Speech Notes - Corporate Profitability Through Credit Management Efficiency

“Corporate Profitability Through Credit Management Efficiency”

Speech Delivered to the Credit Management Association of the Philippines National
Convention in April of 2007

By Raoul A. Villegas

Distinguished Dignitaries, officers of the CMAP, convention delegates, and

guests: I stand before you today to talk about Corporate Profitability through Credit

Management. (turn to slide 2)

It is appropriate to first look at the types of risk that you as credit managers

face, along with your customers. Looking at four broad categories, we can subdivide

the risk pie into several:

• Business risk – faced by credit granting institutions and their customers. Are the

strategies and plans of execution profitable?

• Credit risk – can the customer continue to keep the account current? What are the

chances for financial distress leading to delays or worse, defaults.

• Market risk – are movements in credit prices going to affect future profitability?

• Operating risk – can credit granting institutions balance the sometimes conflicting

imperatives of winning market share through higher credit portfolio growth, with

exercising credit discipline? Can credit granting institutions stay within the

boundaries prescribed by regulators?

• All of these are serious concerns. (turn to slide 3)

Efficiency in managing credit a credit portfolio for profitability is a challenging

goal. In order to be efficient, the credit managers have got to be able to tune out the

noise. In any credit evaluation it is tempting to let trivial information get in the way of

a cold, objective analysis. Efficiency demands focus.




By focusing on key credit factors, depending on the type of customer, credit

managers can increase evaluation throughput.

What is evaluation throughput? It is the rate at which credit evaluations are

processed. If efficiency allows faster evaluation times without sacrificing discipline, the

firm effectively increases its credit pipeline capacity. Increasing capacity, subject to

available capital, allows for more “hits” in granting credit. (turn to slide 4)

100% account and portfolio profitability is a desirable goal. In an ideal world,

the battle would be over even before the fight, as you see this print of Leonidas at

Thermopylae. Xerxes knew that he would win at Thermopylae, it was just a question of

how much it would cost him.

Credit discipline would allow for credit managers to screen credit applicants with

100% accuracy. One would never have bad accounts. There wouldn’t be any defaults.

Net spreads would remain profitable. Everyone would live happily ever after, and I

would not be here in front of you today (turn to slide 5)

However, we live in the real world. In the real world, we have to deal with real

issues like good customers, in-between customers, bad customers, falling spreads,

defaults, provisions, loan losses, and properties paid in kind.

Therefore in the real world we deal with real risk, particularly as it relates to

market risk and credit risk.




While the assumption of credit risk is unlike gambling in its gaming sense, it does

have analogous characteristics. Both require the assumption of risk after a calculation

of odds. In the case of credit risk, it is the odds of repayment vs. the odds of default or

worse.

And to be profitable, one has to assume the risk. You can avoid risk altogether,

but that means you don’t put capital to work. Consequently you don’t earn. (turn to

slide 6)

What one can do to manage the risk with efficiency is to focus not on the

quantity of the analysis, but on the quality of the information, the quality of the

analysis, and the quality of the validation.

There’s a lot that has been said about pricing discipline offsetting portfolio risk,

but in the end one doesn’t collect accrued interest revenue on an account in serious

distress and unable to keep payments current.

The interest margin has got to be translated into cash, and the best way to do it

is to maintain analytic quality. Hardly anybody wants to go into a credit work out

situation. (turn to slide 7)

So the focal point becomes economy of effort.

Economy of effort comes from knowing the key credit factors on which to focus.

These key credit factors form the foundation on which the credit evaluation rests.




Let me stress that the analytical tools that contribute the most value are robust

and blunt. This means that these tools are widely usable, with minor adjustments,

across many different credit customers in the same credit segment.

If we were to use “fine analytical tools” we would find that these tools require

too many adjustments to carry across various customers. We would eat up more time

adjusting the analytical tools and models than actually using them to arrive at a good

credit decision.

The economy of effort is applicable to various credit segments. (turn to slide 8)

Of course, one can segment using retail vs. wholesale credit markets. On one

side, you’ve got individual customers who need credit cards, auto loans, housing loans,

multi-purpose loans, etc. On the other side, you’ve got wholesale credit with major

financial institutions originating funding for distribution. (turn to slide 9)

The other way to think about segmenting credit markets deals with trade credit

vs. other credit. Trade credit covers the regular interplay of transactions between

suppliers and vendors – working capital financing. This is particularly important to

small and medium enterprises.

