Decision Analytics | Loan Origination

--by Kelly Kent

I’ve recently been hearing a lot about how bankcard lenders are reacting to changes in legislation, and recent statistics clearly show that lenders have reduced bankcard acquisitions as they retune acquisition and account management strategies for their bankcard portfolios. At this point, there appears to be a wide-scale reset of how lenders approach the market, and one of the main questions that needs to be answered pertains to market-entry timing: Should a lender be the first to re-enter the market in a significant manner, or is it better to wait, and see how things develop before executing new credit strategies? I will dedicate my next two blogs to defining these approaches and discussing them with regard to the current bankcard market.

Based on common academic frameworks, today’s lenders have the option of choosing one of the following two routes: becoming a first-mover, or choosing to take the role of a secondary or late mover. Each of these roles possess certain advantages and also corresponding risks that will dictate their strategic choices:

The first-mover advantage is defined as “A sometimes insurmountable advantage gained by the first significant company to move into a new market.” (1)  Although often confused with being the first-to-market, first-mover advantage is more commonly considered for firms that first substantially enter the market. The belief is that the first mover stands to gain competitive advantages through technology, economies of scale and other avenues that result from this entry strategy. In the case of the bankcard market, current trends suggest that segments of subprime and deep-subprime consumers are currently underserved, and thus I would consider the first lender to target these customers with significant resources to have ‘first-mover’ characteristics.

The second-mover to a market can also have certain advantages: the second-mover can review and assess the decisions of the first-mover and develops a strategy to take advantage of opportunities not seized by the first-mover. As well, it can learn from the mistakes of the first-mover and respond, without having to incur the cost of experiential learning and possessing superior market intelligence.

So, being a first-mover and second-mover can each have its advantages and pitfalls. In my next contribution, I’ll address these issues as they pertain to lenders considering their loan origination strategies for the bankcard market.

(1) http://www.marketingterms.com/dictionary/first_mover_advtanage

--Kelly Kent

In a recent presentation conducted by The Tower Group, “2010 Top 10 Business Drivers, Strategic Responses, and IT Initiatives in Bank Cards,” the conversation covered many of the challenges facing the credit card business in 2010.  When discussing the shift from “what it was," to “what it is now” for many issues in the card industry, one specific point caught my attention – the perception of unused credit lines – and the change in approach from lenders encouraging balance load-up to the perception that unused credit lines now represent unknown vulnerability to lenders.

Using market intelligence assets at Experian, I thought I would take a closer look at some of the corresponding data credit score profile trends to see what color I could add to this insight. Here is what I found:

• Total unused bankcard limits have decreased by $750 billion from Q3 2008 to Q3 2009
• By risk segment, the largest decline in unused limits has been within the VantageScore® A consumer – the super prime consumer – where unused limits have dropped by $420 billion
• More than 82 percent of unused limits reside with VantageScore A and B consumers – the super-prime and prime consumer segments

So what does this mean to risk management today? If you subscribe to the approach that unused limits now represent unknown vulnerability, then this exposure does not reside with traditional “risky” consumers, rather it resides with consumers usually considered to be the least risky. 

So this is good news, right? Well, maybe not.

Vintage analysis of recent credit trends shows that vulnerability within the top score tiers might represent more risk than one would suspect. Delinquency trends for VantageScore A and B consumers within recent vintages (2006 through 2008) show deteriorating rates of delinquency from each year’s vintage to the next. Despite a shift in loan origination volumes towards this group, the performance of recent prime and super-prime originations shows deterioration and underperformance against historical patterns.

If The Tower Group’s read on the market is correct, and unused credit now represents vulnerability and not opportunity, it would be wise for lenders to reconsider where and how yesterday’s opportunity has become today’s risk.

--by Jeff Bernstein

In my last blog, I discussed the basic concept of a maturation curve, as illustrated below:

Exhibit 1

In Exhibit 1, we examine different vintages beginning with those loans originated by year during Q2 2002 through Q2 2008. The purpose of the vintage analysis is to identify those vintages that have a steeper slope towards delinquency, which is also known as delinquency maturation curve.

