The Macroeconomics of Credit Scores
Tony Hughes
C
redit scores have a long and
celebrated history in the field
of credit risk management.
Ostensibly, such scores assess the
creditworthiness of individuals based on
a statistically derived formula that links
past activity in consumer credit markets
with the probability that a default will
later appear on the individual’s credit
file. According to MyFICO.com, the
FICO is affected by one’s payment
history (35%), amounts owed (30%),
length of credit history (15%), new credit
(10%), and the types of credit used
(10%). This means that factors such
as the number of recent delinquencies,
the number of recent inquiries for
new credit, and the length of time the
individual has been an active consumer
of credit are all taken into account when
calculating the risk score. These scores
then form the primary basis for lenders
in determining whether credit will be
provided or refused and the terms on
which borrowers will be obliged to repay.
Credit score factors. Over
time, credit scores have become firmly
woven into the fabric of U.S. society.
A blemished credit history can add
hundreds or even thousands of dollars to
a person’s annual interest bill. For many
others, staples such as homeownership
and the convenience of credit card
transactions will be denied because of a
poor credit score. Even when it comes
to student loan access, impinged by
the recent financial market turmoil, the
credit scores of individuals and their
parents become key determinants of
eventual educational success. Scores are
sometimes used as the basis for rejecting
job market candidates. Because of the
centrality of scores in people’s lives,
agencies responsible for their construction
and commercialization are heavily
must be unfolding in society to drive a
regulated by the U.S. government.
disconnect between average scores and
The most obvious factors omitted
the observed rate of credit defaults.
from the calculation of credit scores are
A picture of why average scores
macroeconomic. Chart 1 depicts the
are rising can be painted by looking
path of the mean Equifax Risk Score
deeper into their distribution across
3.0 for the U.S. over time. Note that
society. Chart 2 shows the proportion of
average scores declined through the
Americans with very low scores (579 and
2001 recession and subsequent jobless
below) and those with a very high score
recovery before increasing in a seemingly
(740 and up). The proportion of people
inexorable manner to the present day.
with low scores reflects, at least in part,
In the second quarter of 2008, for the
recent increases in delinquencies which
first time in the history of the series, the
have been concentrated at the subprime
average American held a credit score
end of the credit spectrum. The unusual
of 700. Back in late 2001, by contrast,
aspect of this curve, however, is the
such a score would have put you a full
shallowness of the impact of rising
13 points above the national average.
defaults and the fact that the trend to
This trend is difficult to reconcile
higher numbers of people in the lowest
with other events unfolding in credit
credit score band has petered out in
markets. On the face of it, a rising
2008. In part, this recent improvement
average credit score suggests that
is seen as being caused by the tax rebate
Americans, en masse, represent a lower
checks that were distributed mainly
credit risk than they used to, that they
in the second quarter; the first quarter
are now better at paying their bills and
improvement was caused by a related
remaining solvent. Such a conclusion
announcement effect. If this is true, the
is not supported by delinquency and
trend to increased numbers with very
default rate statistics.
In contrast, these
measures, especially
Chart 1: Aggregate Credit Scores Are at Record Highs
in, but not limited
701
3.7
to, the mortgage
ERS 3.0, mean (R)
sphere, have been
Total delinquency rate, % (L)
699
3.5
rising rapidly to the
697
point where total
3.3
delinquencies are
695
now at their highest
3.1
693
point since the 2001
recession. Though
691
2.9
credit scores and
689
delinquencies used to
2.7
run countercyclically
687
to each other, this
Source: CreditForecast.com
2.5
685
is no longer the
98
99
00
01
02
03
04
05
06
07
08
case. Something else
Moody’s Economy.com • www.economy.com • [email protected] • Regional Financial Review / September 2008
27
Chart 2: Rising Scores Driven From the Top of the Distribution
celebrated
example
0.19
0.50
of this is
credit-score
0.48
piggybacking,
0.18
ERS 3.0<580 (L)
ERS 3.0>739 (R)
where one is
0.46
listed as an
0.17
authorized
0.44
user on a
0.16
stranger’s
0.42
credit card (for
a fee) and uses
0.15
0.40
that person’s
Source: CreditForecast.com
superior credit
0.14
0.38
behavior to
98
99
00
01
02
03
04
05
06
07
08
boost the
perception of
one’s own.
