixed proportion of the total expense. The default rate is considered as a continuous random variable. It does not try to estimate default correlation directly. Here, the default correlation is assumed to be determined by a set of risk factors. Conditional on these risk factors, default of each obligator follows a Bernoulli distribution. To get unconditional probability generating function for the number of defaults, it assumes that the risk factors are independently gamma distributed random variables. The final step in Creditrisk+ is to obtain the probability generating function for losses. Conditional on the number of default events, the losses are entirely determined by the exposure and recovery rate. Thus, the distribution of asset can be estimated from the following input data:

i) Exposure of individual asset

ii) Expected default rate

iii) Default ate volatilities

iv) Recovery rate given default


Pricing of the credit is essential for the survival of enterprises relying on credit assets, because the benefits derived from extending credit should surpass the cost.

With the introduction of capital adequacy norms, the credit risk is linked to the capital-minimum 8% capital adequacy. Consequently, higher capital is required to be deployed if more credit risks are underwritten. The decision (a) whether to maximize the returns on possible credit assets with the existing capital or (b) raise more capital to do more business invariably depends upon pricing. Pricing must commensurate with the risks. It is essential for the banks and financial institutions to ensure that the credit risks are not only thoroughly analyzed and mitigated, but also priced adequately. Following are the major sources of income which determine the pricing.

  • Interest rate
  • Commission
  • Fees


Normally a financial institution, while pricing the credit, follows a model more or less similar to:

= cost of funds+ overheads (salaries) + credit risk premium+ profit margin

Cost of funds may include the interest on deposits. The overheads include expenses on stationery, electricity etc and the salaries include of both front end and back end department. Next is the credit risk premium. Various grades have different probabilities of default. The higher the credit risk the higher the premium to be loaded into the pricing. One rough method is to factor in the default probability as the credit risk premium.


RORAC also known as RORA, RAROC or ROCAR attempts to link the returns to the underlying risks involved. The basic principle of RORAC is that not all the assets are equally risky. First the assets are converted on the basis of the risk involved. Then the return on risk assets is calculated by using the following formula:

RORAC= net profit before tax/ Risk assets

The relevance of the RORAC method lies in the fact that all business organizations attempt to maximize the return to shareholders, but the risks taken should be reflected in the returns. This means that as far as possible a business should go in for acceptable levels of credit risk, which provides maximum return on capital. Since the credit risk of each asset is different and enjoys different credit grades ROA is modified by weighting it with different credit weights depending upon credit risk involved. While ROA is measures the net return on asset base, RORAC measures the net return differentiating the assets based on risk involved. The most critical part is the accurate measurement of the risk involved in an asset.


The credit institution is highly influenced by the pricing of competitors. Usually the suppliers of credit offers homogenous products and the competition do exert pressure on pricing. The market should be respected in negotiating credit and loans in an increasingly competitive credit/loan market. Most participants in the large corporate market focus primarily on the credit spread and fees offered and less on subtle structural features. Most of the creditworthy customers usually invite price quotations from different credit providers before arriving at final decision.

Another technique is to follow a broad band pricing with the competitors. In this case the pricing need not be exactly the same as that of the competitors, but fixed taking into count the factors. However the price charged won't be completely out of the context with the market and an inbuilt flexibility will be provided so that the price does reflect the market by linking to market benchmark.


It is generally accepted that certain investments are relatively risk free while other are riskier. Looking from the investors point of view the shareholder seek better return fir the risks they are taking and would attempt to measure it against some benchmark. Another is the cost of capital, which is the minimum return expected by the shareholders. In simple terms-

Economic profit= normal earnings attributable to shareholders- Cost of capital.

Either one rate of cost of capital can be applied to all credit assets of the enterprise or the costs of capital can be differentiated across portfolios.


The credit production costs should be recovered so that the entity survives. The basic pricing model takes into account all the costs and adds up mark-up for profit. The attractiveness of this kind of pricing is that is simple. It ensures that the costs will be recovered from pricing, provided unduly large credit losses do not occur, which is a function of proper credit risk analysis. Cost plus pattern is very evident in the pricing of private sector banks.


In this category the same debtor will be charged differing pricing depending upon the credit. Each credit structure is priced differently.


While structured pricing is applied to different facilities granted to a customer, grid pricing is with reference to the same facility. But pricing is dissimilar because of perceived difference in credit risk. Usually the grid is attached to compliance with certain conditions or covenants.


A credit contract is profitable as long as it provides a return in excess of the minimum required rate of return. The credit inflows are discounted at a specified rate- usually market rate/risk free rate, adjusted to the underlying credit risk- to arrive at an appropriate NPV. Two decisions taken under this category are given below.

a. Pricing of credit

b. Choosing among various credit exposures.

Chapter 4: Basel I and Basel II Accord


Before 1988, many central banks allowed different definitions of capital in order to make their country's bank appear as solid than they actually were. As a result the definition of capital began to diverge more and more. In order to provide a level playing field the concept of regulatory capital was standardized in the first BASEL CAPITAL ACCORD or BASEL 1. Along with definition of regulatory capital a basic formula for capital divided by assets was constructed and an arbitrary ratio of 8% was chosen as minimum capital adequacy.

Different risk weights were assigned for specified categories of exposure. For example, Government securities carried zero risk weight while for corporate exposures, it was 100 percent. The norms were very simple and rudimentary but a good beginning was made. In India, Reserve Bank of India stipulated a minimum capital adequacy of 9 percent and it goes to the credit of both the central bank and the Indian banking fraternity that the regulatory capital requirement was met by the banking system.