The X-axis represents a timeline in months, from month of origination.  Furthermore, the Y-axis represents the 90+ delinquency rate expressed as a percentage of balances in the portfolio.  Those vintage analyses that have a steeper slope have reached a normalized level of delinquency sooner, and could in fact, have a trend line suggesting that they overshoot the expected delinquency rate for the portfolio based upon credit quality standards.

So how can you use a maturation curve as a useful portfolio management tool?

As a consultant, I spend a lot of time with clients trying to understand issues, such as why their charge-offs are higher than plan (budget).  I also investigate whether the reason for the excess credit costs are related to collections effectiveness, collections strategy, collections efficiency, credit quality or a poorly conceived budget.

I recall one such engagement, where different functional teams within the client’s organization were pointing fingers at each other because their budget evaporated. One look at their maturation curves and I had the answers I needed. I noticed that two vintages per year had maturation curves that were pointed due north, with a much steeper curve than all other months of the year. Why would only two months or vintages of originations each year be so different than all other vintage analyses in terms of performance?

I went back to my career experiences in banking, where I worked for a large regional bank that ran marketing solicitations several times yearly. Each of these programs was targeted to prospects that, in most instances, were out-of-market, or in other words, outside of the bank’s branch footprint.

Bingo! I got it! The client was soliciting new customers out of his
market, and was likely getting adverse selection. While he targeted the “right” customers – those with credit scores and credit attributes within an acceptable range, the best of that targeted group was not interested in accepting their offer, because they did not do business with my client, and would prefer to do business with an in-market player.

Meanwhile, the lower grade prospects were accepting the offers, because it was a better deal than they could get in-market. The result was adverse selection...and what I was staring at was the "smoking gun" I’d been looking for with these two-a-year vintages (vintage analysis) that reached the moon in terms of delinquency.

That’s the value of building a maturation curve analysis – to identify
specific vintages that have characteristics that are more adverse than others.  I also use the information to target those adverse populations and track the performance of specific treatment strategies aimed at containing losses on those segments. You might use this to identify which originations vintages of your home equity portfolio are most likely to migrate to higher levels of delinquency; then use credit bureau attributes to identify specific borrowers for an early lifecycle treatment strategy.

As that beer commercial says – “brilliant!”

 

--by Jeff Bernstein

In the current economic environment, many lenders and issuers across the globe are struggling to manage the volume of caseloads coming into collections. The challenge is that as these new collection cases come into collections in early phases of delinquency, the borrower is already in distress, and the opportunity to have a good outcome is diminished.

One of the real “hot” items on the list of emerging best practices and innovating changes in collections is the concept of early lifecycle treatment strategy. Essentially, what we are referring to is the treatment of current and non-delinquent borrowers who are exhibiting higher risk characteristics.  There are also those who are at-risk of future default at higher levels than average. The challenge is how to identify these customers for early intervention and triage in the collections strategy process.

One often-overlooked tool is the use of maturation curves to identify vintages within a portfolio that is performing worse than average. A maturation curve identifies how long from origination until a vintage or segment of the portfolio reaches a normalized rate of delinquency.

Let’s assume that you are launching a new credit product into the marketplace. You begin to book new loans under the program in the current month. Beyond that month, you monitor all new loans that were originated/booked during that initial time frame which we can identify as a “vintage” of the portfolio. Each month’s originations are a separate vintage or vintage analysis, and we can track the performance of each vintage over time.

How many months will it take before the “portfolio” of loans booked in that initial month reach a normal level of delinquency based on these criteria: the credit quality of the portfolio and its borrowers, typical collections servicing, delinquency reporting standards, and factor of time?  The answer would certainly depend upon the aforementioned factors, and could be graphed as follows:

Exhibit 1

 
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--by Kelly Kent

When reviewing offers for prospective clients, lenders often deal with a significant amount of missing information in assessing the outcomes of lending decisions, such as:

• Why did a consumer accept an offer with a competitor?
• What were the differentiating factors between other offers and my offer, i.e. what were their credit score trends?
• What happened to consumers that we declined? Do they perform as expected or better than anticipated?
• What were their credit risk models?

While lenders can easily understand the implications of the loans they have offered and booked with consumers, they often have little information about two important groups of consumers:

1. Lost leads: consumers to whom they made an offer but did not book
2. Proxy performance: consumers to whom financing was not offered, but where the consumer found financing elsewhere

Performing a lost lead analysis on the applications approved and declined, can provide considerable insight into the outcomes and credit performance of consumers that were not added to the lender’s portfolio.