Other credit deals with other requirements – plant expansion, land expansion,

and everything in between not dealing with working capital. I hesitate to call this non-

trade credit, since all credit in some fashion does support trade in one form or another.

(turn to slide 10)




Another way to segment deals with the size and reach of the customer. From

the very small we can see SME financing all the way up to corporate credit, where the

giants borrow.

Each of these markets demands a different type of credit treatment. (turn to

slide 11)

As a credit granting institution, the management has to decide what is the

roadmap, and how do they execute to get there?

How much loss can they tolerate? And there will be losses.

How much in payment delays can they tolerate? Not all credit customers will pay

on time.

Do they want to win share? How low can they go on pricing? Maybe they are

not focused on share but on profitability, in which case pricing takes a back seat to

credit discipline.

How liberal or how strict are the evaluation metrics, and the collection policies?

(turn to slide 12).

So really the first thing to decide from the beginning is what does management

really want? And when I talk about management, I am talking about everyone being

on the same page, from the board all the way down to the supervisors manning the

front lines.




The Board and senior management have got to get together and decide on the

business plan, the strategic direction. Once this is established, that drives the loan

portfolio objectives in terms of quality, composition, growth, and profitability.

From here emerge the overall credit policy and the procedures implemented by

the on the ground troops: middle managers and supervisors. These policies and

procedures govern everything up to and including credit work outs.

If not all insider stakeholders are in agreement, whether board, senior

management, middle management, or supervisors, then it is a sure recipe for trouble.

Therefore it is extremely important to get buy in from all of the troops. Everyone

succeeds or fails as one. (turn to slide 13)

And when we talk about the details underlying credit decisions, we run into the

two types of credit decisions: judgmental vs. statistical.

I believe we have all run into the judgmental criteria. This is the situation where

if an account is on the fence with respect to approval or disapproval, the credit

marketing officer will say “This guy’s rich! He’s got the assets to pay! He is the richest

guy around here!” But these criteria don’t necessarily guarantee that the account will

demonstrate performance.

On the other hand, a credit decision might be too statistical in nature. The

decision to grant credit in this case is too automated and does not place enough weight




on certain key qualitative factors that are important in establishing the context of the

credit decision.

The common assumption on both is that the future will resemble the past, which

is not always the case. Current credit risks being evaluated are compared to past

portfolio experience, with the objective of granting credit only to those that show

acceptable risk.

Automated scoring has adds value in the following ways:

• It quantitatively defines degree of credit risk.

• It allows a hard comparison between disparate applicants.

• Allows tracking of credit performance over time using quantifiable metrics.

• Allows decision automation. This is one key factor in increasing credit

evaluation throughput. (turn to slide 14)

Realistically, a credit decision has two initial outcomes: one either grants credit

or refuses credit.

In the case of a credit refusal, the expected pay off is zero, neither a win nor a

loss. The worst one might say is that there was an opportunity lost should that

customer, if marginal in nature, might have been able to demonstrate good credit

performance.

However if the decision made is to grant credit, the outcome tree branches out

into two more outcomes: either the customer performs, or does not perform. If he




does not perform not only does one bear the cost of funds; one also bears the ultimate

cost of an asset write down.

Therefore the profitability of a customer depends on the conditional probability

first on whether credit is granted, and whether that customer can demonstrate credit

performance. (turn to slide 15)

Ultimately across a large credit portfolio, one is managing a set of credit

exposures put together by credit management, with the aim of producing an expected

value, a profit. If P represents the conditional probability that a customer granted

credit will pay, then 1-P represents the conditional probability that a customer granted

credit will not pay.

At that level, managing the credit portfolio becomes a delicate balancing act of

managing the exposure according to the assessment of the odds, or probabilities. One

can calculate the probability that a credit portfolio will break even, and can even

estimate the present values of revenues and costs.

Ultimately, one is trying to maximize P, or the likelihood that the customer will

demonstrate credit performance. (turn to slide 16)

Here is where we move into the credit factors per segment.

With respect to consumer credit, I believe that a significant number of us here

are aware of the importance of credit scoring, and the drivers of credit scoring. The

most significant factors in order of weight are:




• Payment history.

