The conceptual framework of credit scoring from its
origins to microfinance
(draft)
Vitalie BUMACOV
Applied Research Associate, Burgundy School of Business
and Arvind ASHTA
Professor, Holder of the Microfinance Chair, Burgundy School of Business, CERMi
Abstract
The paper traces the origins of credit scoring and its evolution, trying to understand both: the
needs of lenders and how credit scoring responded to these needs. A special attention is given to
the resemblance between the problems faced when credit scoring based on a statistical technique
first appeared and the problems faced by microfinance lenders nowadays. The review of the
literature on the evolution of the technique in mainstream finance can indicate possible
developments of the technique in microfinance and vice-versa.
Content
Introduction ..................................................................................................................................... 2
Early works ..................................................................................................................................... 5
The commercial era ......................................................................................................................... 9
From consumer credit to corporate lending .................................................................................. 13
Modernism in credit scoring ......................................................................................................... 15
The new dimensions of scoring ..................................................................................................... 18
The microfinance era ..................................................................................................................... 20
Conclusions: The conceptual framework of credit scoring ........................................................... 21
Burgundy School of Business, 2011
Introduction
We trace the sketches of the conceptual framework of credit scoring back to the late 1930s in the
USA. The end of the Great Depression saw retail finance considerably increasing volumes.
Small loans, relatively high interest rates and the efforts to keep operational costs low,
constrained loan officers to increasingly base credit decisions on some mechanical rules. The
practice of using different judgmental credit rating systems was not uncommon (Plummer and
Young, 1940), although intuition and past experience were the only methods of selecting criteria
to be taken into account in the effort to identify future safe and risky borrowers.
The history of credit scoring begins with the study of David Durand in the area of consumer
instalment financing published in 1941 by the National Bureau of Economic Research - a US
nonprofit organization engaged in knowledge diffusion of how the economy works. The study
was commissioned in 1937 after observing that consumer financing faced the Great Depression
better and registered relatively small losses compared to other credit markets (Durand, 1941).
The study pioneered the use of statistics in analyzing credit risk factors in consumer financing.
The author statistically tested credit experience and intuition of 37 lending companies in
identifying bad credit risks. The purpose was to identify consistent and time-proof credit
practices. The research forged the pillars of the framework of the credit scoring techniques and
opened the way for further research.
The resemblance between the debate described in the study of Durand (1941) and the current
post-crisis debates in microfinance is intriguing and promises interesting findings. These could
be interesting equally for mainstream and microfinance practitioners, as well as for scholars. The
academic analysis of actions and reactions that led to the current framework of credit scoring
could provide many lessons, especially to the microfinance sector where the technique of credit
scoring has been struggling to enter the market for the last decade but faces resistance from
microfinance consultants who would like to differentiate the market from standard banking.
The advance and diffusion of the theory regarding statistical discrimination of populations
played a key role in the apparition of the credit scoring conceptual framework. The works of Sir
Ronald Aylmer Fisher that inspired Durand in his study are a good example. The thin link
between eugenics and fundamentals of credit scoring can inflame the debate over the ethical
raison d’être of credit scoring, especially when applied to microfinance markets.
The year 1956 was the second important step in the history of credit scoring. Fair, Isaac and
Company was founded on the principles that data can improve business decisions if used
intelligently. Two years later, this pioneer company registered the first sale of a credit scoring
system (Fair Isaac Corporation, 2010). Today it is estimated that two-thirds of world’s top 100
banks are clients of the company. From the academic view, the creation of a company and its
subsequent success didn’t profit the literature related to credit scoring, as the company’s best
practices and research findings became precious know-how that was strictly guarded and seldom
exhibited, and that too for commercial purposes.
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Five years after the first credit scoring system was sold in the US, a paper published in the
Journal of American Statistical Association noted that “numerical rating systems are not in
widespread use” (Myers and Forgy, 1963). The authors confirmed that in consumer lending,
“statistical credit scoring” represents an improvement over the judgmental-intuitive evaluation of
credit risk.
Several possible causes that prevented the spread of the technique were cited like the reluctance
of credit executives towards application of new techniques and lack of experts willing to exploit
this business opportunity. These half-century old conclusions, if transposed to a microfinance
context should sound very present-day to those who advocate the use of credit scoring in MFIs.
We must note that the success of credit scoring was held back by the lack of affordable means to
perform sophisticated statistical calculations. It is not obvious today, but at that time this was a
huge constraint. The use of computers, usually rented by the week rather than a proprietary tool,
to perform a discriminant analysis offered significant advantages, but also severe limitations. In
today’s microfinance industry too, cloud computing and software-as-a-service are being touted
as possible rent based solutions to the management information systems (MIS) needs of the long
tail of small MFIs (Ashta and Patel, 2010).
We find that the main traits of the conceptual framework of modern consumer credit scoring
were set by 1964. The description of the implementation and functioning of a credit scoring
system in a financial institution (Boggess, 1967) allows us to make this conclusion. In the
example of Boggess, loan applications were entered into the computer to be automatically scored
and approved if policy limits were respected. Within a maximum of 24 hours, as opposed to one
week before the introduction of the credit scoring systems, the credit department accepted or
rejected the application.
The asymmetry of information in microfinance has been well documented (Armendariz and
Morduch, 2005). Common high default rates of microfinance in developed countries and even in
developing countries during the post-crisis period indicate that credit scoring models have a role
to play.
While so far credit scoring was the exclusive domain of consumer lending, Edward Altman
(1968) employed the discriminant analysis approach to financial ratios in order to predict
corporate bankruptcy. Practical applications of his model were supposed to include business
credit evaluation using bankruptcy prediction.
The year 1974 marked a new stage in the history of the conceptual framework of credit scoring.
The US Congress passed the Equal Credit Opportunity Act that, including the following year’s
amendments, prohibited discrimination in granting credit on the basis of race, religion, national
origin, sex, marital status, age and few more such criteria.