Lost lead analysis can also help answer key questions for each of these groups:

• How many of these consumers accepted credit elsewhere?
• What were their credit attributes?
• What are the credit characteristics of the consumers we're not booking?
• Were these loans booked by one of my peers or another type of lender?
• What were the terms and conditions of these offers?
• What was the performance of the loans booked elsewhere?
• Who did they choose for loan origination?

Within each of these groups, further analysis can be conducted to provide lenders with actionable feedback on the implications of their lending policies, possibly identifying opportunities for changes to better fulfill lending objectives. Some key questions can be answered with this information:

• Are competitors offering longer repayment terms?
• Are peers offering lower interest rates to the same consumers?
• Are peers accepting lower scoring consumers to increase market share?

The results of a lost lead analysis can either confirm that the competitive marketplace is behaving in a manner that matches a lender’s perspective.  It can also shine a light into aspects of the market where policy changes may lead to superior results. In both circumstances, the information provided is invaluable in making the best decision in today’s highly-sensitive lending environment.

 

There are a lot of areas covered in your comment: efficiency; credit quality (human side or character in an impersonal environment); and policy adherence. 

We define efficiency and effectiveness using these metrics:

• Turnaround time from application submission to decision;
• Resulting delinquencies based upon type of underwriting (centralized vs. decentralized);
• Production levels between centralized and decentralized;
• Performance of the portfolio based upon type of underwriting; and
• Turnaround time from application submission to decision

Due to the nature of Experian’s technology, we are able to capture start and stop times of the typical activities related to loan origination.  After analyzing the data from 160+ financial institutions of all sizes, Experian publishes an annual small business benchmark report that documents loan origination process efficiencies and inefficiencies, benchmarking these as industry standards.  

Turnaround Time

From the benchmark report, we’ve seen that institutions that are centralized have consistently had a turnaround time that is half of those with decentralized environments.

Interestingly, turnaround time is also much faster for the larger institutions than for smaller.  This is confusing because the smaller community banks tend to promote the close relationship they have with their clients and their communities. Yet, when it comes to actually making a loan decision, it tends to take longer.

In addition to speed, another aspect of turnaround is consistency.  We all can think of situations where we were able to beat the stated turnaround times of the larger or the centralized institutions.  Unfortunately, these tend to be isolated instances versus the consistent performance that is delivered in the centralized environment.

Resulting delinquencies based upon type of underwriting/Performance of the portfolio based upon type of underwriting

Again, referring to the annual small business lending benchmark report, delinquencies in a centralized environment are 50% of those in a decentralized environment. 

I have worked with a number of institutions that allow the loan officer/relationship manager to “reverse the decision” made by a centralized underwriting group.  The thinking is that the human aspect is otherwise missing in centralized underwriting.  When the data is collected, though, the incremental business/portfolio that is approved by the loan officer (who is close to the client and knows the human side) is not profitable from a credit quality perspective.  Specifically, this incremental portfolio typically has a net charge-off rate that exceeds the net interest margin -- and this is before we even consider the non-interest expense incurred. 

Your choice: is the incremental business critical to your success…or could you more fruitfully direct your relationship officer’s attention elsewhere?

Production levels between centralized and decentralized

Not to beat a dead horse, but the multiple of two comes into play here too.  As one looks at the throughput of each role (data entry, underwriter, relationship manager/lender), the production levels of a centralized environment are typically double that of a decentralized.

It’s clear that the data point to the efficiency and effectiveness of a centralized environment

-- by Kelly Kent

The title of this edition, ‘The risk within the risk’ is a testament to the amount of information that can be gleaned from an assessment of the performances of vintage pools.

Vintage pools offer numerous perspectives of risk. They allow for a deep appreciation of the effects of loan maturation, and can also point toward the impact of external factors, such as changes in real estate prices, origination standards, and other macroeconomic factors, by highlighting measurable differences in vintage to vintage performance.

What is a vintage pool?

By the Experian definition, vintage pools are created by taking a sample of all consumers who originated loans in a specific period, perhaps a certain quarter, and tracking the performance of the same consumers and loans through the life of each loan.

