Welcome to Banking Quest

 Business Models

• Any business models including Credit Risk Models, there should be three key components within it:

1. Creating value, 

2. Delivering value, and 

3. Capturing value. 

• This shows that the business model doesn’t revolve around money. It revolves around value.

 Credit Risk Models

• Let us discuss Credit Risk Models.
• Financial institutions used credit risk analysis models to determine the probability of default of a potential borrower.
• The models provide information on the level of a borrower’s credit risk at any particular time. 
• If the lender fails to detect the credit risk in advance, it exposes them to the risk of default and loss of funds. 
• Lenders rely on the validation provided by credit risk analysis models to make key lending decisions on whether or not to extend credit to the borrower and the rate of interest to be charged on the credit.

 Default Probability

• The probability of default (PD) is the probability of a borrower or debtor defaulting on loan repayments.
• Within financial markets, an asset’s probability of default is the probability that the asset yields no return to its holder over its lifetime and the asset price goes to zero. 
• Investors use the probability of default to calculate the expected loss from an investment whereas banks use PD to calculate the expected loss from the loans given by them to the borrowers.
• Default probability is the likelihood over a specified period, usually one year, that a borrower will not be able to make scheduled repayments. 
• It can be applied to a variety of different risk management or credit analysis scenarios. 
• PD depends, not only on the borrower's characteristics but also on the economic environment.
• During COVID pandemic period in our country, the PD would have gone up but for the sops and moratorium expended by RBI and Government of India.
• For businesses, a probability of default is implied by their credit rating. 
• PDs may also be estimated using historical data and statistical techniques. PD is used along with "loss given default" (LGD) and "exposure at default" (EAD) in a variety of risk management models to estimate possible losses faced by lenders. 
• Generally, the higher the default probability, the higher the interest rate the lender will charge the borrower.

 Loss Given Default

• Loss given default (LGD) is the estimated amount of money a bank or other financial institution loses when a borrower defaults on a loan. 
• LGD is depicted as a percentage of total exposure at the time of default or a single dollar or rupee value of potential loss. 
• A financial institution’s total LGD is calculated after a review of all outstanding loans using cumulative losses and exposure.
• Quantifying losses can be complex and require an analysis of several variables. 
• Assuming if Bank A lends Rs. 20 lakhs to Company XYZ, and the company defaults. 
• Bank A’s loss is not necessarily Rs. 20 lakhs. Other factors must be considered such as the amount of collateral, whether installment payments have been made, and whether the bank makes use of the legal system and other systems for recovering the amount lent from Company XYZ. 
• With these and other factors considered, Bank A may, in reality, have sustained a far smaller loss than the initial Rs. 20 lakhs loan.
• Determining the amount of loss is an important and fairly common parameter in most risk models. 
• LGD is an essential component of the Basel Models, a set of international banking regulations, as it is used in the calculation of economic capital, expected loss, and regulatory capital. 
• The expected loss is calculated as a loan’s LGD multiplied by both its probability of default (PD) and the financial institution’s exposure at default (EAD).
• A common variation considers the exposure at risk and recovery rate.
• Exposure at default is an estimated value that predicts the amount of loss a bank may experience when a debtor defaults on a loan. 
• The recovery rate is a risk-adjusted measure to right-size the default based on the likelihood of the outcome.
• LGD (in rupees) = Exposure at Default (EAD) * (1 - Recovery Rate).
• Exposure at default is the total value of a loan that a bank is exposed to when a borrower defaults. 
• For example, if a borrower takes out a loan for Rs. 100,000 and two years later the amount left on the loan is Rs. 75,000, and the borrower defaults, the exposure at default is Rs. 75,000.