• Total debt to total lines.

• Length of credit history.

• Recent requests for new credit.

• Amount and type of credit in use. (turn to slide 17)

We see that with respect to ratio of balances to total limits, this single factor

shows a higher risk when outstanding balances reach 60% or more of what is available

to be borrowed. (turn to slide 18)

And if one takes a look at the age of the oldest credit card in the customer’s

wallet, one can see the risk falls off dramatically for customers that have maintained

the same account for six years or more. That speaks of the ability to manage individual

credit responsibly, a key consideration interestingly enough, in being able to qualify for

more credit. (turn to slide 19)

Further, if one takes into account the number of recent credit checks, within one

year, one can get a sense if a credit customer is exhibiting sober, personal credit

management; or is accumulating new credit at the risk of damaging his or her finances,

and consequently, credit standing. (turn to slide 20)

Add to this a strong inverse correlation between the amount of credit cards held

and risk of an individual, one can get a fairly complete picture of the credit risk posed

by an individual. These are just a few of the pieces of information that one can use to




build up a credit scoring procedure for retail consumer credit. HOWEVER, GREATER

CREDIT EFFICIENCY CAN BE ACHIEVED IF ONE LOOKS AT OTHER QUASI-FINANCIAL

BEHAVIOR SHOWN BY THE CREDIT CUSTOMER. (turn to slide 21)

Insurance industry research has shown that there is a strong negative correlation

between the average insurance loss claimed by people with automobile insurance, who

also have poor credit ratings.

Said differently, people with poor credit ratings usually show higher loss claims

on their insured cars vs. people with good credit ratings. Why? (turn to slide 22)

Second, that same research has shown that those homeowners, with

homeowners insurance, that also have poor credit, show insurance loss ratios three

times as large compared to people that own homes, have homeowners insurance, and

show good credit. Why? (turn to slide 23)

Third, people with poor credit show a propensity for trouble on the road. People

with poor credit get involved in 40% more traffic accidents than people with good

credit. Why? (turn to slide 24)

The statistical arguments are compelling. People with poor credit generate

insurance losses at a greater magnitude, and at a higher rate of frequency, than people

with good credit.

At first it might seem like a tenuous connection. How does customer behavior,

which shows up in insured loss claims, indicate the credit quality of an individual?




If you take the time to think about it, people with good credit tend to be

meticulous across their entire lifestyle.

• They pay bills on time.

• They keep their accounts current.

• They don’t keep a lot of accounts open.

• They keep balances low.

• They pay off debt.

All of these behaviors represent an effort to live a sober lifestyle within one’s means –

certainly the key to showing credit performance.

And it also shows what a tremendous advantage that an organization like CMAP

has in keeping a current, up to date database, and turbo-charging it with new,

significant information. The incentive lies in that in pooling critical information, along

with new ways to analyze it, the size of the credit pie expands for all of the members.

Who says that the CMAP cannot partner with insurance companies for example,

in building up a comprehensive database of consumer credit behavior expressed

through both financial and insurance data? (turn to slide 25)

Let’s think about that – but first I’d like to talk about another segment of credit,

trade credit. When we talk about trade credit of course it usually refers to the credit

implicitly granted by buyers to vendors in the normal course of commercial transactions.




It can also refer to working capital financing, which is a key resource for small and

medium sized businesses.

Key considerations in trade credit efficiency include:

• Mapping out a trade credit policy.

• What tools do you use? How do you execute?

• What kind of reporting do you need, and how can you act on that reporting?

These are all important. (turn to slide 26)

The trade credit policy with respect to evaluation and collection actually can vary

between two poles: liberal and strict. The upper left quadrant emphasizes up front due

diligence. When a favorable credit decision is granted the customer is granted

significant leeway. The assumption is that the probability of delays or defaults is quite

remote.

The lower left quadrant, with liberal analysis and liberal collections, highlights an

effort to win market share.

The upper right hand quadrant, with strict analysis and strict collections,

generally applies to companies that make and sell products and/or services that are

deemed essential, and have little competition. Imagine for example, large industrial

users of power who purchase their power directly from power generators independent

of napocor, but receive delivery through their local power distributor.




The lower right hand quadrant, is slightly related to the effort to win market

share. Credit is automatically granted, but the collections follow up is fairly strict.