However, there were drawbacks in the BASEL 1 as it did not did not discriminate between different level of risk. It stipulated a single rate of capital adequacy for credit risk irrespective of the degree of risk within that category. As a result, there was no incentive in respect of capital for a high quality credit portfolio. It also did not adequately address risks involved in increasing use of financial innovations like securitisation of assets and derivatives and the credit risk inherent in these developments. Attention was also not given to recognize operational risk.


BASEL II proposal have sought to rectify the defects of the old accord. To accomplish this BASEL 2 proposes getting rid of the old risk weighted categories that treated all corporate borrowers the same replacing them with limited number of categories into which borrowers would be assigned based on assigned credit system. Basel II has made a more comprehensive approach to manage risks for the banking system.

Basel II adopts a three pillar approach to risk management.

Under Pillar 1 minimum capital requirements are stipulated for credit risk, market risk and operational risk.

Pillar 2 deals with supervisory review process by the central bank.

Pillar 3 underlines the need for market discipline and disclosures required there under.


The First Pillar sets out the minimum capital requirements. The definition of Total Capital remains unchanged, however revision focuses on measurement of risk i.e denominator of the capital ratio. The Credit Risk measurement methods are more elaborate than in the old accord. The New Accord also proposes a measure for Operational Risk, while the measure of Market Risk remains the same.


With Pillar II, the concept of “economic capital” is introduced into the regulatory capital equation, thus enabling the determination of capital adequacy based on the level of risk posed by a transaction. The Four Principals of Pillar II are:

Principle 1: Banks should have a process for assessing their overall capital adequacy in relation to their risk profile and a strategy for maintaining their capital levels.

Principle 2: Supervisors should review and evaluate banks' internal capital adequacy assessments and strategies, as well as their ability to monitor and ensure their compliance with regulatory capital ratios.

Principle 3: Supervisors should expect banks to operate above the minimum regulatory capital ratios and should have the ability to require banks to hold capital in excess of the minimum.

Principle 4: Supervisors should seek to intervene at an early stage to prevent capital from falling below the minimum levels required to support the risk characteristics of a particular bank and should require rapid remedial action if capital is not maintained or restored


Disclosure requirements are stipulated for banks to encourage market discipline. This helps the market participants to assess the information on capital, risk exposures, risk assessment processes and capital adequacy of the bank. Market discipline supplements regulation as sharing of information facilitates assessment of the bank by others including investors, analysts, customers, other banks and rating agencies. It also leads to good corporate governance. Supervisors can stipulate the minimum disclosures to be made by banks

Examples of proposed level of disclosure are:

  • Data included in disclosure information must be traceable to its original source
  • Systems and processes required to verify data must be of a high standard
  • Credit exposures before and after credit mitigation
  • Credit risk management framework etc.

4.3 Risk weight frame work (Basel I Vs New Framework)

A range of risk weights is to be applied based on the credit rating of the external agencies. A broad comparison of risk weights applicable under the New Capital Adequacy Framework is as under:

Claims on


Basel I

New Framework

Domestic Sovereigns



Foreign Sovereign


20% - 150%


Scheduled Banks

Foreign Banks

Other Banks





20% - 150%




20% - 150%

Retail Portfolio*



Secured by mortgages on Residential Property




Provisions < 20% of Outstanding

Provisions > or = 20% of Outstanding

Provisions > or = 50% of Outstanding







High risk categories (Categories under New framework to be indicated by RBI) $



4.4 Approaches to Measure Credit Risk

The two approaches to measure credit risk are standardized approach (SA) and Internal rating based approach (IRB).

Standardized Approach require banks to use a risk-weighting schedule for measuring the credit risk of banks' assets by suitably classifying assets and assigning risk weights based on the evaluation by the external credit rating agencies taking into account other factors such as risk mitigants, instrument type and asset quality, among others.

The Internal Rating Based approach (IRB), on the other hand, allows banks to use their own internal ratings of counterparties and exposures, which permit a finer differentiation of risk for various exposures and hence delivers capital requirements that are better aligned to the degree of risks. The IRB approach hinges on a formula provided by Basel Committee which has four major variables, viz., probability of default (PD), loss given default (LGD), exposure at default (EAD) and effective maturity (M).

While PD of a borrower or group of borrowers is the central measureable concept on which the IRB approach is built, banks need to estimate LGD and EAD to arrive at a combined measure of expected intrinsic or economic loss.

The IRB approaches are of two types:

1. Foundation IRB (FIRB): the bank estimates the PD associated with each borrower, and the supervisor supplies other inputs such as LGD and EAD.

2. Advanced IRB (AIRB): in addition to PD, the bank adds other inputs such as EAD , LGD, and M. The requirements for this approach are more exacting. The adoption of advanced approaches would require the banks to meet minimum requirements relating to internal ratings at the outset and on an ongoing basis such as those relating to the design of the rating system, operations, controls, corporate governance, and estimation and validation of credit risk components, viz., PD for both FIRB and AIRB and LGD and EAD for AIRB. The banks should have, at the minimum, PD data for five years and LGD and EAD data for seven years. The manpower skills, the IT infrastructure and MIS at the banks would have to be upgraded substantially. The supervisors would need to develop skills in validation and back testing of models. In India, banks were advised to compute capital requirements for credit risk adopting the SA.