In comparison with consumer lending, small business financing was seen by the academics as
the next potential application of credit scoring. However, fears that commercial lending is not as
homogeneous as consumer credit kept academics and practitioners away from that sector. No
paper tried to measure statistically this homogeneity discrepancy and link it to the theoretical
possibility (or impossibility) of developing credit scoring models for commercial lending. Fair,
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Isaac and Company only in 1995 in partnership with Robert Morris Associates started to offer
“pooled-data” credit scoring for small businesses (Fair Isaac Corporation, 2010).
By the end of the century, in the developed world, the credit scoring technique was adapted and
commonly used granting consumer and small business credit. The technique was adapted to be
used in other credit related fields like collections and targeting prospects. Simultaneously,
academics started to look for credit risk evaluation in developing countries. Laura Viganò (1993)
proposed the application of a quantitative model for evaluating creditworthiness of individually
operated small and micro firms based on multivariate discriminant analysis in Burkina Faso. She
concluded that the use of relatively simple credit scoring techniques in development banks has
some advantage and demonstrated the applicability of the credit scoring framework in less
developed countries.
This study (Viganò, 1993) in the microfinance sector marked a new stage in the history of credit
scoring and opened the way for further research. In the area of micro loans, credit scoring still
experiences problems similar to the ones consumer lending experienced in the USA, but in a
different time and context. The analysis of these differences can help us predict the future of
credit scoring in microfinance.
Our review of the literature doesn’t stop at the threshold of the new millennium. The works of
Mark Schreiner and other academics working in linking credit scoring to microfinance are
studied. We conclude by presenting the consolidated conceptual framework of the credit scoring
technique. It can be applied to consumer, business and micro loans. Any improvement of the
framework has the advantage of improving the use of credit scoring in all concerned loan
segments.
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Early works
The set of studies in consumer instalment financing conducted by the US National Bureau of
Economic Research (NBER) in the last years of the 1930s and published in 1940 – 41 represent
clearly the base on which the framework of credit scoring was built. The NBER, as a nonprofit
organization, was engaged at that time in researching and disseminating knowledge about
essential economic facts.
Instalment financing was one important economic activity. Loan outstanding of this sector
doubled from 1934 to 1938, as shown by the reports of a hundred US banks (Chapman and
associates, 1940). The first NBER study “Personal Finance Companies and Their Credit
Practices” edited by Ralph A. Young and associates, and published in 1940, focused on all retail
lending, defined as a transaction where the consumer receives goods or money and engages
himself to pay the price and incurred interests at regular intervals. Home mortgage credit was
excluded due to specificities of such products.
Personal business finance contributed significantly to the growth of consumer credit. A “close
cousin” of the current micro loan, the amount of a personal loan was generally limited to 300
USD and maximum legal interest rates were 2 to 3.5% per month, depending on the State. The
supply of personal loans was encouraged “to combat the loan-shark evil, which had arisen
because the usury statutes prevented the profitable lending of small sums at legitimate rates… If
licensed credit facilities are to be provided to low income groups legal rates must be high enough
to encourage profitable operations” (Young and associates, 1940).
If we ignore the date when the study was conducted, the above quoted text can describe the state
of microfinance in numerous countries nowadays. This shows that following developments in
this paper are interesting not only from the historical perspective on how the framework of credit
scoring evolved, but also how the problems which are similar to the ones being experienced by
the microfinance sector were addressed.
The need to reduce transaction costs and enhance the appraisal of risks was obvious in a
competitive environment. The third NBER study titled “Commercial Banks and Consumer
Instalment Credit” by John M. Chapman and associates, published in the same year, presented
the problems experienced by lenders. These lenders were conscious that some characteristics of
the applicants are related to their credit risk. Such items were collected and considered in the
evaluation. For the credit risk, two main factors were considered to be important: willingness and
ability to repay the loan. Information as personal characteristics of the applicant, income, net
worth and other financial characteristics were considered to be important predictors of the ability
to repay.
“On the basis of experience, and to some extent intuition, the loan officer decides which
applicants are more likely to default than others or which loans are likely to involve collection
costs so great as to render the transaction unprofitable… Lenders need to know the relative
importance of as many credit risk factors as can be isolated, and in making a final decision on a
loan application the responsible officer must give due weight to each factor” (Chapman and
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associates, 1940). Second part of the citation defines the main problem that credit scoring is
supposed to solve.
In order to identify factors the notion of index of bad-loan experience was introduced and
defined as percentage of bad loans divided by percentage of good loans. The index was
calculated for each item or class of items that the factor comprises. A supra-unit index indicated
that the item gathered relatively more bad loans than good loans, by consequence the item could
be a possible risk factor if found statistically significant. Of course, some high indexes could be
just the result of the chance.
The higher the index, the higher the odds that the item has credit risk prediction power. The same
works for low indexes. In this case the item would gather a significantly larger share of good
loans than bad loans. In general, if the item is an indicator of risk, than the factor is. If we find
that young clients tend to be bad clients (item: aged 25 years or less) then the age (the factor)
could be an indicator of risk: high risk for young applicants, low risk for older applicants.
The authors, based on samples of good and bad clients coming from different financial
institutions found the most significant indicators of credit risk the following items (and factors):
possession of a bank account, stability of employment, nature of occupation, permanence of
residence, ownership of real estate and industrial affiliation. They made an important remark that
challenges researchers till today. “Since these borrowers had already passed through a selection
process at the hands of credit men, the sample cannot be considered completely representative of
the general run of personal loan applicants” (Chapman and associates, 1940), meaning that there
might be other important factors that didn’t manage to be correctly represented in the samples
because credit officers screen applicants and may eliminate an important proportion of what they
perceive as bad risks.
The second NBER study, “Sales Finance Companies and Their Credit Practices” by Wilbur C.
Plummer and Ralph A. Young (1940), presents a functioning credit rating system, which we
consider to be the closest ancestor of a credit scoring model. The use of such systems was not
uncommon in the late 1930s. In some cases a rating of good, fair or poor was entered for each
item considered as an indicator of credit risk. The final rating would represent the average of
favorable and unfavorable indications. “In other cases a specific grade is entered opposite each
item, and the sum of the grades serves as the index of credit risk” (Plummer and Young, 1940).
The use of such systems is not uncommon today, especially in micro and SME lending.
The work of David Durand, which is one of the following NBER studies, published in 1941
under the title of “Risk Elements in Consumer Instalment Financing”, marks the first important
step in the development of the technique of credit scoring – the beginning.