Vintage pools can be analyzed for various characteristics, but three of the most relevant are:

* Vintage delinquency, which allows for an understanding of the repayment trends within each pool;

* Payoff trends, which reflect the pace at which pools are being repaid; and

* Charge-off curves, which provide insights into the charge-off rates of each pool.

The credit grade of each borrower within a vintage pool is extremely important in understanding the vintage characteristics over time, and credit scores are based on the status of the borrower just before the new loan was originated. This process ensures that the new loan origination and the performance of the specific loan do not influence the borrower’s credit score. By using this method of pooling and scoring, each vintage segment contains the same group of loans over time – allowing for a valid comparison of vintage pools and the characteristics found within.

Once vintage pools have been defined and created, the possibilities for this data are numerous...

Much of the discussion on Capitol Hill revolves around sufficient risk-based capital and the derivation of how much tier 1 capital and/or common equity capital is appropriate. Most of our solution offerings and consulting services address various aspects of risk management, from targeting prospective customers, through loan origination and risk-based pricing, to ongoing relationship management and portfolio monitoring. We have been addressing risk management with our clients long before the recent financial and economic crises. We are both ready and able to assist new and existing clients in many ways: to effectively and efficiently address the management of credit and other risks and to develop strategies that offer optimal risk-based profit performance. We are always monitoring regulatory developments and, as always, will strive to assist our clients with new best practices to operate as effectively as possible under any new regulations affecting risk management policies, processes and governance responsibilities.

• Take a look at how your perception of small business loans has kept you from preventing problem situations.
   
• Consider migrating to a more proactive approach in your loan renewal/review process; it can have an impact on your profitability.
   
• Consider freeing up more time for your lenders and relationship managers and investigate what could be done with that time to better benefit your financial institution.

The debate continues in the banking industry -- Do we push the loan authority to the field or do we centralize it (particularly when we are talking about small business loans)?

A common argument for sending the loan authority to the field is the improved turnaround time for the applicant. However reality is that centralized loan authority actually provides a decision time almost two times faster than those of a decentralized nature.  The statistics supporting this fact are from the Small Business Benchmark Study created and published by Baker Hill, a Part of Experian, for the past five years.

Based upon the 2008 Small Business Benchmark Study, those institutions with assets of $20 billion to $100 billion used only centralized underwriting and provided decisions within 2.5 days on average. In contrast, the next closest category ($2 billion to $20 billion in assets) took 4.4 days.

Now, if we only consider the time it takes to make a decision (meaning we have all the information needed), the same disparity exists.  The largest banks using solely centralized underwriting took 0.8 days to make a decision, while the next tier ($2 billion to $20 billion) took an average 1.5 days to make a decision.  This drop in centralized underwriting usage between these two tiers was simply a 15 percent change. This means that the $20 billion to $100 billion banks had 100% usage of centralized underwriting while the $2 billion to $20 billion dropped only to 85% usage. Eighty-five percent is still a strong usage percentage, but it has a significant impact on turnaround time.

The most perplexing issue is that the smaller community banks are consistently telling me that they feel their competitive advantages are that they can respond faster and they know their clients better than bigger, impersonal banks.  Based upon the stats, I am not seeing this competitive advantage supported by reality.  What is particularly confusing is that the small community banks, that are supposed to be closest to the client, take twice as long overall from application receipt to decision and almost three times as long when you compare them to the $20 billion to $100 billion category (0.8 days) to the $500 million to $2 billion category (2.2 days).

As you can see - centralized underwriting works.  It is consistent, provides improved customer service, improved throughput, increased efficiency and improved credit quality when compared to the decentralized approach.  

In future blogs, I will address the credit quality component.

We have talked about: the creation of the vision for our loan portfolios (current state versus future state) – e.g. the strategy for moving our current portfolio to the future vision. Now comes the time for execution of that strategy.

In changing portfolio composition and improving credit quality, the discipline of credit must be strong (this includes in the arenas of commercial loan origination, loan portfolio monitoring, and credit risk modeling of course). Consistency, especially, in the application of policy is key. Early on in the change/execution process there will be strong pressure to revert back to the old ways and stay in a familiar comfort zone.  Credit criteria/underwriting guidelines will have indeed changed in the strategy execution.