CHAPTER 5: Credit Risk Mitigation (CRM)

5.1 Introduction

Banks use a number of techniques to mitigate the credit risks to which they are exposed. For example, exposures may be collateralised in whole or in part by cash or securities, deposits from the same counterparty, guarantee of a third party, etc. The revised approach to credit risk mitigation allows a wider range of credit risk mitigants to be recognised for regulatory capital purposes. A collateralised transaction is one in which:

Banks have a credit exposure and that credit exposure is hedged in whole or in part by collateral posted by a counterparty or by a third party on behalf of the counterparty. Here counterparty is one on whom bank has an on or off balance sheet credit exposure.

Banks have a specific lien on the collateral and the requirements of legal certainty are met.

The revised approach to credit risk mitigation allows a wider range of credit risk mitigants to be recognized for regulatory capital purposes than is permitted under Basel I subject to meeting the requirements of legal certainty i.e documentation for all the credit risk mitigation used in collateralized transactions and the same must be binding on all parties and legally enforceable in all relevant jurisdictions. Banks must have conducted sufficient legal review to verify this and have a well founded legal basis to reach this conclusion and undertake such further review as necessary to ensure continuing enforceability.

RBI has prescribed adoption of comprehensive approach for the purpose of CRM which allows fuller offset of security of collateral against exposures by effectively reducing the exposure amount by the value ascribed to the collateral. Under this approach Banks which take eligible financial collateral (e.g. cash or securities as detailed below), are allowed to reduce their credit exposure to a counter party when calculating their capital requirements to take in to account the risk mitigating effect of the collateral. However, this is allowed on account-by-account basis even within the regulatory retail portfolio.

The Banks will need to calculate the exposure to which the risk weight is to be applied, after adjusting for the collateral value. In order to take into account the future variation in the value of the collateral (as the value of the collaterals can change from time to time) a discount is to be applied to the value of the collateral before it is adjusted against the outstanding in the account. Similarly, the exposure to the counter party is also required to be adjusted to take into account the future fluctuations in the exposure value, as in certain type of transactions the exposure value may change due to possible market movements. These adjustments are referred to as ‘Haircuts'. Additionally where the exposure and collateral are held in different currencies an additional downwards adjustment must be made to the volatility adjusted collateral amount to take into account the possible future fluctuations in exchange rates.

Further guarantees provided by a range of guarantors have also been recognized as credit risk mitigants and a substitution approach will be applied i.e. wherever the applicable risk weight to the guarantor based on his rating is lower than the applicable risk weight of the borrower, the risk weight of the borrower shall be substituted by the risk weight of the guarantor to the extent the exposure is guaranteed. For the balance the risk weight of the borrower shall be applied.

Where the exposure and collateral are held in different currencies and additional downwards adjustment must be made to the volatility adjusted collateral amount to take account of possible fluctuations in exchange rates.

Eligible financial collateral

The following collateral instruments are eligible for recognition in the comprehensive approach:

i) Cash (as well as certificates of deposit or comparable instruments, including fixed deposit receipts, issued by the lending bank) on deposit with the bank which is incurring the counterparty exposure.

ii) Gold: Gold would include both bullion and jewellery. However, the value of the collateralized jewellery should be arrived at after notionally converting these to 99.99 purity.

iii) Securities issued by Central and State Governments.

iv) Kisan Vikas Patra and National Savings Certificates provided no lock-in period is operational and if they can be encashed within the holding period.

v) Life insurance policies with a declared surrender value of an insurance company which is regulated by an insurance sector regulator.

vi) Debt securities rated by a chosen Credit Rating Agency in respect of which the banks should be sufficiently confident about the market liquidity where these are either;

a) Attracting 100% or lesser risk weight i.e. rated at least BBB (-) when issued by public sector entities and other entities (including banks and Primary Dealers); or

b) Attracting 100% or lesser risk weight i.e. rated at least PR3/P3/F3/A3 for short-term debt instruments.

vii) Debt securities not rated by a chosen Credit Rating Agency in respect of which the banks should be sufficiently confident about the market liquidity where these are:

a) issued by a bank; and

b) listed on a recognised exchange; and

c) classified as senior debt; and

d) all rated issues of the same seniority by the issuing bank are rated at least BBB (-) or PR3/P3/F3/A3 by a chosen Credit Rating Agency; and

e) the bank holding the securities as collateral has no information to suggest that the issue justifies a rating below BBB (-) or PR3/P3/F3/A3 (as applicable) and;

f) Banks should be sufficiently confident about the market liquidity of the security.

viii) Units of Mutual Funds regulated by the securities regulator of the jurisdiction of the bank's operation mutual funds where:

A price for the unit is publicly quoted daily i.e. where the daily NAV is available in public domain; and

Mutual fund is limited to investing in the instruments listed in this paragraph.

5.2 Haircuts

Haircuts in principle are Standard Supervisory Haircuts and Own-estimate haircuts. Banks in India use only the standard supervisory haircuts. The standard supervisory haircuts assuming daily mark to market, daily re-margining and a 10 business day holding period would be as follows:

Issue Rating for Debt Securities

Residual Maturity

( in years)


(in percentage)


Securities issued/guaranteed by the Govt. of India and issued by the State Governments (Sovereign securities)


Rating no applicable - as Government securities are not currently rated in India.