We consider this study to have pioneered the use of statistics in analyzing credit risk factors in
lending. The author used a large sample of 7,200 consumer loans disbursed by 37 financial
institutions: commercial banks operating personal loan departments, personal finance companies,
industrial banking companies, automobile finance companies and appliance finance companies.
Borrower characteristics were copied from their loan application forms. These included: age,
sex, marital status, dependents in the household, stability of employment, permanence of
residence, and other socio-demographic variables. Also borrower’s assets and liabilities, and
loan characteristics like amount and number of installments were available. No information
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about past credit behavior was collected, nor information on “matters like physical or mental
health, which are certainly germane to risk problem, but which obviously do not lend themselves
to analysis in a statistical study of credit risks” (Durand, 1941). The author highlights the
limitations of the sample due to screening of applicants by the loan officers.
Later, different techniques as the reject inference were to be developed, but at that time the
solution proposed by Durand was “to make experimental loans, which amounts to a temporary
lowering of standards, with a possible increase in losses, and a subsequent adjustment of
standards on the basis of the newly gained experience” (Durand, 1941). This solution is still
considered in present days.
Durand (1941) makes the interesting remark that in practice it is difficult to make a precise
distinction between good and bad loans. If the net revenue from a loan doesn’t cover the
expenses to recover it, it is certainly a bad loan. If the loan and interest are repaid in full and on
time it is considered by lenders to be a good loan. Delinquency has a role to play in making the
distinction. Serious delinquencies lead to additional expenses like collection or court actions with
no guarantee of a return. Since it is impossible to determine accurately, in advance, when a loan
ceases to be profitable, this has to be estimated.
The author proposed several criteria by which a commercial bank could identify bad loans for
the sample he needed for the study: “loan was more than 90 days delinquent; comaker [cosigner]
paid all or part of loan after demand by bank; legal action was taken; loan was charged off”
(Durand, 1941).
By simply analyzing the mathematical means of all linear factors in good loans and in bad loans,
differences between these two mutually exclusive groups are observed. These differences are
even more obvious when using the previously defined index of bad-loan experience by classes of
the factor.
About the sampling, Durand finds another interesting aspect. Even if large samples are preferable,
collecting thousands of cases is impossible or too expensive, and a sample of 100 good and 100 bad
loans may be adequate for empirical significance.
For the microfinance sector these findings should be very important as sampling in many MFIs is
still a big problem due to bad databases or lack of MIS. Some institutions don’t have many bad
loans, but still find a scoring tool useful to be able to serve more clients by gradually lowering
screening barriers while controlling the credit risk. From experience, we may say that the “100
good and 100 bad loans” for a credit scoring system is possible within the microfinance reality.
Durand (1941) identifies another threat: over time, risk experience to be used for future
estimations may change. One of the proposed solutions is to limit the study to short
homogeneous periods. The fact that such periods have to be “recent” is not mentioned but seems
to be implicit.
In order to pass from the influence of isolated credit factors on the bad-loan experience to a
global approach, Durand (1941) employs the concept of a “credit-rating formula”. It is a formula
which combines most important factors and the corresponding weights for the classes.
Computed, the result is a credit-rating score, which is used as the basis for accepting or rejecting
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loan applications. Today, the term “scorecard” and “algorithm” have replaced the expression
“credit-rating formula”, but in the essence, each scorecard is a formula and the definition for the
later remains unchanged in the industry till present days.
Intuitive-subjective credit-rating formulae have been created before and in use in certain
financial institutions. Durand (1941) created the first “purely objective credit formulae by
statistical methods”. This stage marks the beginning of credit scoring. The statistical approach
towards the selection of factors that predict credit risk is an essential part for the conceptual
framework of credit scoring. There is no credit scoring without empirics.
Durand points precisely the advantages of credit scoring. Loan officers can assess ordinary
applications faster and “most of the routine work of investigation to be handled by rather
inexperienced and relatively low-salaried personnel” (Durand, 1941).
The success of the formula is tested using the distribution of good and bad loans by that formula.
By virtue of the multivariate analysis, the “efficiency index” is higher than the highest index of
all individual factors. The index used by the author measures half of the sum of all absolute
difference between percentages of good and percentages of bad loans for all classes of the factor.
The same index is calculated for all classes of score in the distribution.
The sum of the differences is zero. The highest possible index is 1, indicating that the risk factor
has such characteristics that gathers all the bad loans and none of the good ones. In such case
credit scoring is useless as all bad loans can be eliminated using one factor. The lowest index is 0
– no credit risk discrimination potential. A perfect scorecard will have an index of 1, which is
never the case in practice.
Durand (1941) used the statistical technique of discriminant analysis to generate the scoring
formulae. He also presented a formula to be used in fixing the cut-off, which takes in account the
profitability of the clients. His work lays down the principles of the conceptual framework of
credit scoring.
We retain that for developing a credit scoring formula we need a representative sample of good
and bad borrowers. We need to know their detailed profiles: the more characteristics the better.
Statistical methods are used to identify items and their weights to compose the formula. The
resulting scoring will be used to accept or reject applicants based on a certain cut-off. Obviously,
statistics may contradict some subjective ways of thinking. Durand (1941) discovered that
women tend to be good risks to the surprise of some lenders that were convinced of the contrary.
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The commercial era
The revolutionary approach of Durand required some time to reach scholars and the
professionals involved in consumer lending. That explains the small number of research studies
covering the topic of credit scoring till the 1960s. On the contrary, consumer lending continued
to grow in USA touching larger segments of the public, while Europe was recovering after the
war.
In the paper “Development of Numerical Credit Evaluation Systems” published in 1963 by
James H. Myers and Edward W. Forgy comes with important findings and improvements to the
conceptual framework of credit scoring. At a point, the growth of consumer credit in USA
became unsustainable, since under the pressure of the demand, many financial institutions grew
“beyond their capacities to train and maintain an adequate staff of experienced credit evaluators”
(Myers and Forgy, 1963). Microfinance today experiences similar problems. If that situation was
favorable for the diffusion of credit scoring in the USA, now it is quite the time for the technique
to invade micro lending.