(turn to slide 27)

Of course, we are all familiar with the activity ratios analysis, which include such

measures as tracking average collection period, days of inventory, days payables, and

the cash conversion cycle. These require no elaboration in this forum of seasoned

professionals. However, there is an opportunity to re-cut the available data in different

ways. (turn to slide 28)

Drilling down using the same available data, it is possible to come up with three

alternative measures at which to look:

• Average collections per day.

• Average disbursements per day.

• Net direct cash flow per day.

What are these and how does one use them? (turn to slide 29)

Average collections per day is nothing more than your average accounts

receivable divided by days outstanding.

Disbursements per day is nothing more than average payables divided by days

payables outstanding.

The net of the two is your net direct cash flow per day. These are all estimates

of course.




The trick to using these measures is not to compare them strictly using the

answers generated. One can always pump up the average collections per day for

example, just be booking more accounts receivable. The math proves it out.

However if one normalizes a higher accounts receivable balance using the base

days sales outstanding of the period to which it is being compared, one can get an idea

of how much a prospective credit is underperforming.

It is this “what if” analysis that provides greater context, adds value, and allows

for scenario building in the credit analysis. Assigning probabilities to various scenarios

allows another tool for building up an expected value for each prospective credit.

Please note that the quantitative aspect also has to be complemented by non

quantitative, contextual information. These include such things as:

• Length of time in business.

• Reference histories from other customers.

• Payment history.

• Presence of supplier lawsuits or court ordered liens.

• Conduct and reputation of the principals or owners.

(turn to slide 30)

Of course, the base measures will never go out of style. Credit managers ignore

the base measures of credit evaluation at their own peril. These are the quantitative

bed rock of any credit analysis. But as credit markets become more competitive, there




is a tendency for the credit quality of the average credit portfolio to deteriorate. Credit

granting institutions need to search for that extra metric, the incremental information

that might give them an edge in finding that one incremental credit worthy customer.

(turn to slide 31)

Hand in hand with the credit granting goes the management reporting of

account status and action items. Rather than succumbing to report overload, and

providing every last detail of an account on a regular basis, management has to select

the few key metrics that matter to them the most.

These metrics have to be compiled religiously and reported regularly. The

important thing one has to remember when selecting metrics is that the measurements

must be quantifiable, comparable, and actionable.

Quantifiability means that there is an acknowledged standard as to how these

metrics are calculated, which leaves no room for subjectivity.

Comparability means that metrics could be compared historically, and that one

can interpret clearly the trends emerging from the data.

Actionability means that when the metric is interpreted according to the

standards of the credit policy of the institution, and the conclusion is that there is a

significant negative variance on the metrics associated with the account, there are

definitive steps taken immediately. These steps will follow a gradual escalation in

severity depending on the variance in credit behavior displayed by the customer.




At no point in time should management be scratching their heads based on what

they see in the credit reporting, and start asking “ok, so now what do we do?” (turn to

slide 32)

From here we move on to small business vs. corporate. No other sector in our

economy is so starved for credit, and so important to kick starting our economic

growth. An old adage that rings true some of the time is that lenders lend to those that

don’t require the funds. Everyone wants a good account, so in this country there might

be a phenomenon where every chases all of the same good accounts.

For us to get credit to this vital sector, the challenge is to diversify the

distribution of credit to small business. This sector has distinctly different needs

compared to corporates. (turn to slide 33)

The challenge in doing a credit evaluation of small business comes down to

validation.

Character and reputation count for a lot in terms of evaluating a small business

owner, hand in hand with business performance.

The financial statements require piecing together disparate information from

various sources.

Collateral is important, but most of the emphasis is to lend based on the

normalized cash flow performance of the small business.




It is true that the greatest single cause of credit failure and business failure

associated with small business is the lack of cash flow planning. They run out of cash

and it usually comes as a surprise to them. (turn to slide 34)

Some keys to SME credit evaluation of course involve looking at the industry and

the business plan; examining the sources and uses of cash flow; and where possible,

keeping an eye on the collateral. Who wouldn’t want to have an account like the

proverbial golden egg? More so than the golden egg, you’ve got to find the goose that

lays it. (turn to slide 35)

And part of that analysis also involves looking at liquidity and solvency ratios,

activity ratios, and leverage ratios . But the quality of the derived ratios is directly

proportional to the quality of the information plugged into those ratios!