= 1 year


1 year and < or

= 5 years


> 5 years



Domestic debt securities other than those indicated at Item No. A above including the securities guaranteed by Indian State Governments




= 1 year


1 year and < or

= 5 years


> 5 years



A to BBB


PR3/P3/F3/A3 and

Unrated bank securities

= 1 year


>1 year and < or

= 5 years


>5 years



Units of Mutual Funds

Highest haircut applicable to any of the above securities, in which the eligible mutual fund


Cash in the same currency


The ratings indicated in above table represent the ratings assigned by the domestic rating agencies. In the case of exposures toward debt securities issued by foreign Central Governments and foreign corporate, the haircut may be based on ratings of the international rating agencies, as indicated below:

Issue rating for debt securities as assigned by international rating agencies

Residual Maturity


Other issues

AAA to AA/A-1

= 1 year



1 year and < or

= 5 years



> 5 years



A to BBB/

A-2/A-3/P-3 and

Unrated Bank Securities

= 1 year



1 year and < or

= 5 years



> 5 years



Other aspects related to haircut are as follows:

Sovereign will include RBI, DICGC and CGTSI.

Banks may apply a zero haircut for eligible collateral in respect of NSC, KVP, surrender value of insurance policies and bank's own deposits.

The standard supervisory haircut for currency risk where exposure and collateral are denominated in different currencies is 8%.

For transactions in which bank's exposure is unrated or bank lends non-eligible instruments, the haircut would be 25%.

5.3 Credit Mitigation Techniques

On-balance sheet netting

On-balance sheet netting is confined to loans/advances and deposits, where banks have legally enforceable netting arrangements, involving specific lien with proof of documentation. The haircuts in this case would be zero except when a currency mismatch exists.


Where guarantees are direct, explicit, irrevocable and unconditional banks may take account of such credit protection in calculating capital requirements. However, guarantees which carry lower risk weight than the counter party would only be used for calculation of capital charge. The protected portion of the counter party exposure is assigned the risk weight of the guarantor whereas the uncovered portion retains the risk weight of the underlying counterparty.

Range of eligible guarantors (counter-guarantors)

Credit protection given by the following entities would be recognised:

i)Sovereigns, sovereign entities (including BIS, IMF, European Central Bank and European Community as well as those MDBs, ECGC and CGTSI), banks and primary dealers with a lower risk weight than the counterparty.

ii) Other entities rated AA (-) or better. This would include guarantee cover provided by parent, subsidiary and affiliate companies when they have a lower risk weight than the obligor. The rating of the guarantor should be an entity rating which has factored in all the liabilities and commitments (including guarantees) of the entity.

The exposure protected by guarantee will attract risk weight applicable to the protection provider with the exception that exposure covered by State Govt. Guarantee will attract risk weight of 20%. The uncovered portion of the exposure will attract risk weight of applicable on the underlying counterparty.

The exposure amount after risk mitigation is calculated as give below:

E* = max {0, [E x (1 + He) - C x (1 - Hc - Hfx)]} where:

E*= the exposure value after risk mitigation

E = current value of the exposure

He= adjustment appropriate to the exposure

C= the current value of the collateral received

Hc= Discount (haircut) appropriate to the collateral

Hfx= Discount (haircut) appropriate for currency mismatch between the collateral and exposure

Following highlights impact of credit risk mitigation recognized under Basel II:

Basel I

Basel II




Adjustment (Haircut) for Exposure



Exposure after Haircut






Haircut (Discount)



Net Value of collateral after haircut



Net Exposure


Capital at 9%



CHAPTER 6: Credit Risk Management at PNB

Based on the Basel II accord, RBI indicated that all banks in India shall adopt “Standardized Approach of Credit Risk” w.e.f. 31.03.2007. To implement this, RBI prescribed that the Banks in India shall start parallel run of “Standardized Approach of Credit Risk” from 01.04.2006.

In terms of RBI guidelines, the bank has implemented standardized approach as prescribed as on 31.03.08. To evaluate the risk weighted assets for credit risk under Standardized Approach, bank has got upgraded the existing LADDER system called “Cris-Mac”, which shall generate the requisite reports.

The software was initially pilot tested in 10 branches. After successful pilot run, the bank decided to implement the software in entire bank through LADDER system w.e.f. 31.03.2006. For ensuring smooth implementation of the software, jointly with MIS Division, HO has conducted seminars for LADDER teams of all Zones/Regions on “Implementation of Standardized Approach on Credit Risk” in the month of May 2006. A CD containing software for implementation of Standardized Approach was sent to all the Zones and the process of data collection is in progress. The bank uses two tools for credit risk: ‘PNBTrac' and ‘PMS'.

As the implementation of new capital adequacy framework was dependent on all the branches/ offices of the Bank, circulars were issued containing appropriate guidelines. References of such circulars have been given at the appropriate place. Consolidated guidelines were issued through circulars for the benefit of the branches.

While migrating to Basel-II, the capital requirement shall be calculated as per Basel-I as well as Basel-II for 3 years i.e. March 2008, March 2009 and March 2010.

6.1 Credit Risk Management - Framework

The overall framework of credit risk management in the bank comprises of following building blocks:

1. Credit Risk Management Structure

2. Credit Risk Policy & Strategy

3. Processes and Systems


Under overall credit risk management framework, the bank has put in place the following structure:

1. Risk Management Division (RMD)

The Division is headed by CGM/GM with distinct functions related to credit risk, namely framing of policies, monitoring and managing industry risk and integrated risk management functions. But w.e.f 30.04.2008 these are called as Circle Risk Management Departments (CRMDs). The operational work is looked after by DGM/AGM/CM of the Circle Office who is not directly involved in the process of the sanction of credit proposal. Their responsibilities include monitoring and initiating steps to improve the quality of the credit portfolio of the Circle, tracking down the health of the borrowal accounts through regular risk rating, besides assisting the respective Credit Committee in addressing the issues on risk.