Durand (1941) used aggregate data instead of building scoring formulae based on each
institution’s individual sample - fact that could explain the reduced efficiency of his scoring
formula. He used a sample to build the formula and the same to test it, which supposes a risk of
biased results. Myers and Forgy (1963) on the contrary, worked with the data from one financial
institution. They engaged in a battle for predictive effectiveness of scoring formulae. A new
component that was added to the framework is the use of a “hold-out” sample. There are chances
for a formula that can separate well the good and bad clients in the original sample to have no
predictive power at all. Since then, the use of a separate sample to test the effectiveness of the
formula became mandatory.
The authors used different statistical approaches to identify the items and corresponding weights
to be included in the four resulting credit scoring formulae: discriminant analysis (same used by
Durand), stepwise regression, equal weights for pre-selected predictive items and a “doublediscriminant analysis” consisting of selecting a subsample of good and bad loans which received
the lowest scores under the first approach and re-performing the analysis on this low-end
subsample.
Myers and Forgy (1963) found that a smaller number of variables (12) will predict almost as
well as all (21) variables that had significant risk discrimination potential. The “face validity”
was important for the institution in the process of validation and use of the formula. If the lender
cannot understand and justify different variables that are selected, there are chances that credit
scoring won’t be used, or employed in a wrong way.
In spite of substantial improvement over judgmental approaches, credit scoring was in limited
use. Possible reasons were reluctance of experienced credit executives, difficulties in developing
and implementing credit scoring or “the unwillingness on the part of statistical consultants to
invade the domain of the credit manager and do the selling job necessary to transform such idea
into a successful and useful operating tool” (Myers and Forgy, 1963).
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It is likely that the authors were not aware that for 7 years in California a business named Fair,
Isaac and Company (FICO) was proposing credit scoring solutions for financial institutions.
Actually it was in 1958 when the company decided to play big by sending letters to the 50
biggest national financial institutions inviting them to receive explanations on the concept of
credit scoring. One credit grantor responded and in the same year the company sold its first
credit scoring system. Today 90 of the 100 largest financial institutions in the US and all the 100
largest US credit card issuers are FICO clients (Fair Isaac Corporation, 2010).
The apparition of a business player confirmed the potential of the technique of credit scoring
predicted by academics. The credit scoring industry has been born and with it the distinction
between the public interest and private competitive advantage in the field of predicting credit
risk. As the company’s know-how was strictly guarded, we continue the research for information
on the academic side. In the paper of Paul F. Smith “Measuring Risk on Consumer Instalment
Credit” published in 1964 in Management Science we find a reference of a paper written by Earl
Isaac named “Statistical Problems in the Development of Credit Scoring Systems”. It appears to
be the only paper about credit scoring written by the mathematician that co-founded and
managed for a long period FICO.
In the same paper Smith (1964) proposes a different methodology for calculating the credit score
by adding together the “bad account probabilities” corresponding to each item of the applicant.
The probabilities, all positive, are calculated using a sample of good and bad loans. The author
also proposed to multiply by 1,000 these probabilities for easier computing of the sum and
clearer interpretation of the results. This multiplication by 1,000 would be adopted by some
practitioners, but it won’t help the technique, or the interpretation. It helps only people that
prefer integral numbers instead of fractions. The scoring methodology, even if simple and clear,
was rejected as not empiric, even if probabilities were used. Was considered the proposal to
compare the scoring formula with the traits of rejected applications, to judge on possible bias of
the sample.
In his short article titled “Concepts and Utilization of Credit-Scoring Techniques” published in
the journal Banking in 1966, Martin Weingartner presents the state of the art of credit scoring in
USA. He links the increasing success of the technique with electronic computers becoming
available. The author mentions the risk based selection of loan characteristics – the loan amount
depends on the credit score, no evidence of risk based pricing.
Weingartner (1966) highlighted the importance of performing tests before credit scoring is used
by the institution. Besides the validation using the hold-out sample, the author suggested using
the formula to score current delinquent accounts to observe if these receive low scores. This easy
to perform test would not be included in the conceptual framework of credit scoring, but it
certainly can be used every time when extra confidence in the formula is required.
For the first time in the literature the importance of a field trial is mentioned. The procedure
supposes the use of credit scoring first by only a few loan officers or by only one branch out of
the entire network. The procedure is intended for “training as well as for ironing out difficulties
that arise” Weingartner (1966). This is not a constituent of the framework but a mandatory stage
from a pure practical view of the implementation process.
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Post-implementation reports certainly are a constituent part of the conceptual framework of
credit scoring. Weingartner (1966) proposes “a running barometer of the quality of credit
granted, and of the quality of applications turned down”. If the management on a regular basis
analyses the scores of applications that were accepted and of those that were rejected, the
overrides could be identified and examined. The average score of accepted applications and the
trend would indicate the quality of newly engaged portfolio.
New research trends in credit scoring include efforts to build formulae that predict the
profitability of loan accounts instead of the chances of registering delinquencies, but also the use
of the scoring technique in identifying accounts more likely to respond to different collection
procedures (Weingartner, 1966). The latter, even if similar to credit scoring, will be referred as
“collection scoring” and by consequence excluded from the current framework.
Figure 1 - the graph used by Weingartner (1966) to explain the use of the credit scoring distribution
We conclude that the main traits of the conceptual framework of modern consumer credit
scoring were set by the mid 1960s. The previously cited papers, plus the description of the
implementation and functioning of a credit scoring system in a financial institution done by
William P. Boggess in the Harvard Business Review in 1967 in a paper titled “Screen-test your
credit risks” allows us to make this conclusion.
In the company that implemented credit scoring in 1964, loan applications were automatically
scored by the computer if policy limits were respected. Within 24 hours the credit department
accepted or rejected the application based on the score. Before, this task required up to one week.
The financial institution experienced an increase in staff of less than 2%. “The company cut bad
debt losses enough to realize a $1.5 million profit improvement on more than $100 million in
sales in the first full year of the system’s operation” (Boggess, 1967).