In the coming blogs we will be discussing:

• assessment of the current state in your loan portfolio;
• development of the specific strategy to effect change in the portfolio from a credit quality perspective and composition;
• business development efforts to affect change in the portfolio composition; and 
• policy changes to support the strategy/vision.

The debate continues in the banking industry -- Do we push the loan authority to the field or do we centralize it (particularly when we are talking about small business loans)?

A common argument for sending the loan underwriting authority to the field is the improved turnaround time for the applicant. However, reality is that centralized loan authority actually provides a credit decisioning time almost two times faster than those of a decentralized nature.  The statistics supporting this fact are from the Small Business Benchmark Study created and published by Baker Hill, a Part of Experian, for the past five years.

Based upon the 2008 Small Business Benchmark Study, those financial institutions with assets of $20 billion to $100 billion used only centralized underwriting and provided decisions within 2.5 days on average. In contrast, the next closest category ($2 billion to $20 billion in assets) took 4.4 days.

Now, if we only consider the time it takes for decisioning (meaning we have all the information needed), the same disparity exists.  The largest banks using solely centralized underwriting took 0.8 days to make a decision, while the next tier ($2 billion to $20 billion) took an average 1.5 days to make a decision.  This drop in centralized underwriting usage between these two tiers was simply a 15 percent change. This means that the $20 billion to $100 billion banks had 100% usage of centralized underwriting while the $2 billion to $20 billion dropped only to 85% usage. Eighty-five percent is still a strong usage percentage, but it has a significant impact on underwriting turnaround time.

The most perplexing issue is that the smaller community banks are consistently telling me that they feel their competitive advantages are that they can respond faster, have consistent account management and they have better relationship management practices than bigger, impersonal banks.  Based upon the stats, I am not seeing this competitive advantage supported by reality.  What is particularly confusing is that the small community banks, that are supposed to have better relationship management, take twice as long overall from application receipt to decision and almost three times as long when you compare them to the $20 billion to $100 billion category (0.8 days) to the $500 million to $2 billion category (2.2 days).

As you can see - centralized loan underwriting processes work.  They are consistent, provide improved customer service, improve throughput, increase efficiency and improve credit quality when compared to the decentralized underwriting approach. 

In future blogs, I will address the credit quality component of loan underwriting processes.
 

“Unprecedented times,” “financial crisis,” “credit crisis” and many other terms continue to be buzzwords that we hear every day.  We are almost becoming desensitized to the terms, yet we feel the impact on a daily basis.  Everyone is waiting for some positive news in the financial services industry and more bad news keeps coming.

Each quarter we continue to read about financial institutions claiming that the worst is over. They have recognized the risk in their portfolios through risk assessment, set aside adequate reserves or loan loss allowances and are now ready to turn the corner.  Yet we continue to read about these same institutions coming back with more bad news, more credit losses and a restatement of the assurance that the problems have been recognized. As a result, this financial risk management has brought to light all of the high-risk accounts and the trend will begin to change.

Why does this story keep repeating itself? 

Reason one 
Management assesses to what extent the market (both stock market and the client base) will tolerate the level or degree of bad news, recognize losses to that extent and will then work hard to try to correct any known issues before we actually have to report the next quarter.  Unfortunately, this approach simply delays the inevitable and brings into question the risk management practices of the particular institution.  Like the boy who cried wolf, the more times you make a statement and it proves to be false, the less likely you will be believed the next time. 

Reason two
The financial institutions are actually surprised each quarter with a new batch of credit losses.  The institution, its credit management team and workout areas are diligently trying to address the current problem. But, just when they start to see the light at the end of the tunnel, a new batch of credit problems arise.  For the most part, the credit issues still persist in the high-volume, low-dollar credits such as residential mortgages, home equity loans, automobiles, credit cards and small business loans.  Due to the sheer volume of clients/loans, it becomes more difficult to assess what issues may be brewing in the portfolio.  For the large volume, small dollar portfolios, the notion of a pending credit issue comes when the delinquency starts to rise to a delinquency of 60 or 90 days. The real issue is identifying those accounts that are likely to go 60 or 90 days past due and then assess the likelihood that they will go into charge-off.

Regardless of the reason, we have a “credibility” problem in addition to a “credit” problem.
 