2. Risk Management Committee (RMC)

It is a Sub-Committee of Board with overall responsibility of formulating policies/procedures and managing all the risks. It adopts integrated approach in managing all the risks.

3. Credit Risk Management Committee (CRMC)

It is a top level functional Committee headed by CMD and comprises of EDs, CGMs/GMs of Credit, Treasury, Risk Management, etc. as per the directives from RBI. Its specific responsibilities are implementation of credit policy; monitoring credit risk on bank wide basis, devise delegation of credit approving powers, asset concentration, portfolio management, regulatory/legal compliance, etc.

4. Credit Committees

The bank has in place “Grid/Committee” system in credit sanction process. Consequent upon implementation of 3-tier structure where Circle Office headed by AGM/DGM/GM will be acting as a sole tier between the branch and HO, the scope of Credit Committee has been widened to cover the proposals falling under the vested loaning powers of AGMs working as Circle Heads. Accordingly, every loan proposal falling within the vested powers of Circle Head and above is discussed in a Credit Committee, which, on the merit of the case, recommends the proposal to the sanctioning authority. Such committees have been formed both at HO and Circle Office levels. The credit committee at HO includes CGM/GMs-Credit and CGM/GM-RMD. For credit proposals falling within the vested power of CGM/GM, the credit committee at HO includes DGM/AGM/Chief Manager-CAD and DGM/AGM/Chief Manager-RMD.

5. Credit Audit Review Division (CARD)

Bank has also set up a Loan Review/Audit Mechanism to be looked after independently by CARD.


A comprehensive credit risk management process encompasses the following steps:

A. Credit Risk Identification and Measurement

B. Grading of Borrowers under the Rating System

C. Reporting and analysis of Credit Risk

D. Portfolio Management

E. Use of Securities as Risk Mitigants

F. Use of Guarantees as Risk Mitigants

A. Credit Risk Identification and measurement

Credit risk management process involves identification, measurement, monitoring and control. The process of identification of credit risk is done by:

  • Identifying potentially good and weak industries to manage risk in portfolio through industry wise exposure ceiling model.
  • Identifying potential credit risk in a new as well as existing borrower through various credit risk rating models.
  • Identifying signals of weakness in an existing borrower through preventive monitoring system.
  • Identifying weak accounts having incipient sickness.

1. Industry Rating

Bank has subscribed to the ICRA industry ratings to be used in credit risk ratings. Industry Rating along with scenario provided helps the credit officials in appraising/sanctioning credit proposals and deciding on exposure. The industries not rated are to be treated as ‘BB' denoting neutral so far as applicability of loaning power restrictions is concerned.

2. Credit Risk Rating Models

To measure risk in individual borrowal accounts, the bank has identified various segments viz. large corporate, mid corporate, small, NBFC, New projects, Banks, Retail, etc. Borrowers in these segments reflect similarity of potential credit risk factors and as such can be rated using the single model for the segment. Parameter under these models captures potential credit risk both internal as well as external, which may affect the credit quality of a borrower. The credit risk rating awarded to a borrower is subject to review from time to time. All existing borrowers above a thresh hold limits are also subject to a continuous preventive monitoring system. This system helps in identifying accounts developing adverse signals to initiate corrective measures. The bank also has system of identifying and monitoring weak accounts which develop incipient sickness.

The bank has developed the following models/tools:

Credit Risk Rating Model

Total Limits


Large Corporate

Above Rs. 15 Crore (OR)

Above Rs.100 Crore, except Trading concerns

Mid Corporate

Above Rs.5 Cr and up to Rs.15 Cr. (OR)

Above Rs.25 Cr and up to Rs.100 Cr.

All trading concerns falling in the Large Corporate category shall also be rated under this model

Small Loans

Above Rs.50 lakh & up to Rs.5 Cr (AND)

Up to Rs.25 Cr.

Small Loans II

Above Rs.2 lakh & up to Rs.50 lakhs


All Non Banking Financial Companies irrespective of Limit

New Projects Rating model

Above Rs. 5 Cr. (OR)

Cost of Project above Rs.15 Cr.

Entrepreneur New Business Model

Borrower setting up new business and requiring finance above Rs 20 lac upto Rs. 5 Cr (AND)

Cost of Project upto Rs.15 Cr.

However, all new trading business irrespective of limits shall be rated under this model

Half Yearly Review of Rating

i) Listed companies.


ii) Availing limits (FB+NFB) above Rs.50.00 crores from our bank.

Facility Rating Framework

Assigning rating to facility sanctioned to the borrower based on default rating and securities available

Exposure to undertakings of State

All states within Union of India

Credit Risk Rating models for Banks/ FI

All banks and Financial Institutions

Exposure Ceiling Model for various industries

All industries where the bank has substantial exposure

Segment Rating Methodology

Pool wise default and ratings approach for loans upto Rs.50.00 lakhs and loan under exempted category of rating.

In order to provide a standard definition and benchmarks under the credit risk rating system, following matrix has been adopted in all the risk rating models.

Rating category




Minimum Risk

Excellent business credit, superior asset quality, excellent debt capacity and coverage.


Marginal Risk

Very good business credit, very good asset quality and liquidity, very good debt capacity and coverage.


