How to create decision strategies for small business lending

Some of you may be thinking finally we get to the meat of the matter.  Yes the decision strategies are extremely important when we talk about small business/business banking.  Just remember how we got to here though, we had to first define:

Without the above, we can create all the decision strategies we want but their ultimate effectiveness will be severely limited as they will not have a foundation based upon a successful execution.

First we are going to lay the foundation for how we are going to create the decision strategy.  The next blog post (yes, there is one more!) will get into some more specifics.  With that said, it is still important that we go through the basics of establishing the decision strategy.

These are not the same as investments.

Decision strategies based upon scorecards

We will not post the same disclosure as do the financial reporting of public corporations or investment solicitations.  This is the standard disclosure of “past performance is not an indication of future results”.  On the contrary, for scorecards, past performance is an indication of future results.  Scorecards are saying that if all conditions remain the same, future results should follow past performance.  This is the key.

We need to fully understand what the expected results are to be for the portfolio originated using the scorecard.  Therefore we need to understand the population of applications used to develop the scorecards, basically the information that we had available to generate the scorecard.  This will tie directly with the information that we required of the applications to be submitted.

As we understand the type of applications that we are taking from our client base we can start to understand some expected results. By analyzing what we have processed in the past we can start to build about model for the expected results going forward. Learn from the past and try not to repeat the mistakes we made.

First we take a look at what we did approve and analyze the resulting performance of the portfolio. It is important to remember that we are not to be looking for the ultimate crystal ball rather a model that can work well to predict performance over the next 12 to 18 months. Those delinquencies and losses that take place 24, 36, 48 months later should not and cannot be tied back to the information that was available at the time we originated the credit. We will talk about how to refresh the score and risk assessment in a later blog on portfolio management.

As we see what was approved and demonstrated acceptable performance we can now look back at those applications we processed and see if any applications that fit the acceptable profile were actually declined. If so, what were the reasons for the declinations?  Do these reasons conflict with our findings based upon portfolio performance? If so, we may have found some additional volume of acceptable loans. I say "may" because statistics by themselves do not tell the whole story, so be cautious of blindly following the statistical data. My statistics professor in college drilled into us the principle of "correlation does not mean causation".  Remember that the next time a study featured on the news.  The correlation may be interesting but it does not necessarily mean that those factors "caused" the result.  Just as important, challenge the results but don't use outliers to disprove here results or the effectiveness of the models.

Once we have created the model and applied it to our typical application population we can now come up with some key metrics that we need to manage our decision strategies:

    Expected score distributions of the applications

    Expected approval percentage

    Expected override percentage

    Expected performance over the next 12-18 months

Expected score distributions

We build the models based upon what we expect to be the population of applications we process going forward. While we may target market certain segments we cannot control the walk-in traffic, the referral volume or the businesses that will ultimately respond to our marketing efforts. Therefore we consider the normal application distribution and its characteristics such as 1) score; 2) industry; 3) length of time in business; 4) sales size; etc.  The importance of understanding and measuring the application/score distributions is demonstrated in the next few items.

Expected approval percentages

First we need to consider the approval percentages as an indication of what percent of the business market to which we are extending credit. Assuming we have a good representative sample of the business population in the applications we are processing we need to determine what percentile of businesses will be our targeted market. Did our analysis show that we can accept the top 40%? 50%?  Whatever the percentage, it is important that we continue to monitor our approval percentage to determine if we are starting to get too conservative or too liberal in our decisioning. I typically counsel my client that “just because your approval percentage is going up is not necessarily an improvement!”  By itself an increase in approval percentage is not good.  I'm not saying that it is bad just that when it goes up (or down!) you need to explain why. Was there a targeted marketing effort?  Did you run into a short term lucky streak? OR is it time to reassess the decision model and tighten up a bit? 

Think about what happens in an economic expansion. More businesses are surviving (note I said surviving not succeeding). Are more businesses meeting your minimum criteria?  Has the overall population shifted up?  If more businesses are qualifying but there has been no change in the industries targeted, we may need to increase our thresholds to maintain our targeted 50% of the market. Just because they met the standard criteria in the expansion does not mean they will survive in a recession. "But Joel, the recession might be more than 18 months away so we have a good client for at least 18 months, don't we?". I agree but we have to remember that we built the model assuming all things remain constant. Therefore if we are confident that the expansion will continue at the same pace infinitum, then go ahead and live with the increased approval percentage.  I will challenge you that it is those applicants that "squeaked by" during the expansion that will be the largest portion of the losses when the recession comes.

I will also look to investigate the approval percentages when they go down.  Yes you can make the same claim that the scorecard is saying that the risk is too great over the next 12-18 months but again I will challenge that if we continue to provide credit to the top 40-50% of all businesses we are likely doing business with those clients that will survive and succeed when the expansion returns.  Again, do the analysis of “why” the approval percentage declined/dropped.

Expected override percentage

While the approval percentage may fluctuate or stay the same, another area to be reviewed is that of the override.  Overrides can be score overrides or a decision override.  Score override would be contradicting the decision that was recommended based upon the score and/or overall decision strategy.  Decision override would be when the market/field has approval authority and overturns the decision made by the central underwriting group.  Consequently you can have a score override, a decision override or both.  Overrides can be an explanation for the change in approval percentages.  While we anticipate a certain degree of overrides (say around 5%), should the overrides become too significant we start to lose control of the expected outcomes of the portfolio performance.  As such we need to determine why the overrides have increase (or potentially decrease) and the overrides impact on the approval percentage.  We will address some specifics around override management in a later blog.  Suffice to say, overrides will always be present but we need to keep the amount of overrides within tolerances to be sure we can accurate assess future performance.

Expected performance over next 12-18 months

The measure of expected performance is at minimum the expected probability/propensity of repayment.  This may be labeled as the bad rate or the probability of default (PD).  In a nutshell it is the probability that the credit facility will be a certain level of delinquency over the next 12-18 months.  What the base level expected performance based upon score is not the expected “loss” on the account.  That is a combination of the probability of default combined with the expected loss given event of default.

For the purpose of this post we are talking about the probability of default and not the loss given event of default.  For reinforcement we are simply talking about the percentage of accounts that go 30 or 60 or 90 days past due during the 12 – 18 months after origination.

So bottom line, if we maintain a score distribution of the applications processed by the financial institution, maintain the approval percentage as well as the override percentages we should be able to accurately assess the future performance of the newly originated portfolio.

Coming up next… A more tactical discussion of the decision strategy

 

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