MIS combined with the virtues of credit scoring give the lender the possibility to operate
procedures that adapt the strategy of the company to shifts in applicants’ population. The
concerned company developed a new scoring formula each 6 months and track changes in the
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weights of characteristics included in the scoring formula over time (Boggess, 1967). An
economical approach would consist in tracking changes in characteristics of applicants and
develop new formulas when shifts are considered significant. Another approach would
recommend the replacement of the formula only when the risk discrimination power starts to
reduce. The inconvenient is, as Boggess (1967) noted “nine months’ time, rather than a long
period, was necessary before the good and bad accounts could be predicted with acceptable
accuracy”.
The credit behavior maturity is a mandatory concept for institutions willing to develop a credit
scoring formulae with relatively recent data. This is often the case with microfinance institutions.
It means also that the results of using a scoring formula will be seen in a certain time. So, if the
risk discrimination power deteriorates, the institution has to react fast. The scoring reports having
the function to control the use of credit scoring come to complete the conceptual framework.
THE POPULATION:
MULTIVARIATE ANALYSIS:
Two representative samples of recent or
current borrowers whose mutually exclusive
status of good or bad risk is known or can be
precisely estimated.
Statistical link between
characteristics of the profile of
the borrowers and their status as
good or bad risks
The detailed profile of borrowers at the
moment of loan application is known.
SCORING FORMULA:
b.) the hold-out sample
Formula is built using the
development sample and tested
on the hold-out sample. Results
are compared with the
rejected sample
Good risks
When computed the formula
gives a score
CONTROL:
a.) the formula development sample
bad risks
rejects
Computation of the formula
gives a score DISTRIBUTION:
USE:
CUT-OFF (S):
Applicants
are scored
and are
accepted or
rejected
Determination of the cut-off
score based on the analysis of
profitability of good loans
and losses generated by bad
loans
The result of
scoring the holdout sample is a
scoring
distribution
Figure 2 – the conceptual framework of credit scoring as existed during the middle of the 1960s.
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From consumer credit to corporate lending
While credit scoring was the exclusive domain of consumer credit, in corporate lending
academics and practitioners started to consider statistical techniques to replace or enhance the
traditional ratio analysis in evaluating the health of the companies. Edward I. Altman (1968)
employed the discriminant analysis approach to predict corporate bankruptcy using financial
ratios, which gave better statistical significance when analyzed within a multivariate framework.
Altman (1968) used only ratios, ignoring other important categories of variables like loan
variables or business-demographic variables. At that time no paper addressed seriously the
importance of business-demographic variables in predicting credit risk. Altman concludes that
the model, in spite of the potential it has, “should probably not be used as the only means of
credit evaluation” (Altman, 1968).
Altman’s contribution to the conceptual framework of credit scoring is important as it opens it
for business loans. So far, credit scoring was used exclusively in consumer credit where the
subject to be scored was the person applying for a loan. If Altman’s findings were to be applied
in practice, then the framework would include a second category of subjects – the companies.
The profile of a company is different from the profile of an applicant for consumer credit.
Yair Orgler (1970), inspired in part by the work of Altman, focuses on the use of the credit
scoring technique on current (in-progress) commercial loans. The proposed purpose was not to
accept or reject but to review periodically the quality of the loans already disbursed. He makes
the interesting remark that business borrowers in general do not belong to large homogeneous
populations as do customers for consumer credit. This problem was not yet defined in scientific
terms by the academics. The condition of homogeneity of the population of subjects to be scored
becomes part of the framework of credit scoring.
Due to the fact that commercial borrowers are so diverse and their loan products so varied in
terms of maturity, amount and security, the application of a credit scoring model for approval of
loans would be less appropriate then using it for periodic quality reviews of current loans. “Its
main advantage is in releasing loan officers and bank examiners from routine evaluations of all
loans and allocating their time to a small proportion of riskier borrowers” (Orgler, 1970).
The use of the scoring technique to measure the evolution of the credit risk during the course of
the loan is certainly new, interesting and useful to some extent, knowing that corporate loans
have longer maturities and meantime changes can affect seriously the credit risk. The fact that
during the reimbursement of the loans new information comes regularly to enrich the profile of
the borrower, especially data on current payment behavior, makes this type of scoring formulas
relatively robust in predicting defaults. Financial institutions, on the other hand, have limited
freedom of action. The risk can not be avoided as the money has been already lent. Corrective
actions only can be done. This particularity, like in the case of collection scoring, can not be
accommodated by the conceptual framework of credit scoring. A new framework, very similar to
the one of credit scoring, emerged and its importance would soar with the extension of Basel
Accords.
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This divergence between consumer loans and corporate loans does not exist in microcredit.
Micro lending addresses generally poor and self-employed populations. The borrower in a
majority of cases is the person, his household and his small entrepreneurship, all in one. His
business and household are assessed as these will generate the money to reimburse the loan. If
not existing as formal and trusted documents, a balance sheet and an income statement,
simplified to the maximum, will be created and assessed using ratio analysis or similar
procedures. The applicant – the person – will be evaluated using the consumer lending
techniques.
Several scholars, including Yair Orgler (1970) mention the dissertation of David Ewert (1968)
that proposed a credit scoring formula to be used by wholesale distributors in granting trade
credit to retail stores. As the stores were mostly small – one-owner companies needing trade
credit of amounts similar to the consumer credit – the formula used a combination of variables
describing the owner – the person applying for the loan and variables describing the financial
position of the store – the company to be the legal responsible for the loan.
From this experience, we will retain that the profile of the owner of the business as well as the
profile of business itself may be important in predicting the credit risk of small companies. When
transferring the technique of credit scoring, from consumer lending to business credit, there is an
area where these two confound. This is certainly the case of micro lending. The phenomena
known as “informal economy” has certainly a role to play in blurring the limits between the
physical person and the business entity he owns. As the literature review conducted so far
involuntarily focused on the US papers, none addressed this issue. With the credit scoring
technique spreading in the developing markets, this issue will reappear.
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Modernism in credit scoring
The early 1970s saw the industry of credit scoring growing. FICO started collaboration with
Wells Fargo – major financial institutions in the US. The credit scoring provider already was
planning to export the technique to Europe (Fair Isaac Corporation, 2010). Increasing number of
academics approached different practical and concrete topics related to the scoring technique.