poor credit should resume during the
Credit scoring methodology does not
third and fourth quarters of 2008.
explicitly account for this type of strategic,
endogenous behavior. This is like the
A further puzzle is apparent when
Hawthorne Effect overlaid with the rat
one considers the top of the credit
cunning people acquire when they realize
score distribution. The proportion of
there is money to be made with little or
Americans with a 740 score or better
no effort required on their part.
has risen very sharply since the start of
As the wise guys get better at
the decade, with only a brief interlude
working
the system, word spreads and
for the 2001 recession. It is clear that
their
numbers
bulge. One should not
those with excellent credit had no
think
that
this
trend is being driven by a
difficulties, as a group, in navigating
their way through the last recession and
small number of super wise guys taking
have so far had no discernable difficulty
bold, overt actions to lift their scores. A
during the current downturn. This only
more likely driver is that many millions
explains flat credit scores for this group,
of Americans are taking small, almost
however. The upward trend in scores is
indiscernible, discreet actions to lift
another matter altogether.
their scores by the odd point or two.
One possible explanation for this
Anecdotally, I have met many people
is to imagine a group of individuals
around the country who routinely take
who will be called, for want of a better
small actions or non-actions with the
description, “wise guys.”1 Membership
sole purpose of keeping their credit
of this group is limited to those who
scores up. Even yours truly has a credit
have the incentive, means and financial
card with a zero balance; if not for the
acumen to exert a degree of control over
possible impact on score, it would
their personal credit score. For example,
have been cancelled many moons ago.
it is well known that, all else being equal,
The existence or non-existence of this
it is generally better for your score to
account has effectively no bearing on my
have a higher credit limit on a credit
underlying credit risk.
card. A wise guy will therefore strategize,
Those further down the score
opting to increase their limit whenever
spectrum will not get as great a boost;
the bank makes them such an offer. The
similar actions by this group would be
customer has no need for extra credit in
more likely to backfire and increase
this instance; their only derived benefit
both their perceived and actual credit
is measured in additional credit score
risk. Gaming the system does not work
points. The wise guy is in no way a
if doing so causes you to default. The
better credit risk than she was previously,
rise in average credit scores, therefore,
despite what the individual’s credit
is driven by highly capable individuals
score happens to say about her. Another
with liquid finances and with large credit
scores from the outset.
Modeling this type of behavior is
1
difficult in the extreme. The capacity of
The term is used in a gender-neutral manner.
28
individuals to take advantage of loopholes
is certainly finite; eventually all the easy
strategies will be fully utilized, leaving
only risky strategies that have a marginal
effect on scores. At the steady state, credit
scoring methodology as it currently exists
should therefore perform acceptably,
although lenders may have to adjust their
treatment of high-score individuals. A
760 wise guy is by definition a greater
credit risk than a 760 who does not game
the system. The process by which the
pool of potential wise guys is gradually
exhausted could be thought of as a
diffusion process modeled with some sort
of logistic curve. Inferring the point of
inflection—where the take-up rate starts
to decelerate—would be, however, a very
challenging endeavor.
Other answers to the puzzle come
from considering the factors other
than delinquencies and defaults that
affect an individual’s credit score. For
instance, in general, those with dense
credit files—lots of trades—have higher
scores than those with thin credit files.
This suggests that increased credit
utilization, especially at the top of the
score distribution, could be a factor
driving rising credit scores. This type
of behavior might be proxied by the
generational fall in the U.S. personal
savings rate witnessed over the past
two decades but which now seems
to be stabilizing and even improving
slightly. One can posit that those with
meager savings will have a higher credit
requirement and thus more trades in
the consumer credit industry than they
otherwise would have. So long as this
activity does not translate into increased
defaults, it will tend to support higher
and higher average credit scores. Once
again, however, it would be folly to
suggest that razor-thin personal savings
actually reduce aggregate credit risk.