Exposure At Default

• When analyzing default risk, banks will often calculate the EAD on a loan as it aims to predict the amount, the bank will be exposed to when a borrower defaults. 
• Exposure at default constantly changes as a borrower keeps on paying down his loan.
• The main difference between LGD and EAD is that LGD takes into consideration any recovery on the default. For this reason, EAD is the more conservative measurement as it is the higher figure. 
• LGD is more often the best case scenario that relies on multiple assumptions.
• For example, if a borrower defaults on their remaining car loan, the EAD is the amount of the loan left they defaulted on. 
• Now, if a bank can then sell that car and recover a certain amount of the EAD, that will be taken into consideration to calculate LGD.

 Calculation of LGD

• Let us take an hypothetical example. Assuming a Bank gives 100 business loans of Rs. 10,000/- each in a village.
• So, total amount of loan given is:
  • Rs. 10,000 x 100 = Rs. 10 lakhs and this is called EAD.
• Let us assume that 2 loans have become NPA.
• So, PD% would be 2%, that is 2 loans out of total loans of 100. Let us assume that the borrowers have service only the interest and not the principal.
• Let us assume that the borrowers have given some collateral and the bank expects a recovery of 60% from the collateral. 
• We can find out the Expected Loss (EL), by using the following formula:
• EL = EAD X PD% X LGD
• LGD = 1 minus Recovery Rate
• Rs. 10 lakhs x (2/100) x (1 – 0.60)
• Rs. 10 lakhs x 0.02 x 0.40 = Rs. 8,000/-

 Concept of EL & UL

• Expected Loss (EL) is like depreciation on Plant & Machinery. It is a known loss and hence this charge has to be recovered through P&L of the Bank.
• If P&L is not able to absorb this loss, then the owner or promoter has to pump in sufficient cash to absorb this loss (December, 2015 – Asset Quality Exercise).
• Banks can recover this loss, by loading this element (loan loss provision) as part of their loan product pricing. So, higher the NPA, the loan interest for a bank should be higher when compared to another bank whose NPA provisions are less.
• On the contrary, UL is an unknown loss and this would be a surprise element for the Bank or financial entity.
• The adverse deviation over EL is called UL.
• The UL component has to come from the capital contribution of the owners or promoters.

 Concept of  UL

• Capital is key to financial institutions, as its primary function is to cover unexpected losses arising primarily out of Credit, Market & Operational risks.
• The Global financial crisis of 2008 had revealed that the capital kept by the banks were inadequate even for covering the existing Credit risk and adding fuel to the fire, the capital was additionally required for managing the liquidity and strategic and reputational risks also.

 Predictive Credit Risk Models

• Lending is more of a futuristic oriented job.  Finally, the Bank has to make a judgement whether the borrower will repay the given loan or not. 
• For small ticket loans, this futuristic job analysis can look simple but for big loans and projects, carrying out this job by a single officer is more complex and complicated.  
• In view of this, risk experts have come out with several Predictive Analytics models and automated processes, which can help the banks to take an informed decision.
• All types of credit risk management require data analytics, and support of data. 
• With this, the processing tools can give an idea about the borrower whether the entity will be able to reasonably pay back the loans given or not. 
• Therefore, the predictive analytics process is the practice of deriving information from existing data in order to identify the likelihood of patterns and predict future outcomes and trends of the borrowers. 
• These predictive analytics among other things include forecasts about the borrower as to what might happen in the future for the loans given with an acceptable level of reliability. 
• It will also carry out what-if scenarios, i.e., scenario analysis under various assumed future scenarios and risk assessment on the borrowers. 
• These models also provide estimates of credit risk and the contribution or capital required from the bank side to take care of the unexpected losses.