Modest Risk

Good business credit, good asset quality and debt capacity & coverage.


Average Risk

Average business credit with satisfactory asset quality and liquidity, good debt capacity and coverage.


Marginally Acceptable Risk

Acceptable business credit with average risk, acceptable asset quality, modest debt capacity.


High Risk

Not creditworthy, generally acceptable asset quality.



Unacceptable business credit, normal repayment in jeopardy, inadequate projected net worth and paying capacity.

With a view to have better and wider differentiation among the borrowers within a rating categories of PNB-AA to PNB-B, a suffix of (-) is appended with the rating category for the borrowers in the first quartile and a suffix of (+) is appended with the rating category for the borrowers in the fourth quartile. In addition, the status of the Half Yearly Review of Large & Mid Corporate borrowers is quoted by a suffix of either “Outlook Negative”, “Out look Stable” or “Out look Positive” after the rating category. Ratings with AAA, AA, A, BB and B grades signify “Investment Grade” and C and D rating grades are called “High Risk Grade”.
C. Reporting and Analysis of Credit Risk

The MIS in respect of risk rated accounts under all the models (based on the amount of the limits) is to be made available to the Risk Management Division.


Sanctioning Authority

Credit Risk Rating Authority

Vetting/Confirming Authority


i) CRMD in consultation with branches

ii) Large Corporate Branches and identified branches


Circle Office


The assistance of CRMD/Functional Manager (Credit) of Circle Office may be taken in case of need.



Officer/Manager, Credit Section

An official designated by the Incumbent not connected with processing/recommending of the concerned loan proposal.

D. Portfolio Management

The rated portfolio of the loan accounts should be monitored by RMD periodically to ensure proper mix of various risk category accounts and thereby the asset quality of the portfolio. With this aim the bank has taken the following measures:

i)Evaluation of rating wise distribution of borrowers in various industries is done to assess/appraise the quality of bank's portfolio.

ii)Industry scenario analysis is being undertaken taking into consideration the changes in industrial and external environment e.g. changes in Economic/Fiscal/Monetary policies, general slowdown/boom in the economy etc.

iii)All limits are to be renewed/reviewed at least once in 12 months. In order to have constant monitoring of the portfolio of the bank in the lower rating categories of “C” & “D” the bank has the system in place of having discriminatory time schedules for review of Credit limits in these categories of accounts. The review of such accounts is required to be done every 6 months.

iv)The Bank has since appointed “Relationship Managers” in its Large Corporate branches with the aim of proper monitoring of bank's exposure in high value accounts so as to ensure constant surveillance on the substantial share of the loan portfolio of the bank which can alter its risk profile.

E. Collateral Management Policy

Bank has already placed comprehensive Collateral Management Policy, duly approved by the Board. Apart from primary security, bank takes collateral security in borrowal accounts to mitigate the risk. Detailed guidelines on creation of valid charge, valuation and verification of various types of collateral security are issued from time to time.

F. Use of Guarantees as Risk Mitigants

Appropriate acceptable guarantee is obtained as the risk mitigant in various borrowal accounts. The understanding of the field functionaries to use Guarantees as risk mitigant is also well established. Present, credit risk rating system evolved in the bank is aimed at measuring the Probability of Default of the borrower. To measure the impact of Credit Risk Mitigants, bank is implementing Facility Rating Model.


PNB TRAC is an internally developed centralized web based software application for assessment of credit risk in a borrowal account. It incorporates all rating models on a single platform and enables on line rating of borrowers. The data is stored in a centralised server, which makes the data collection and storage easier.

Preventive Monitoring System is put in place to track the changes in the account based on the the adverse signals observed in the operations of account and select performance parameters.

6.2.1. PNB TRAC

It stands for PNB Techniques for Risk Assessment of Credit. While some banks are installing risk management as part of a larger programme of moving towards a framework of “integrated risk management” others are revamping their existing risk management systems to install sophisticated software.

So far, Punjab National Bank is the only state-owned bank that has developed an in-house model for credit risk and patented it.

The parameters on which the request for loan by an organization is evaluated are as follows:

A. Financial Evaluation

1. Industry Comparison

1.1 Gross Sales Growth Rate (%)

1.2 OPBDIT/Sales (%)

1.3 Short term bank borrowings/ Net Sales (%)

1.4 Operating Cash Flow/ Total debt (%)

1.5 Net Operating cash flow/ Total debt (%)

2. Absolute Comparison

2.1 Debt equity ratio


2.3 Current ratio

2.4 Interest coverage ratio

2.5 DSCR

2.6 ROE (%)

3. Future Risk (Subjective)

3.1 Impact of Contingent Liability

3.2 Foreign Transaction risk

3.3Impact of merger/demerger

3.4 Cash flow adequacy

3.5 Impact of diversion on future financials

4. Subjective assessment of Financials

4.1 Transparency in accounting

4.2 Quality of inventory

4.3 Realisibilty of Debtors

4.4 Quality of investment

5. Trend Adjustment (Adjustment for financial trend)

B. Business Evaluation

1. Operating efficiency Evaluation

1.1 Operating leverage

1.2 Inventory Turnover

1.3 Net sales/Op. assets

1.4 Raw Material consumed/ net sales

1.5 Credit period allowed

2. Market Position evaluation (subjective)

2.1 Competitive position

2.2 Input related risk

2.3 Production related risk

2.4 Product related risk

2.5 Price Competitiveness

2.6 marketing

C. Management evaluation

1. Achievements

1.1 Actual vs. Targeted Sales

1.2 Actual vs. Targeted PBT

2. Subjective assessment of management

2.1 Set up

2.2 Commitment and sincerity

2.3 Track record in debt management

2.4 Capital Market perception

2.5 track record in execution of projects

2.6 Financial strengths/ flexibility/ group support

D. Conduct of account Evaluation

1. Preventive monitoring system rating

2. Status of account

3. Operations in account

4. Submission of financial data/ statements

After obtaining the scores for all the above mentioned parameters a composite score is calculated this is based on the weights assigned to respective categories.