What is predictive at the individual
loan level does not necessarily translate
into sustainable outcomes at the
macroeconomic level.
Another possible culprit is the
payment hierarchy, which describes
the preferences people have in terms of
what to default on first. Keep in mind
that prior to the current episode, even
in subprime, mortgage delinquencies
and foreclosures were relatively rare
events caused by joblessness, health
problems and divorce. Until 2006,
Moody’s Economy.com • www.economy.com • [email protected] • Regional Financial Review / September 2008
total mortgage delinquencies stayed in
a narrow band just above 2% in terms
of total value, but this has since risen
sharply so that now about 4.5% of all
mortgages, by value, have a delinquent
status. Auto delinquencies have also risen
recently, though not nearly as sharply as
mortgages; in 2007, for the first time,
the auto delinquency rate fell below the
mortgage rate and the gap between the
two measures has continued to increase.
To put it into context, the mortgage
delinquency rate is now approaching, on
a trend basis, the credit card delinquency
rate; during the last recession, card
delinquency was more than double the
mortgage delinquency rate relative to the
amounts outstanding in each category.
This shuffling of the payment
hierarchy holds the potential to upset
credit scoring methodology. With the
regression technology used to develop
individual credit scores, input variables
with relatively little variability tend, all
else being equal, to be statistically weaker
predictors than variables that vary widely
across disparate individuals. Since
mortgage delinquencies were rarer, they
have a lower variance, by definition, than
credit card delinquencies, which, until
recently, were far more common. This
does not upset the scoring methodology
so long as mortgage delinquencies remain
rare. But if, overnight, such events
increase in frequency, the measured
impact of mortgage delinquencies on the
probability of future default will tend to
be understated.
The final unusual phenomenon that
is currently unfolding is a sharp rise in the
balances held by the community on their
collective credit cards. In previous instances
of this, the root cause was generally elevated
confidence leading to increased use of the
fantastic plastic for luxury purchases. One
would be hard pressed, given that consumer
confidence is close to decade-low levels and
that consumer spending remains weak, to
ascribe this phenomenon to bullishness
at the cash register. A more watertight
explanation involves a rise of desperation
credit card utilization as the ability to extract
home equity for consumption has eroded
through the housing bust. Again it is
conceivable that scoring models have been
tricked by the emergence of this trend. It
would be hard to avoid the conclusion that
desperation card use would increase credit
risk at the aggregate level, but if scoring
models mistakenly believe that rising
utilization is a positive sign, scores may be
rising at a time when delinquencies and
defaults are also on the increase.
Why do these trends matter? One
could reasonably argue that the primary
purpose of credit scores is to consistently
rank the individuals in society from lowest
risk to highest. If the rise in average credit
scores is uniform, or occurs in such a
way as to retain the existing order of the
individuals in society, then, arguably,
the distortions discussed above do not
matter. Since scores at the top end of the
distribution are accelerating ahead of scores
further down, there is no evidence that
leapfrogging is occurring as a consequence
of the distortions. If, however, bulging
numbers of wise guys are the root cause
of rising scores, leapfrogging would be
occurring, with wise guys jumping ahead
of lower credit risk individuals just because
they have the knowledge.
Even if this explanation does not
cut the mustard, though, the distortions
still matter.
The logistics of volume credit
decisioning require concrete rules for
acceptance and rejection and the terms
on which credit will be provided to those
whose applications pass muster. The way
this typically happens is that managers,
presumably as high up as the CFO and
CEO, make decisions about the direction
in which the portfolio is headed and
adjust the parameters by which it is
created and managed accordingly. These
parameters are then fed into computers
so that workers on the call center floor
or their supervisors can easily make
decisions consistent with the parameters
established by management.