Altman z Scores

• The biggest calamity in the US that can befall on equity investors and lenders who supported the company is corporate bankruptcy, which wipes out the equity of the entity and knocks the stock’s investment value down to zero.
• Fundamental analysis attempts to gauge the financial strength of a company using a variety of metrics.
• Used in conjunction with one another, financial ratios can often help to predict the long-term viability of a company. 
• However, this is not always the case; sometimes the ratios of a company give conflicting views. 
• To help eliminate this confusion, New York University Professor Edward Altman developed the Z-Scores in the late 1960s to explicitly address the likelihood that a company would go bankrupt.
• The Altman Z-Score actually consists of five performance ratios that are combined into a single Z-score, which are the constants found out by Altman from his research. 
• These five ratios are weighted using the following formula: Z-Score = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E. Where:
  • A = net working capital ÷ total assets.
  • B = retained earnings ÷ total assets.
  • C = earnings before interest & taxes ÷ total assets.
  • D = market value of equity ÷ total liabilities.
  • E = sales ÷ total assets.
• When analysing the Z-Score of a company, the lower the value, the higher the odds that the company is headed toward bankruptcy.
• Altman came up with the following rules for interpreting a firm’s Z-Score: 
  • Below 1.8 indicates a firm is headed for bankruptcy depicted by red area 
  • Above 3.0 indicates a firm is unlikely to enter bankruptcy depicted by green area & 
• Between 1.8 and 3.0 is under watch category depicted by grey area.

Deconstructing the Z-Score 

Net Working Capital to Total Assets: This ratio measures a company’s efficiency and its short-term financial health. 

• Positive working capital means that the company is able to meet its short-term obligations. 
• Negative working capital means that a company’s current assets cannot meet its short-term liabilities; it could have problems paying back creditors in the short term, ultimately forcing it into bankruptcy. 
• Companies with healthy, positive working capital shouldn’t have problems in paying their short-term payment obligations which are arising in the working capital cycle.

Retained Earnings to Total Assets:  The retained earnings of a company are the percentage of net earnings not paid out as dividends; they are “retained” to be reinvested in the firm or used to pay down debt. 

• Retained earnings are calculated as follows: Op. Bal of  retained earnings + net income (net loss) – dividends paid.
• The ratio of retained earnings to total assets helps to measure the extent to which a company relies on debt, or leverage. The lower the ratio, the more a company is funding assets by borrowing instead of through retained earnings. In case debts are more, it would increase the risk of bankruptcy if the firm cannot meet its debt obligations.

Earnings Before Interest & Taxes to Total Assets: This is a variation on return on assets, which is net income divided by total assets. This ratio assesses a firm’s ability to generate profits from its assets before deducting interest and taxes.

Sales to Total Assets: The ratio of sales to total assets, more commonly referred to as asset turnover, measures the sales generated by a company for every rupee’s worth of its assets. In other words, asset turnover is an indication of how efficiently a company is using its assets to generate sales. 

• The higher the number, the better it is, while low or falling asset turnover can signal a failure by the company to expand its market share.

Market Value of Equity to Total Liabilities: The ratio of market value of equity to total liabilities shows how much a company’s market value (as measured by market capitalization, or share price times shares outstanding) could decline before liabilities exceed assets. Unlike the other ratio components used by the Z-Score, market value is based on the perception of the market.

• The market capitalization of a firm is an indication of the market’s confidence in a company’s financial position. Generally speaking, the higher the market capitalization of a company, the higher the likelihood that the firm can survive going forward.

Weaknesses of the Z-Score:

• Z score analysis mainly depends on the ratio analysis.  Therefore, whatever the drawback ratio analysis had, the same is applicable to Z score analysis also. 
• The biggest drawback, as is the case with all financial analysis, is that the Z-Score is reliant on the quality of the underlying financial statement data. If a company is “cooking the books,” its financial statement data is not a true representation of the strength (or lack thereof) of the company. Hence, this defect will be passed on to Z score analysis also. It should be remembered that the Z-Score is only as good as the data that goes into it (“garbage in, garbage out”).
• All ratios are arrived at based on historic data and the analysis expects the management would behave in the same fashion in future also as it behaved in the past.  But it can happen that the management can change its style of function in the future and go for positive course correction. In such, case, the model prediction can go wrong.

