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