The credit score is a key cog in this
machine. A lender, for example, will have
a cutoff score at which they will not even
consider the application. This score might
be somewhere in the 500s for a subprime
auto lender and the low 700s for a primegrade mortgage provider. Someone with a
599 might get a flat rejection from a given
company while a 600 might be approved.
Conversely, a 700 might get a 6.5% fixed
rate mortgage while a 699 will only qualify
for a 6.75% loan. The simple fact is that
small distortions can have large effects on
individuals close to boundary conditions
and on the companies that establish
the boundaries. When distortions are
widespread and poorly understood, the
Moody’s Economy.com • www.economy.com • [email protected] • Regional Financial Review / September 2008
effect will either be to increase credit risk
in society relative to where it is perceived
to be or lead the finance industry to leave
millions of dollars on the table as people
are unnecessarily rejected or put off by an
unnecessarily high cost of obtaining credit.
If credit scores are rising, if defaults
do not fall and if lenders have a static
or slow-moving lending policy with
respect to credit scores, outcomes in
the consumer credit markets cannot be
optimal. The purpose of this article is
to explore recent trends in credit scores
and to suggest methods companies might
employ to cope with emerging trends
in scores in building their portfolios.
The central plank of this work involves
correcting credit scores for the effect of
external factors such as macroeconomic
cycles that are currently not considered in
score construction.
Two premises. The work outlined
below is built on two simple premises.
First and foremost, it is posited that
the distribution of credit scores, after
controlling for macroeconomic shifts
and business cycle fluctuations, should
be constant or only slowly moving over
time. This proposition is built on solid
foundations. It is not reasonable to
believe that the U.S. populace’s collective
willingness to remain current on their
debts should change from month to
month unless this willingness is affected
by economic circumstances. This
willingness could, however, undergo
longer-term shifts as preferences and
borrowing cultures change in ways that
are unrelated to the performance of
the economy. Because shifts caused by
macroeconomic fluctuations are factored
out, the remaining distribution of scores
should remain relatively stable over time.
The second premise is that,
collectively, people should not be
rewarded with greater access to credit
just because the economy has recently
boomed. Conversely, people, collectively,
should not be punished just because
external economic events such as
recessions have occurred to impact their
performance in credit markets.
The basis of this second premise is
that the end of a boom, an extended period
in which favorable economic conditions
have supported improved credit scores,
is exactly the wrong time to open the
credit floodgates. At this point, economic
circumstances for the vast majority of
29
people have peaked and can then only get
worse. The tendency at the moment is for
lenders to be hyper-aggressive during such
periods; the current problems in subprime
mortgages, however, have demonstrated
the folly of this type of business strategy.
Whether it is employed in credit cards,
auto loans or student loans makes very
little difference in terms of the underlying
principle, even though the drivers of
market behavior will differ substantially
from product to product. Conversely,
deep in recession, the situation for most
people can only improve. Prudent lending
practices, aimed at reducing credit risk
through the cycle, would suggest more
aggressively seeking out new business
during such periods.
Skeptics may, at this point, correctly
suggest that the supply of credit is more
difficult and the demand for new or bigger
credit lines is never present during such
recessionary periods. Moody’s Economy.
com’s response is that though this is
true, it is not the role of credit scores to
further exacerbate a deepening decline in
access to or demand for new credit during
recessions. A credit score that reflects
economic reality should always be better
than one that does not. Further, there
remains a strong argument to suggest
that, although times are tough, lenders
should be more aggressive where it is
possible at the margin during economic
downturns when only the best credit risk
individuals, after controlling for credit
scores, are actively seeking out new lines
or extending old ones.