Emphasis is put on costs and net present value of loan repayments (Edmister and Schlarbaum,
1974), but also on best statistical techniques to be used and a better definition of good and bad
risks. The role of credit bureaus in helping identify bad credit risks is mentioned. Some serious
works are focused on low income populations.
Muchinsky (1975) finds that two dimensions of the borrower's repayment behavior are critical to
its classification by the lender as good or bad credit risk. One is obviously the delinquency and
the second: the anticipated repayment of a loan. The fact that the borrower reimburses the loan
prematurely is susceptible of making the account unprofitable as interest margin perceived only
for a short period doesn’t cover transaction costs. This perspective enriches the perception of bad
clients.
The efforts to use the scoring technique to facilitate extension of credit to low income clients
indicate a certain maturity of the concept of credit scoring in the US. Certainly, the Consumer
Credit Protection Act (CCPA) of 1968 had a major role in regulating the industry. The Equal
Credit Opportunity Act of 1974 had also notable implications. Including its 1976 amendments,
this Law prohibited discrimination in the granting of credit mainly on the basis of race, religion,
sex, marital status and age. These ethical concerns were formalized within the legal framework
and in this way adjusted the conceptual framework of credit scoring in USA.
Even if many countries are not concerned by such limitations, ethical and sound selection of
variables predicting the credit risk is a rule that seems to be obeyed in part by the practitioners.
Gender and marital status as exceptions are often used in credit scoring formulae designed to
score low-income populations in the virtue of “positive discrimination”. In microfinance, women
in spite of proven better credit behavior are more often refused access to credit (D'Espallier,
Guérin and Mersland, 2009). Divorced women experience even stronger exclusion. If a scoring
formula can help increase the chances of excluded female borrowers to get loans while
improving the quality of the portfolio, then many might be seduced by such opportunity.
The US National Commission on Consumer Finance created by the same CCPA ordered a
research to determine the feasibility of a credit scoring system applicable to low income
consumers. The conclusion was negative indicating that variables most likely to discriminate
credit risk of low-income consumers are presently excluded from standard loan application
forms (National Commission on Consumer Finance, 1972). Joan Tabor and Jean Bowers (1977)
conclude that credit scoring should be re-designed to be employed efficiently in evaluating credit
quality of low income consumers. Establishment of household financial consultants is suggested
– an idea that certainly doesn’t contribute to the initiative of lowering transaction costs.
Donald Sexton (1975), on the contrary, found that only few variables with credit risk predictive
power differed between high-income and low-income households and thus could not make the
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conclusion that different procedures are required for high- and low-income populations. We
believe that this issue is reduced to the question of homogeneity of populations, and low income
borrowers, except the income, may be different in many aspects. For the conceptual framework
we note that it is possible that low-income customers have different credit behavior habits that
may be considered separately. It is important to mention that in the effort of extending credit in a
sustainable manner to poor US borrowers new techniques of cost reduction like credit scoring
were seriously considered.
The “in-house knowhow” character of credit scoring systems used by the financial institutions
represented an increasing problem for the scholars, who found it difficult to relate and research
on how well the industry incorporated new tendencies and legal requirements into practice. The
new regulation required statistically sound scoring systems be constructed using empirical
methodologies, but no precise standards were imposed. The hypothetical obligation to
demonstrate the soundness of a scoring system in Court made the scholars focus on different
technical aspects and assumptions that were ignored, as long as the model was showing evidence
of credit risk discrimination on the hold-out sample or in practice.
In the absence of case studies, Robert Eisenbeis (1978) analyzed the credit scoring systems
developed by academics at that time, hoping that these were reflecting the systems in use by
lenders. Since the majority of models were developed using discriminant analysis, he pointed out
statistical problems the technique had and warned the public on the inherent risks.
With the apparition of credit bureaus selling information on past credit performance, the cost of
extra information was considered in different credit granting schemes (Eisenbeis, 1978). For the
conceptual framework of credit scoring, such considerations foresaw new enhancements. If a
scoring formula predicts the credit risk accurately using few variables, why pay for extra
information? On the contrary, for the loans in the “grey area”, at the limit of the cut-off, if
additional information can help discriminate better, extra costs are clearly justified. Can one
scoring formula accommodate such features?
James Ang, Jess Chua and Clinton Bowling (1979) were amongst the first to build a nonparametric credit scoring system. They applied the decision tree technique to a credit scoring
related problem. The result is not a scoring formula as before, but an algorithm represented by a
tree-like scheme. The characteristics of the scored subject guide the user through the nodes and
branches of the tree till the estimated bad or good class of the applicant is determined.
The use of “automatic interaction detector analysis” showed that the relationships between late
payments and some borrower variables are nonlinear. (Ang, Chua and Bowling, 1979). By
consequence, linear credit scoring models may not be always appropriate. Since this technique
remains to be a multivariate analysis, the conceptual framework doesn’t change. We will note
however that besides the discriminant analysis which was applied first by Durand (1941) to the
loan screening question and since, extensively used by academics, and the regression analysis,
that started being popular by the end of the 1970s, other non-parametric techniques belonging to
the multivariate analysis may be used in identifying good and bad credit risks.
We conclude that by the end of 1970s, credit scoring was a recognized industry. The concept
found its first use in Europe, implemented by FICO in a bank in 1977 (Fair Isaac Corporation,
2010). The company by that time delivered approximately 500 systems to approximately 200
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customers, including about half of the 50 largest US banks and 20 US finance companies,
according to the testimony of William Fair - that time head of FICO, in front of a Senate
Commission (U.S. Senate, 1979).
The framework of credit scoring entered its modern times. From this perspective it’s hard to
imagine that the concept of credit scoring will change significantly.
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The new dimensions of scoring
From the perspective of the 1980s, looking back at the Equal Credit Opportunity Act, which was
originally perceived as a possible threat to credit scoring, we have a different impression. Since
the use of some discriminatory but predictive variables was prohibited, many feared an overall
reduction in predictive power of scoring systems, even if a study showed no negative impact
(Nevin and Churchill, 1979). However, since judgmental systems were strongly criticized as
being subjective and thus prone to discrimination evaluation of credit officer, credit scoring had
to gain. It was supposed to be objective and under the new Law, respectful of ethical issues.