The weights are

  • Financial Evaluation: 40%
  • Business & Industry Evaluation: 25%
  • Management Evaluation: 25%
  • Conduct Evaluation: 10%

The final rating so received depicts the risk associated with the organisation and depending on the risk category RWA is assigned to the loan.

Risk categories based on the score obtained

Score Obtained



Above 80


Minimum Risk

above 77.50 upto 80.00


above 72.50 upto 77.50


Marginal Risk

above 70.00 upto 72.50


above 67.50 upto 70.00


above 62.50 upto 67.50


Modest Risk

above 60.00 upto 62.50


above 57.50 upto 60.00


above 52.50 upto 57.50


Average Risk

above 50.00 upto 52.50


above 47.50 upto 50.00


above 42.50 upto 47.50


Marginally acceptable Risk

above 40.00 upto 42.50


above 30.00 upto 40.00


High risk

30.00 and below




1. The credit risk rating models for borrowal accounts availing aggregate limit of above Rs. 2 Lakh are already in use in Bank. On validation of credit risk rating models, it has been observed that the high risk in certain critical parameters, which may substantially affect the ability or willingness of the borrower to repay its dues as per terms of agreement, is not fully captured due to the limitations of the models. As such, borrower may get a better rating due to these limitations.

2. In order to address these limitations, it has been decided to introduce the following hurdle points:

2.1 Financial Evaluation:

The various parameters are evaluated to assess the ability of a borrower to meet its commitments on due dates.

Insufficient future cash flows of a borrower to meet its commitments coupled with high TOL/TNW ratio may be very risky and increase the Probability of Default (PD). Hence it has been decided that if:-

a)The total financial score (after subjective assessment and trend adjustment) is equal to or less than 40% and

b)Score under the parameter “Future cash flow adequacy” is ‘0' (i.e. future cash flow adequacy-company is likely to default)


c) Score under the parameter “TOL/TNW” is ‘0' (i.e. the ratio of TOL/TNW is >5)

Then the rating of the borrowal may be downgraded to ‘B'. If the co.'s rating is already ‘B' or ‘C' then it will be downgraded by one notch.

2.2 Management Evaluation:

The various parameters are evaluated to assess the management's competency and their willingness to pay its dues as per terms of agreement. If commitment & sincerity of the management is doubtful and the accounts of the company/group companies are irregular with Banks/FIs/other institution or statutory liability is overdue or the payments are delayed, the probability of default increases substantially. As such

a) If score under the parameter “Commitment and Sincerity” OR ‘Track Record in Debt Repayment' is ‘0'

The credit risk rating derived from the model may be downgraded to ‘B'. If, in such case, the rating is ‘B' or ‘C', the rating will be downgraded by one notch.

b) If the score under the parameters “Commitment & Sincerity” and ‘Track Record in Debt Repayment' is greater than‘0', and upto ‘1',

The rating may be downgraded by one notch.

To ensure the quality and consistency of credit risk ratings, vetting of the rating is also done.


The above hurdle points will be applicable to Large and Mid Corporate Accounts only.


Credit Monitoring/Post Sanction follow-up is an important ingredient of a sound credit management system and calls for monitoring of the health/conduct of the borrowal accounts on regular intervals on a continuous basis.

Keeping this concept in mind, bank had developed a Post Sanction monitoring tool, namely, Preventive Monitoring System. Moreover, the PMS score has been used as one of the critical inputs for credit risk rating. This system is an on-line reporting system to the sanctioning authority on quarterly basis.

The Preventive Monitoring system, at present, is applicable in respect of all borrowal accounts having exposure of above Rs.1 crore in CBS Branches and above Rs.12 crore in Non-CBS Branches.

The system was proposed to replace Quarterly Review Sheets (QRS) as QRS has its own limits.The aim is to minimize the loan losses by focusing on accounts showing “Early Warning” signals of deterioration.


Comprehensive-Covers indicators of conduct of account, business performance etc.

Objective-Health of the account is reflected as a single numerical score.

Diagnostic-The reasons behind deterioration are analysed for taking remedial steps.

Memory-Unsatisfactory features or irregularities are accounted for one year.

Preventive-Timely action / corrective measures can be taken in Early Warning Category accounts.

Continuous monitoring of health & conduct of account. Captures negative signals in respect of 27parameters.

PMS rank is an input to credit risk rating. PMS rank is calibrated on a scale of 1-10.

Bank initiates necessary actions on accounts showing early warning signals through PMS or having ‘C' or ‘D' (high) risk.

CHAPTER 7: Findings and Conclusions


1. In terms of RBI guidelines, the bank has implemented standardized approach as on 31.03.08.

2. The bank has carefully implemented the Basel - II norms by adopting a comprehensive framework which includes Credit Risk Management Structure, Strategy & Policy and Processes & Systems.

3. Bank has upgraded the existing LADDER (Loans and Advances Data Desk for Evaluation and Reports) system called “Cris-Mac” to evaluate the risk weighted assets.