Data and method. The data
used for the empirical work is drawn
from CreditForecast.com and from the
extensive macroeconomic databases of
Moody’s Economy.com. CreditForecast.
com data are based on a 5% random
sample of all credit files housed by
Equifax. Included are data describing the
proportion of individuals with an ERS 3.0
credit score within a number of distinct
bands. The bands included are 280-579,
eight bands in 20-point increments up to
739, and then a band for the high score
740-850 group. This set of bands is
exhaustive, meaning that the sum of the
proportions for a given time period can
be fixed at unity. The data are quarterly
and cover the period from the beginning
of 1998 up to the first quarter of 2008.
The other data dimension Moody’s
Economy.com has is spatial. The credit
30
score band proportions for the top 200
metropolitan statistical areas in the U.S.
as well as 50 rest-of-state areas are used.
This means that the Moody’s Economy.
com panel data set covers the entire U.S.
with consistent data on 250 distinct
subregions. The main benefit of this type
of analysis is that differential business
cycle timing and amplitude across the
heterogeneous U.S. economy can be
considered. Even during the current
recession, for example, places such as
Texas and other energy- and exportdependent regions are performing rather
better than areas that have been hit hard
by the housing downturn.
The economic drivers used to model
how credit scores change through the
cycle include labor market factors, house
prices, personal household income, retail
sales, and an estimated figure for GDP
within the region. Moody’s Economy.com
bolsters these regional series with national
macro data, including the personal
savings rate (mentioned above) and the
federal funds interest rate. These factors
are allowed to influence credit scores
both contemporaneously and with lags;
it is assumed that macroeconomic trends
evolve exogenously, implying that credit
score fluctuations do not substantively
impact the performance of the local
economy in each region.
One could reasonably imagine that
other data from CreditForecast.com
could be used to explain movements
in credit scores. Factors such as the
delinquency rates on various products,
balances outstanding, or the number of
distinct credit trades might be excellent
predictors of credit score evolution and
all are available for use in this project.
Moody’s Economy.com opts not to use
these variables, however. The primary
aim here is not merely to explain credit
score trends but to correct scores for
macro factors that are not presently
used in their construction. Factors
such as delinquencies are already
taken into account when constructing
scores; including them in the correction
models would therefore constitute
double counting. Moody’s Economy.
com restricts itself only to data that are
fundamentally external to the credit
scoring system as it currently stands.
The Moody’s Economy.com approach
to modeling the credit score distribution
is straightforward. Equations that
describe the dynamic evolution of the
proportion of accounts in each band
using the macro variables described
above while enforcing the adding-up
constraint implied by the exhaustive
nature of the bands being considered
are simply fitted. This action allows the
data to be effectively filtered into various
components, one of which constitutes
the effect of the macro economy on the
proportion of individuals within each
band. By then removing this component,
which is a function of observed historical
macro data, the proportions that
would pertain if the economy was a
constant, static or irrelevant factor in the
determination of scores can be identified.
Once the corrected score bands have
been identified, finding the correction
factor for individual scores is easy.
Suppose an individual in Albuquerque in
March 2008 with a score of 673 is at the
55th percentile of the local distribution
of uncorrected scores. From the
corrected distribution, the score that
corresponds to the 55th percentile is
identified and this constitutes the new
score corrected for economic fluctuations.
In tables and charts referred to below, the
“correction factor” is the point
differential between an individual’s
corrected and uncorrected scores.
Because each individual remains at a
constant percentile within his or her local
score distribution, the corrected scores
exactly retain the order of individuals
in a given area that was derived using
the uncorrected credit score. Though
a 399 may catch a 400 following the
correction, the 400 will not be overtaken.
The only case where these individuals
may switch places occurs if they live in
different regions experiencing different
macroeconomic conditions at the time the
correction is being made.
Results. Charts 3 and 4 illustrate
forecasts for credit score proportions
for San Francisco relative to observed
outcomes. It is fairly clear that the
forecast models do not fit the data
perfectly, but they do express the broad
trends and cycles that are evident in
the series. As mentioned earlier, if
forecasting was the sole aim of the
analysis, other credit variables and
forecasts from CreditForecast.com would
be incorporated, and thus estimates that
match the observed proportions more
closely would be computed.