Part 2

To continue the discussion from my last post, we also must realize that the small business borrower typically doesn’t wait until we are ready to perform our regularly scheduled risk management review to begin to show problems.  While a delinquent payment is a definite sign of a problem with the borrower, the occurrence of a delinquent payment is often simply too late for any type of corrective action and will result in a high rate of loss or transfer to special assets.

There are additional pitfalls around the individual risk rating of the small business borrower or the small business loans; but, I won’t discuss those here.  Suffice to say, we can agree that the following holds true for portfolio risk management of small business loans:

• Active portfolio management is a must;
• Traditional commercial portfolio management techniques are not applicable due to the cost and effectiveness for the typical small business portfolio; and
• Collection efforts conducted at the time of a delinquency is too late in the process.

One last thing, the regulators are starting to place higher demands on financial institutions for the identification and management of risk in the small business portfolio.  It is becoming urgent and necessary to take a different approach to monitor that portfolio.

Just as we have learned from the consumer approach for originating the loans, we can also learn from the basic techniques used for consumer loan portfolio risk management.

We have to rely upon information that is readily available and does not require the involvement of the borrower to provide such information.  Basically, this means that we need to gather information (such as updated business scores and behavioral data) from our loan accounting platforms to provide us with an indication of potential problems.  We need to do that in an automated fashion.  From such information we can begin to monitor:

• Changes in the business score of the small business borrower;
• Frequency and severity of delinquencies;
• Balances maintained on a line of credit; and
• Changes in deposit balances or activity including overdraft activity.
  

This list is not exhaustive, but it represents a solid body of information that is both readily available and useful in determining the risk present in our small business portfolio.  With technology enabling a more automated assessment of these factors, we have laid the groundwork to develop an efficient and effective approach to small business portfolio management.  Such an approach provides real- time regular assessment of the portfolio, its overall composition and the necessary components needed to identify the potential problem credits within the portfolio.

It is past time to take a new approach toward the proactive portfolio management of our small business loan portfolio retaining the spirit of commercial credit while adapting the techniques of consumer portfolio management to the small business portfolio.

We have talked about: the creation of the vision for our loan portfolios (current state versus future state) – e.g. the strategy for moving our current portfolio to the future vision. Now comes the time for execution of that strategy.

In changing portfolio composition and improving credit quality, the discipline of credit must be strong (this includes in the arenas of commercial loan origination, loan portfolio monitoring, and credit risk modeling of course). Consistency, especially, in the application of policy is key. Early on in the change/execution process there will be strong pressure to revert back to the old ways and stay in a familiar comfort zone.  Credit criteria/underwriting guidelines will have indeed changed in the strategy execution.

In the coming blogs we will be discussing:

• assessment of the current state in your loan portfolio;
• development of the specific strategy to effect change in the portfolio from a credit quality perspective and composition;
• business development efforts to affect change in the portfolio composition; and
• policy changes to support the strategy/vision.

More to come.

The problem in the 2005 to 2007 home lending frenzy was not just granting credit to anyone who applied. It was giving loans to everyone at essentially the same price range regardless of normal credit risk scrutiny.

While “selling” financial services may be largely an art form, appropriate risk-based pricing is more of a science.

Although the financial press seemed to have discovered sub-prime lending in the last year or so, such high-risk lending isn’t new at all. It has been (and is still being) done since finance and money were invented. And, importantly, sub-prime lending has been done profitably by many lenders all along.  The secret to their success, not surprisingly, has always been risk-based pricing -- even if they didn’t call it that until recent times.

Sub-prime funding has been available in many forms and from many sources. Providers range from venture capitalists to pawn shops. It includes pay-day lenders, micro loans, tax refund loans, consumer finance companies, and even dates to Shakespeare’s merchant of Venice.

We often hear complaints that the effective rates (prices) on loans from such sources are unfairly high and predatory. The cost of that credit is high, but so is the risk of that credit. Without these kinds of sources, and their high rates, there would not be any credit granted from for-profit sources to high-risk borrowers. 