4. Bank uses Credit Scoring model to evaluate the risk associated with the asset. Under this the bank has developed an in-house credit model named as ‘PNBTrac' and it is the only state owned bank that has developed such a model and patented it.

5. Under this model the request for loan is evaluated under four major aspects namely financial evaluation, management evaluation, business evaluation and conduct of account evaluation.

6. All these evaluations provide a scoring on that aspect and a final credit scoring is obtained after taking into account the weights of the parameters.

7. The ratings so obtained are downgraded if the company is not able to match the criteria set under the financial and management evaluation.

8. Another important step taken by the bank is the implementation of ‘Preventive Monitoring System (PMS)' which is an on-line reporting system. It was implemented to replace the Quarterly Review Sheets (QRS).

PMS rank is an input to credit risk rating and is calibrated on a scale of 1-10. Bank initiates necessary actions on accounts showing early warning signals through PMS or having ‘C' or ‘D' (high) risk.

As a result of complying with the Basel II Accord the bank has performed well in terms of both CRAR and reduction of NPAs.

CRAR improved to 14.03 percent as at the end of March 2009, which is 52bps higher than its peer average of 13.51 percent. Similarly, Tier I capital at 8.98%, which is 72 bps higher than its peers' average of 8.26%. Overall, the bank's capitalization has been stable and should enable the bank to sustain its growth momentum in the near term.

Ratio of Gross NPAs to Gross advances was 1.77 per cent; showing an improvement of 97 bps over the previous year. Ratio of net NPAs to net advances stood at 0.17 per cent; an improvement of 47 bps over the previous year.

Priority Sector Credit at Rs 50,793 crore was 41.53 percentage of Adjusted Net Bank Credit (ANBC) and continues to be above the national goal of 40 per cent.

Agricultural credit at Rs 24,057 crore was 19.72 percent of ANBC and continues to be higher than the national goal of 18 percent.


The bank has been able to move along the guidelines issued by RBI and adopted the Basel -II Accord. As per the latest guidelines issued by RBI, all the banks have to move on to the advanced approaches and the bank has a long way to go.

The bank has adopted very good techniques in term of software and monitoring systems but need to take more steps to go to advanced approach. The bank has a complete risk management philosophy which is updated every year which indicates that the bank is adhering well to the exposure and risk norms.

The bank has no credit management techniques like credit derivatives in its basket for better credit risk management.

Chapter 8: Recommendations


RBI has issued new guidelines for migrating to advanced approaches from the present standardized approaches by 2013. So the bank has to take appropriate steps to prepare for its implementation.

The bank has to develop a comprehensive database and the data has to be collected and stored for different segments to cater to.

The bank already issues the circulars relating to various updates required in working of the bank but should also hold training classes to provide employees first hand experience and in depth knowledge of the softwares and the processes.

The bank should go in for testing before implementing the advanced approaches as it has done in case of the standardized approach.

The Credit Rating agencies also should develop advanced Internal Ratings Based Techniques so as to develop a database. Each Bank has its own expertise in determining PD of companies across different sectors. A common database be developed in which the experience of different banks is stored.

The risk weights under standardized approach need to be revised to give unrated debt a higher risk weightage.

CHAPTER 9: References

1. Brown, Aaron (2004): The origins and evolution of credit risk management, Global Association of Risk professionals, downloadable: www.eraider.com/images/articles/RiskHistory1.pdf

2. Circulars No. 12/06, 14/08, 80/08, 208/08, 15/08, 02/08, 08/07, 06/07, 12/08, 11/07, 20/08 and 55/08 of the bank.

3. Domnigues, Ronald: Creditmetrics Model, downloadable: http://www.ronalddomingues.com/index.php?lang=&s=risk&id=37

4. Elizalde, Able (2005): Credit risk Models II: Structural Models., CEMFI and UPNA, downloadable: citeseer.ist.psu.edu/elizalde03credit.html

5. Gopinath, Shyamala (2006): Changing Paradigms in Risk Management: downloadable: www.bis.org/review/r061002c.pdf

6. Harpar, David: Basel II: IRB Approach concepts, downloadable: http://www.bionicturtle.com/learn/article/basel_ii_irb_approach_concepts/

7. I Altman, Edward (2006): Default Recovery Rates and LGD in Credit Risk Modeling and Practice: An Updated Review of the Literature and Empirical Evidence, downloadable: www.defaultrisk.com/pp_recov_53.htm

8. Joseph, Ciby, ‘Credit Risk Analysis', edition 2, pp. 245-270.

9. Mitali, Katali: Credit risk: so far so good, downloadable: www.domain-b.com/management/finance/20031107_good.html

10. Proposed Treatment of Expected and Unexpected Losses, downloadable: http://www.federalreserve.gov/boarddocs/press/bcreg/2003/20031030/attachment.pdf

11. Rajashekar, H S (2003): Risk Management: Shape up or Languish: downloadable: www.dqindia.ciol.com/content/bank/103070206.asp

12. Saunders, Anthony and Cornett millon, ‘Financials Institution Management', edition 6, pp 245-290

13. Sy, Wilcon (2008): Credit Risk Models: why they failed in credit crisis, APRA, downloadable: www.econpapers.repec.org/paper/apraprewp/wp2008-03.htm

14. Tarashev, Nikola A. : An Empirical Evaluation of Structural Credit-Risk Models, downloadable: http://www.ijcb.org/journal/ijcb08q1a1.pdf

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