Moody’s Economy.com • www.economy.com • [email protected] • Regional Financial Review / September 2008
Chart 3: Proportion With Low Scores Will Peak Later This Year Chart 4: The Top-End Forecast Depends on the Savings Rate
Proportion with ERS 3.0>739, San Francisco
Proportion with ERS<580, San Francisco
0.61
0.11
0.60
0.59
0.10
Forecast
0.58
Forecast
0.57
0.09
0.56
0.55
0.08
Actual
0.54
Actual
Source: CreditForecast.com
Source: CreditForecast.com
0.53
0.52
0.07
00
01
02
03
04
05
06
07
08
09
The forecasts suggest that the
proportion of people with ERS 3.0
scores below 580 will rise to about 10%
in late 2008 before stabilizing close to
current levels above 8% when economic
conditions eventually settle down. The
trend in high scores, meanwhile, is being
modeled using personal savings rates,
which are expected to now stabilize. As a
result, the proportion of San Franciscans
with a high score is expected to level off
at around 58% of the local credit active
population. This forecast relies on the
savings rate being the correct driver of
rising scores. This is doubtful, especially
if some of the other explanations for the
trend discussed in the introduction turn
out to be true. As such, this prediction
should be taken with a grain of salt.
In terms of correcting the scores,
meanwhile, whether the savings rate is
the correct driver of rising scores at the
top of the distribution is largely irrelevant;
the sole aim of Moody’s Economy.com
10
11
12
13
00
01 02
03 04
in this case is to correct the trend by
factoring out its impact. This can be done
perfectly well using either the savings
rate or a simple linear time trend. These
two variables have been highly positively
correlated over the period under analysis
here so using either factor will have
approximately the same effect on the final
corrected score.
Charts 5 and 6 illustrate the
correction factors relevant in San
Francisco for individuals with different
credit scores. Two periods are used to
provide the illustration: June 2000, the
dying days of the tech boom and the
subsequent booming local economy; and
December 2002, when the economy was
struggling in the wake of recession and
the jobless recovery that followed.
For those with scores under 680,
the calculated correction factors strongly
“lean against” prevailing economic
circumstances. Corrections tend to help
people achieve higher scores during the
05
06 07
08 09
10 11
12
13
recession and penalize during the boom.
For those with high scores, meanwhile,
the recession and boom have little impact
on underlying credit behavior, and thus
correction factors are not required to “lean
against” prevailing local macroeconomic
conditions through the cycle. Instead,
the trend toward higher scores is the
key characteristic that is being modeled.
The correction factors neutralize this
trend, leading to smaller corrections that
often run counter to those observed for
individuals with lower scores.
The size of the corrections amounts
to a maximum of a 6-point penalty for
those with 580 credit scores in mid2000 and a bonus of 12 points for the
same individuals in December 2002.
This means that those who maintain a
consistent ERS 3.0 of 580 through this
period will see an 18-point swing in their
corrected scores when the effect of the
economy is taken into account. Though
these corrections seem modest in the
Chart 5: Credit Scores Can Be Corrected for Economic Factors
Correction factor, San Francisco, June 2000
Chart 6: Corrections Change Through the Business Cycle
Correction factor, San Francisco, December 2002
5
4
3
2
1
0
-1
-2
-3
-4
-5
-6
-7
14
Source: Moody's Economy.com
Source: Moody's Economy.com
12
10
8
6
4
2
0
-2
-4
280
340
400
460
520
580
640
700
760
820
280
340
400
Moody’s Economy.com • www.economy.com • [email protected] • Regional Financial Review / September 2008
460
520
580
640
700
760
820
31
Chart 7: Corrections Change Across Different Regions
Correction factor, Miami, September 2006
Chart 8: Corrections Support Higher Lending in Tough Times
Correction factor, Miami, March 2006
2
12
10
0
8
6
-2
4
2
-4
0
-2
-6
Source: Moody's Economy.com
-4
Source: Moody's Economy.com
-6
-8
280
340
400
460
520
580
640
context of individuals, from lenders’
perspectives they are far more substantive.