So the challenge comes down to validation, validation, validation!

Character and competence of the principals or business owners remain key

validation items.

Character without competence means that one will have a well-meaning

borrower with a soured loan.

Competence without character means that one will have a sharp borrower that

will take advantage of various ways to evade debt repayment, perhaps.

You can make all of the site visits, background checks, interviews, and lifestyle

checks that you want. Success in validation comes down to knowing three things:




• Does the principal or owner show total command of the information

regarding his industry and his business?

• Does the principal or owner have a sterling record with his suppliers,

customers, and other bankers or credit providers?

• Does he live within his means? Does he show a sober, meticulous

attitude towards his lifestyle? That same sober attitude will be reflected

in how he handles his business and money affairs. (turn to slide 36)

Other useful metrics exist. Aside from the qualitative validation, one can also

track the cash burn ratio and the cash as a percent of interest bearing debt.

Cash as a percent of interest bearing debt is self explanatory. Major lenders who

also accept deposits of the small business borrower have the advantage here, since

they can look at their records in real time. One can track the seasonality of the

business’s cash build up, the seasonality of cash requirements, and immediately red flag

the situation if adverse information deviating from the norm emerges.

As far as cash burn, this simply answers the question: how many days of cash

does this firm have at any given time? This is a very useful metric, because one of the

ways to use this is to pair it up with the analysis of the activity ratios. One can see

immediately if the borrower is suffering from adverse business developments.

Abnormally low cash balances in the face of normal business conditions is a red flag,

especially for small businesses. (turn to slide 37)




A lengthy discussion of corporate credit is not necessary, but I’d like to highlight

some key points.

Of course in evaluating corporate credit one has to look at the industry and the

business of the corporate customer.

One item that I would like to highlight concerns the power of knowing the

accounting behind the corporate numbers.

The assumption is that this field does not require the same level of qualitative

validation as the small business, and that the numbers delivered have been audited as

well.

In this scenario we’ve always got to remember that income performance varies

from cash flow performance. Revenue earned is not the same as revenue collected!

(turn to slide 38)

To my mind, one powerful equation in corporate credit is assets plus expenses

equals liability plus equity plus revenues. We have many reasons to thank the catholic

religious – one of these reasons is that catholic religious invented double entry

bookkeeping, which evolved into modern accounting.

I’d like everyone to pause and think about knowing the implication of this

equation. The equation is not complicated; it is elegant in its simplicity but powerful at

the same time. (turn to slide 39)

Using this equation, one can determine the




• Quality of revenue.

• Proper matching of expenses.

• Misreported balance sheet valuations.

• Quality of cash flow.

It all points to getting to a level of comfort with the numbers reported by the customer,

and being able to detect when things are out of whack.

• Is the customer recognizing revenue properly?

• Are expenses being accounted for properly? Are they playing around with

depreciation assumptions, changing allowances for uncollectible accounts?

• Are they delaying writing down inventory? Are they understating or not

recording costs? Are they changing the percent of completion estimates

for contracted work?

• Are they showing misreported valuations on their balance sheet?

One can use the accounting tools to literally get under the hood of the engine, so to

speak, and check if the car is running as smoothly as the customer claims. It is almost

like having a third eye in terms of the credit evaluation. (turn to slide 40)

One will never replace the traditional corporate credit metrics:

• Liquidity ratios.

• Activity ratios.

• Leverage ratios.




• Return ratio analysis.

Again, credit managers ignore these metrics at their own peril. (turn to slide 41)

But within these metrics, we can still focus on individual metrics that matter.

These include:

• Working capital utilization.

• Asset turnover.

• How rich or poor margins are.

• Return on assets, and return on equity.

• Operating and free cash flow, especially with respect to interest and debt

coverage.

• Net worth to liabilities. For those publicly owned companies, market value of

equity to liabilities matters. (turn to slide 42)

In sum, I can leave you with the following notions:

• Follow the cash, since the other ways out of a credit work out problem do not

pay as well.

• Discipline in analysis and credit policy implementation wins.

• As with other forms of risk taking, the efficient management of credit risk comes

down to knowing the odds. Thank you very much for your time.

Speech Notes - Corporate Profitability Through Credit Management Efficiency

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