Noel Capon (1982), one of the strongest opponents of the use of “brute force empiricism” in
credit scoring, remarks that the credit scoring formulae in use included variables with no obvious
or logical relationship to creditworthiness, while other variables that seem to be directly related
to the capacity of repayment, like income, may not be included at all. It is true that with
computers becoming more popular and powerful and statistical packages becoming more userfriendly and affordable, many practitioners engaged in a “wild west” conquest of credit statistics
to find new predictive variables or “surrogates” for prohibited variables.
In parallel, the credit bureau industry grew, supported by the continuous expansion of the
consumer credit market. The databases had to expand in volumes and complexity. It was not
surprising that in 1981 FICO introduced the first credit bureau score (Fair Isaac Corporation,
2010). Against a larger fee, the inquiring financial institution would henceforth receive not only
the existing credit record of the applicant but also a credit score. The score could be used solely
or in combination with the institution’s appraisal result in deciding to grant credit or not. The
presence of an external score does not affect however the conceptual framework of credit
scoring.
A new concept called “expert system” was supposed to have serious implications in the credit
business, particularly in consumer credit. Backed-up by developments in the IT industry, expert
systems were software designed to imitate the way of thinking of human experts. Holsapple,
Tam, and Whinston (1988) presented a summary of application of such techniques in finance.
We find that some financial institutions were already using expert systems in credit analysis and
approval.
The advantage of a “cyber expert” is the possibility to have an output in real time or much faster
than human experts will provide the decision. Such tools, having characteristics belonging to the
artificial intelligence domain, may go beyond the function of decision support. In combination
with a functional credit scoring system with clearly defined scoring strategies, an expert system
may take credit decisions for the big majority of loan applicants in real time, leaving to the loan
officers the task of treating applications falling in the grey area, if such areas are defined.
Since the quality of the decision can be measured with hindsight at later stages, engineers
imagined expert systems acquiring knowledge automatically from past experience and
particularly this kind of artificial intelligence can have implications for the credit scoring
framework. The principles of credit scoring won’t change, but what might change is the way the
scoring formulae are kept up to date. This is especially important because any scoring algorithm
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tends to lose its credit risk discrimination power over time due to the evolution of the society. If
notable changes occur, and the financial institution is able to trace them, the scoring formula has
to be recreated, as adjusting a formula may be statistically impossible. In both cases the
operation is expensive, so a scoring system that adjusts itself to the changes of the environment
seems to be an interesting innovation.
With the globalization, the technique of credit scoring has started being disseminated across the
world. Papers coming from non-US academics appear but their work doesn’t bring anything new
to the framework of credit scoring. Particular statistic questions continue to be the most
researched topics. The efforts to approach the credit scoring problem from a multi-period
perspective or from the long-time discussed profitability perspective struggle to find their way
into practice.
The credit scoring principles progressively “invaded” other banking and non-banking sectors. It
is currently employed in banking fraud detection, in marketing, but also in the insurance industry
to cite just a few. However, every use of the scoring technique outside the credit granting
procedure is considered by us to be outside of the conceptual framework of credit scoring. On
the other hand, the application of the scoring technique in approving loans to small and micro
businesses, but also to mortgage loans, enriches the framework. In 1995 FICO in partnership
with Robert Morris Associates developed a credit scoring tool for small business. The concept of
“pooled-data” was introduced to solve the problem of heterogeneity of profiles of small
businesses (Fair Isaac Corporation, 2010).
While developed countries progressively adopted the credit scoring techniques, Laura Viganò
(1993) studied the applicability of credit scoring on individually operated small and micro firms
in Burkina Faso. She concluded that the use of relatively simple credit scoring techniques in
development banks in less developed countries has some advantage. These findings are
encouraging, knowing the demonstrated ability of such systems to cut transaction costs and
enhance the decision making process.
Credit scoring enters a new stage: the development finance. In developed countries, the
technique reached a certain threshold of perception and understandability. Obviously, new
statistical techniques can give even more predictive power, but the face value of the technique
has to remain clear to those that decide to buy or implement credit scoring in their financial
institutions. These deciders are rarely advanced statisticians.
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The microfinance era
We perceive the introduction of credit scoring in microfinance as a new stage in the history of
credit scoring. Researchers used the technique of credit scoring in a similar context as Duran
(1941) used it in USA after the Great Depression. The concept did a full cycle to return to the
origins of the problem of granting loans at lesser cost to (micro) borrowers.
As Mark Schreiner noted in his cornerstone paper “Credit Scoring for Microfinance: Can It
Work?” published in 2000, credit scoring for microfinance can work. The difference is in the
information, which is usually qualitative and informal. The new challenge of credit scoring is
incorporating and adapting to this constraint. Neither Durand nor other scholars treated the topic
of using informal data for credit scoring purposes.
Unfortunately, in many countries around the world, even amongst the rich, the informal and
semi-formal sector represents an important share of the GDP. There are lots of people behind
this economy that need financial services. Microfinance is a fair answer to a large majority of
these needs.
Although “credit scoring is one of the most important uses of technology that may affect
microfinance” (Rhyne and Christen, 1999), we need to redefine the conceptual framework of
credit scoring to allow its full application to micro lending. The development of the framework
will serve as a guide for practitioners in applying efficiently credit scoring in microfinance.
Credit scoring doesn’t have to be rediscovered, but adjusted and promoted in order to cut
transaction costs and make the credit available to the excluded as long as the credit risk can be
measured and controlled.
The concept of information asymmetry pioneered by Stiglitz has a particular connotation in
micro lending. Statistics have a major role to play in reducing the information gap as have onground investigations. Who thought before that the opinion of a neighbor of the applicant for a
micro loan can be strongly correlated with his credit behavior? Such qualitative information can
now be harnessed and incorporated in a credit scoring algorithm.