Remember that we are talking about
altering the scores of hundreds of
thousands of San Franciscans—and
tens of millions of Americans—while
recession is raging or when the economy
is booming. It means that a local
lender with a baseline cut-off score
of 580 should instead use 570 when
the economy is lousy or 586 when the
economy is doing better than its potential.
Charts 7 and 8 show similar relations
for Miami in September 2006 and March
2008. During the booming housing
market in 2006, correction factors are
unambiguously negative, with no scores
receiving a boost and many being penalized.
One interesting point is that significant
corrections kick in at a fairly high credit
score—above 640. These corrections
would have helped ameliorate the building
subprime crisis, but not by as much as
might have been expected, especially since
most of the mortgage problems were in
the subprime space. Current credit score
recession bonuses, meanwhile, are centered
on subprime borrowers, meaning that these
are the individuals currently most acutely
impacted by external economic events,
through no collective fault of their own.
Conclusion. Credit scores are
often taken for granted in the credit risk
assessment and loss forecasting field.
Those with higher scores are routinely
assumed to be lower risk than those with
blemishes on their credit files, which is
almost always a reasonable conclusion.
The results of this article should,
32
700
760
820
280
340
400
however, give pause to those who use
scores as the be all and end all of credit
risk assessment. Rising credit scores
across the population at a time when
defaults are also rising sharply suggest
that the mapping between credit scores
and the underlying probability of default
is breaking down. This can be so even
when scores are fulfilling their primary
aim of correctly ordering the individuals
in society from lowest to highest risk.
Those in the industry who use
scores in a static or slow moving manner
when engaged in credit decision systems
should be most acutely alarmed by the
findings presented here. Believing that a
700 now is the same as a 700 in January
or a 700 during the housing boom is
extremely myopic. The results of this
article indicate that credit score shifts
can be well explained by fluctuations in
the underlying macro economy. These
economic effects can then be forecasted
and their effect can be readily filtered
out from individual level credit scores. A
lender who is concerned about vintage
quality fluctuations that are not well
explained by credit score dynamics would
be well advised to institute corrected
scores like those developed here. Systems
that employ macro corrected credit scores
would be identical to those currently
used across the consumer credit industry.
Cultural payment habits will still evolve
over time, but these shifts will be glacial in
comparison with economic changes that
influence scores from month to month.
Finally, validation of these corrected
scores is an open issue that Moody’s
460
520
580
640
700
760
820
Economy.com is vigorously exploring.
The corrections hold the potential to
improve the ordering of individuals
because a 599 in a recession-bound
area will be moved above a 600 in a
region that has recently boomed. If this
is consistent with relative credit risk in
the two areas—if the 599 is actually
a better forward-looking risk than the
600—improvements in ordinal predictive
ability should naturally follow.
Validation in this instance should
not be confined to how well risk ordering
is achieved, however. A second question
concerns whether a specific credit score,
say 600, maps to a consistent probability of
default through the business cycle. Most
lenders assume—with little conviction—that
this probability is constant in the short term,
although most recognize that probabilities
of default can change in the longer term for
a stated credit score. Incidents such as the
subprime crisis starkly point out the reality
that lenders are slow to adjust to changing
dynamics in the economy. Improved
intelligence regarding every aspect of the
decisioning process is needed if repeat
occurrences are to be avoided in either
the mortgage industry or in other areas
of consumer finance. It would be vastly
preferable for the industry in this regard if a
700 meant the same thing for a longer period
of elapsed time as economic events evolve.
This second type of validation,
often overlooked in the scoring industry,
would almost certainly show substantial
improvements for the macro corrected credit
scores over their more static, individually
determined, existing counterparts.
Moody’s Economy.com • www.economy.com • [email protected] • Regional Financial Review / September 2008
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Moody’s Economy.com • www.economy.com • [email protected] • Regional Financial Review / September 2008