 Conclusion:

• The Z-Score is another tool that investors/lenders can use to monitor the safety of their investments. 
• A steadily declining score can signal underlying problems with a company. 
• That being said, it is still subject to shortcomings faced by all financial metrics. 
• At a minimum, however, a low or declining Z-Score should spur or make the investor or lender conduct more in-depth analysis to seek out the root cause or the truth. 

 J.P. Morgan’s ‘CreditMetrics’ Model

• In this model, analysis of the entire portfolio is based on an assessment of the credit risk of individual instruments and subsequently applied to the portfolio by taking into account the cross correlation of bonds. 
• The model was created by the JP Morgan bank back in 1997. Since 1999, it has become part of the credit risk management for almost all major banks. 
• The CreditMetrics model is constantly being developed and improved upon since its introduction to become a more flexible model to respond to market developments and regulatory requirements. 
• This model belongs to the category of mark-to-market models, because it estimates default of the issuer on the basis of the rating changes. This model measured the portfolio credit risk and the value by extensively using bond-rating data provided by Moody’s and Standard & Poor’s. 
• The structure of this model, consists of four parts and they are:
  • Use of Value at Risk (VaR) to predict the credit risk of a single exposure.
  • Use of VaR to predict the credit risk of the portfolio.
  • The third part is to assess the correlations between the assets which have made up the portfolio.
  • The fourth one is the basic exposures that form part and support the total exposure.

 Credit Suisse’s CreditRisk+ Model

• This is another industry model, which is based on a typical insurance mathematics approach and therefore, also often called an actuarial model. 
• This model is based on the actuarial calculation of the expected default rates and measuring the unexpected losses from the default.  
• This model was originally developed by Credit Suisse Financial Products (CSFP) and is now one of the financial industry’s benchmark models in the area of credit risk management. 

 Meaning of Credit VaR

• Credit Value at Risk (cVAR) is a measure of the potential economic loss on credit exposures due to credit events.
• Credit Value at Risk may be calculated for individual assets, portfolios, or even institutions.
• It can be expressed in absolute terms, such as Rupees or Euros or Dollars, or as a percentage of total exposure.
• The calculation requires three inputs: the current market price, the planned sales price, and their respective default probabilities.
• It becomes important to calculate credit value at risk when there is a need to determine the level of an institution’s capital adequacy requirement.
• Credit Value at Risk is a number that tells you how risky your credit portfolio is. It will tell you the unexpected losses of the credit portfolio over one year at some confidence level. The losses are expressed in terms of currency units or credit exposure.
• It helps banks and financial institutions to determine the level of their capital adequacy requirement. In other words, it is a measure of how much money you stand to lose from exposure to credit risk over one year under normal market conditions.
• The potential loss arrived at using this method is also called Economic Capital required by the entity to take care of the risk. 
• Credit Value at Risk is influenced by three factors

1. 1. The probability of default for each obligor in the portfolio – PD.
   2. The exposure to the obligor at default, or collateralization level which is defined as the percentage of the exposure that would be lost if the counterparty defaults – LGD.
   3. The exposure at default is modified by a credit conversion factor which reflects the expected loss that would be incurred in a worst-case scenario, such as a downturn of the economy or a systemic crisis – EAD.

Calculate Credit Value at Risk:

• The loss distribution of the credit portfolio is determined by simulation, and the Credit Value at Risk is calculated as follows,
• Credit Value at Risk=Worst Credit Loss-Expected Credit Loss

 IMPORTANCE OF CREDIT VALUE AT RISK:

• This is a measure of the amount that you could lose from a credit portfolio over one year under normal market conditions. 
• It is important to determine levels of capital adequacy requirement in order to show prudence in making business decisions and effective management of risk.

To sum up:

• Credit Value at Risk is a number that tells the bank how risky its credit portfolio is. 
• This number tells you the unexpected losses over one year at a selected confidence interval. 
• The losses are expressed in terms of currency units or credit exposure. 
• The capital adequacy ratio helps banks and financial institutions to figure out how much money they need.