It is fascinating that in today’s quest for data to make better decisions, banks still use only three out of 177 credit risk criteria to make a credit decision.
Let me share with you feedback that I provided a client on the issue of Name Lending, a topic that seems to be prevalent in most countries. I have seen this syndrome not only in emerging markets, but also in Eastern Europe and Asia. For those that are unfamiliar with the topic, Name Lending means lending to an obligor based on essentially three criteria related to the individual not the firm:
1. Perceived reputation of the owner of the firm in the market place. Essentially what is known of the owner from checkings in the market, feedback from friends and family, and how often this person appears in papers and talk shows, public forums etc.
2. Past history of the obligor or the owner, and whether there were any known incidents of defaults with any institution.
3. Perceived networth of the owner. Driving a Ferrari or having fancy offices (with an assertive attitude) provides the impression that the owner or obligor has the ability to create wealth and therefore is able to repay bank debt with ease. The fact that this wealth could have been borrowed from another institution or from the person’s own firm does not feature in the calculation.
Assuming for a second that the above three criteria are genuine in determining probability of default (PD), they represent only 3 out of 177 that are needed to determine PDs. This means at best it falls far short of what is needed to make a credit risk decision. No wonder that bankers who continue to use this archaic methodology rely almost entirely on collateral security to conduct lending business.
Although the above argument should kill any notion with regards to practicing this misguided form of lending, some traditional bankers still insist on it. I was recently asked by a banker to elaborate how such practices can be managed under the Basel guidelines, and I thought I would share this with you.
Rating of obligors without financials A bank is using our Credit Risk System (CRS) and was complaining (would you believe!) that (a) the ratings for SMEs were so high that it pushed the Portfolio Risk Rating (PRR) to over 7, and (b) that its experience on default with them did not reflect their high ratings. As a result, the bank wanted ways to better capture their risk than the seemingly rigid way the CRS does it (by requesting financials, or impose a high rating when financials were unavailable).
Let us elaborate on the issues a little more. Essentially, each obligor has an Expected Loss (EL), which is comprised from essentially Probability of Default (PD) x Loss Given Default (LGD). The PD is assessed by using a risk rating tool. The tool’s results have to be validated by assessing why risk ratings for some obligors seem to be high when the default rates are low, and visa versa. This validation process is easy when you have numbers as you can capture “passing the hat syndrome” etc. When you do not, we have a problem that is compounded by the high rating the system imposes. So the question is: Is there is a way to use other data to assess the EL of these Name Lending clients? The answer yes, there are three ways:
1. Create your own numbers. One of the fundamental tenants of quantitative assessments is to enter numbers that you believe in. We use audited statements because someone spent time and effort to validate the numbers provided by obligors. If there were not auditors, bankers have to do the audit work themselves. If there were no numbers at all, then bankers have to ask the right questions, and validate the answers in order to acquire numbers they can rely on. To reach this stage of maturity, officers have to know accounting very well and should have been practiced in the art of auditing. If they were not, then create an audit team in the bank that can do this across all obligors, call it the “Due Diligence Team” that can visit clients, ask questions and checks the inventory, warehouse, whatever books there are etc. However this process is costly in terms of expenses, but guess what, it is cheaper than the capital needed for playing Russian roulette.
2. Use account movement data. Account statements are very useful in providing the following cash flow information:
a. Identify core borrowings b. Returned checks (if bank reporting is helpful) c. General flavor of the transactions (again if bank reporting is helpful) d. Turnover of facility utilization (total debits against facility total) e. Share of Wallet (in terms of total credits versus Sales etc)
It’s main drawback is that it is difficult to decipher if there were many transactions, and the level of information acquired does not start to cover one tenth of the data needed to make an assessment (Cash Cycle, EBITDA margins etc). As such, it still needs to be combined with (1) above.
3. Forget the PD, just structure the facility to control the LGD. In case (1) and (2) above are too burdensome or unattainable, focus on the LGD instead: concentrate on structuring the facilities such that the offerings are linked to specific cash flow streams; and force the cash to flow through the bank first before anyone else for repayments. In the short term at least this can go a long way to managing the unknowns associated with lack of data. For example:
a. Receivable discounting: Discount invoices, checks, LCs etc; b. Channel Financing: Get data through vendors and clients; c. Bonded Warehousing: Control the good and reduce the EL, or d. Do what the Canadian banks do, limit the offerings to a percentage of receivables and inventory balances at the end of each month.
Does this argument sound familiar? You got it: Credit Product Programs (CPPs). You need to create them. You may have the mechanism to do so through the consultancy that we offer, and you have the capability of acquiring default rates to substantiate a case for lower LGDs. This mechanism can help you override the ratings in confidence with full data support. Perhaps (as I suspect) the dilemma here is: would business want to let go of the current freewheeling roulette for a tighter control over credit offerings? Alas, you can’t have the cake and eat it at the same time. Either tighten offerings, or run with high ratings.
Some banks opt for tightening the LGD through using collateral security instead. The problem with relying solely on collateral security is twofold:
1. That reliance on collateral security is a volatile proposition both on the nature of the collateral (volatile pricing), and the frequency of exercising the rights to the collateral when liquidating the collateral in order to settle outstandings. This is not banking, as banks require a ship that sails in smooth waters, not with a turbulent balance sheet and income statement.
2. Secondly, this reliance is without supporting data, and as such, at best can only be used as a second way out.
TM and RACs One way to manage Name Lending is to use a Score Card system. This is the TM and RACs that we will be introducing to the CRS later this year, and which essentially is designed to cover the risk areas that are not normally covered in the 177 criteria. It normally compliments risk rating systems. However its use must be accompanied with a well documented Product Program, and supported by data to estimate and manage the PDs.
For more details on 6 Sigma’s CRS, , please contact us on email@example.com.
© 2014 6 Sigma Group
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