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Conclusions: The conceptual framework of credit scoring
Credit scoring is a tool designed to help manage the credit granting problem. It is based on an
algorithm that predicts the future classification of the applicant as a good or bad credit risk using
the known profile of the subject, which belongs necessarily to a homogeneous and massive
population. The algorithm is derived using a multivariate analysis technique that allows
identifying characteristics of the profile and respective weights of recent or current borrowers
whose status as good or bad risks is known. The assumption is that future borrowers will have a
credit behavior alike past borrowers with similar profiles. Statistical significance and
representativeness have to be respected. Due to the fact that past borrowers had been screened by
loan officers during their approval process, the population of clients with known credit risk status
is biased. The profiles of rejected applicants have to be confronted with the profiles of recent
good and bad clients and results considered. In the effort to reduce bias, the algorithm is
developed using a sample and tested on a hold-out sample. The discrimination power of the
algorithm is measured and tested if statistically and economically significant. If acceptable, the
credit scoring system is implemented and used in the process of screening loan applicants.
Results of the use of the credit scoring system are regularly verified using reports.
The population: comprises subjects interested in the loan products of the financial institution. A
loan is a transaction where one part receives an amount of money or the goods bought using it,
and engages to reimburse the same amount at a later date in one or more installments to the
second part which bears the credit risk and perceives from the former an interest. Some subjects
of the population received loans before, some were refused and some will apply in the future.
One particularity of the subjects is the homogeneity and by consequence the needs of the
subjects in mater of financial services are similar. We may consider all the applicants for a
consumer personal loan as a separate population. Most important categories of populations are:
consumer borrowers, corporate borrowers, small business borrowers and microfinance
borrowers, but also mortgage borrowers. We may note that the concept of population can be
extended to the applicants of other financial institutions offering similar loan products.
If we analyze past applicants, then we notice that some were rejected as considered too risky. A
large proportion received a loan and reimbursed it as agreed with the financial institution. These
are certainly good borrowers and the financial institution probably proposed them to renew their
loans. There are also few borrowers that didn’t reimburse the loan as agreed. Some of them
reimbursed the loan in advance. Some defaulted and did not repay the loan outstanding. Some
registered serious delinquencies and engaged with the financial institution in collection actions.
In the big majority of such cases the financial institution lost money in doing business with the
clients. By consequence such loans are bad and no new credit was extended to them.
Exceptionally some delinquent loans may still remain profitable due to penalties charged on
overdue capital or due to recovered amounts that would cover all extra costs plus the expected
revenue. Such cases remain in the area of exceptional.
Obviously, the profitability of each loan is the best indicator for it belonging to the mutually
exclusive class of bad and good loans. Delinquency however serves as a good proxy and is used
extensively as an indicator for identifying good and bad loans. Analysis of the delinquency has
to be done to determine the kind of delinquency that leads to collection costs and default.
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Different procedures may consider the borrower in a multi-period frame estimating present and
future profits generated by the client.
We argue in the favor of simplicity. It also seems that simpler definitions of a bad loan imply
better prediction power of the scoring model. No research can support or reject our thoughts. In
the context of limited samples as is often the case of microfinance, current borrowers may be
already classified as good and bad customers with certain accuracy. If delinquency is at the
origin of the classification, then much before the end of the loan, clients reach a certain maturity.
It means that if they didn’t register delinquency before this maturity, it is highly probable that
they won’t register any delinquency after. For bad loans, once the bad status is reached, it will
remain with the borrower till the end of the loan.
The profile of the subject: groups all the available information on the subject applying for a
credit. This subject can be the person applying for a consumer loan, a business applying for a
commercial loan or a micro borrower. At small business scale, the distinction between company
and household is blurry, so the profiles of both have credit risk prediction power. We came to the
conclusion that the profile of an applicant can accommodate five categories of characteristics:
socio-demographic variables (for subjects where the person is at the center of the decision),
business-demographic variables (for business subjects), financial data, product data and past
credit behavior.
The multivariate analysis: is at the essence of credit scoring. Different techniques are proposed
and all techniques are empiric. This is the big advantage of credit scoring over judgmentalintuitive reasoning of the loan offices. The result is an algorithm. A scoring formula, a scorecard,
a model – all these are algorithms. Since the use of the non-parametric techniques, the algorithm
is the notion that resumes well the mechanisms at the core of credit scoring. The algorithm tells
the user how to proceed to identify the forecasted class to which belongs an applicant. The
accuracy of the forecast is also known.
The algorithm also includes the precise rules of accepting or rejecting the applicant. In nonparametric algorithms the cutoff is predetermined, while in parametric algorithms the cutoff is
set based on the score distribution. The cutoff indicates the estimated share of good and bad
loans that will be accepted at the selected level.
The use of credit scoring: is meant for the loan officer who does the calculations and follows
the logical instruction of the algorithm, or for an expert system – software. Here the two
important opportunities in cutting the cost of the transaction. First: simplify the task of the loan
officer in the appraisal process and by consequence reduce the cost of appraisal and second:
exclude the officer from the process. In microfinance, the exclusion of the loan officer seems so
far impossible, since information which is qualitative and coming from informal sources has to
be correctly collected and estimated.
During the use of the credit scoring, its true performance is gradually revealed. The initial use of
credit scoring is always delicate, since the effects on screening bad risks will be seen in several
months or more. The characteristics of the population have to be measured also as these can
indicate a possible change in the credit behavior habits of the borrowers. Recent developments in
the area of credit scoring made possible the construction of algorithms that use the control
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function to adjust gradually the algorithm to new changes in the population and to incorporate
the knowledge obtained during the use of credit scoring.
Ethics at the core of credit scoring: Durand (1941) when pioneering credit scoring used a
discriminant statistical technique. The methodology was adapted by Fischer (1937) and
subsequently employed in different areas including eugenics. Credit scoring was meant to
discriminate. Is it ethical? The problem seems complex but the answers to the following
questions may help. If costs limit extension of credit to small borrowers and if the fear of credit
risk prohibits extension of credit due to severe information asymmetry, should scoring be kept
away from microfinance? Should solidarity be always with the low-income borrowers in
covering the high cost of losses?
If a characteristic is a risk predictor, it will probably be considered during the judgment of the
loan officers. It will also be considered by the credit scoring algorithm. If the direct use of the
characteristic is prohibited, it will be probably used indirectly via other characteristics. The
personality of the borrower is certainly very important but empirics are science. When the
question is in the cost, a natural solution is credit scoring.
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