Listed firms that regularly provide pay-day loans or cash advances to sub-prime borrowers have very high gross margins and very high credit charge-offs, compared to banks. They also have much higher risk-based capital (or equity) positions that range from 40 percent to 60 percent of their average assets. This risk-based capital burden is much higher than the 8 to 10 percent found at commercial banks. So the sub-prime lenders have a significantly larger capital cushion than banks. Most of these financial results and ratios are examples of successful risk management where the credit risks are identified, managed, priced and backed by sufficient capital.

Then…along came the rose-colored greed of the housing bubble that resulted in aggressive building and selling of homes, loan originations to all (no-down, no-income, no-assets, no-problem mortgages), securities packaging and attractive ratings, and global leveraged investing -- all by prime-oriented entities and all at prime-oriented prices. Well, obviously, it didn’t work.

Risk-based pricing of mortgages would have dissuaded many home buyers to begin with… but what would we have done with all of those shiny new homes? Realistic credit models (that took into account a full credit cycle and a huge proportion of sub-prime credits) would not have rated mortgage-backed securities as AAA. Regulators that were still focused on earnings correctness (the last major snafu) should have been looking into realistic net asset values. And highly compensated investment bankers, with 30-to-1 leverage ratios, would not have gone overboard with intuitively dodgy investments. Few of these players took risk management seriously.

The new danger is that banks are doing the whole thing in reverse. They are tightening lending standards -- which is, of course, a euphemism for shutting off credit. The danger has nothing to do with so-called credit standards. It’s the general over-reaction of shutting off credit to all borrowers, again, without regard to relative risk. The latest Federal Reserve Board survey of senior loan officers paints a picture of rapid tightening to record levels.

We feel that credit standards should always improve AND that loan pricing should always proportionately reflect risk-adjusted rates and terms. Opening the flood gates and then slamming them shut is a very pro-cyclical behavior pattern on the part of bankers that doesn’t reflect a measured approach, borrower-by-borrower, using reasonable risk management at the client relationship level.

Whenever an industry encounters problems, the natural tendency is to play the blame game.  In the banking industry, credit risk managers are looking for who or what to blame for the tide of charge offs and delinquencies in their under-performing loan portfolios and in their commercial loan origination operations.  Credit scoring has definitely taken it on the chin as an easy target during 2008. 

Is credit scoring the problem? Absolutely not! 

As with anything, the more complacent we become…and the more we “turn off our brains” and stop thinking…the more risk we assume.  The more we solely rely upon the credit score alone, the more we subject ourselves to the risks inherent in “score and go” lending.

We are all well aware that credit scoring measures propensity to repay and not capacity to repay.  Over the past several years, the propensity to repay has been boosted by ever-increasing real estate values and by the refinance boom.  For example, some consumers have been able to survive on a 50 percent debt–to- income due to constant use of credit cards …by paying off those cards with a home mortgage refinance.  That set of behaviors would have shown a propensity to repay…but  was it ever acceptable to have 50 percent of your income go to debt payments?! 

Statistically it may have worked for a few years, but once real estate values stopped escalating, the problem with lack of capacity to repay reared its ugly head. 

When it comes to risk management, let’s get back to reality and sound principles.

We know that financial institutions are tightening their credit standards for lending.  But we don’t necessarily know exactly how financial institutions are addressing portfolio risk management -- how they are going about tightening those standards. 

As a commercial lender, when the economy was performing well, I found it much easier to get a loan request approved even if it did not meet typical standards.  I just needed to provide an explanation as to why a company’s financial performance was sub-par and what changes the company had made to address that performance -- and my deal was approved. 

When the economy started to decline, standards were suddenly elevated and it became much more difficult to get deals approved.  For example, in good times, credits with a 1.1:1 debt service coverage could be approved; when times got tough – and that 1.1:1 was no longer acceptable – the coverage had to be 1.25:1 or higher. 

Let’s consider this logic.  When times are good, we loosen our standards and allow poorer performing businesses’ loan requests to be approved…and when times are bad we require our clients perform at much higher standards.  Does this make sense?  Obviously not.  The reality is that when the economy is performing well, we should hold our borrowers to higher standards.  When times are worse, more leniency in standards may be appropriate, keeping in mind, of course, appropriate risk management measures.

As we tighten our credit belts, let’s not choke out our potentially good customers.  In the same respect, once times are good, let’s not get so loose regarding our standards that we let in weak credits that we know will be a problem when the economy goes south.