Innovation in the Retail Banking Industry: Credit Scoring Adoption and Consequences on Credit Availability
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Innovation in the Retail Banking Industry:
Credit Scoring Adoption and Consequences on Credit
Availability∗
Marcello Bofondi and Francesca Lotti†
Bank of Italy, Research Department
March 11, 2005
Abstract
Credit scoring techniques represent a major innovation aimed at reducing the costs
of underwriting processes and improving the accuracy of the estimates of borrowers’
default probability. The benefits deriving from the diffusion of credit scoring techniques
are potentially very large, both from the borrowers’ and the banks’ point of view. We
analyze the patterns of diffusion of this technology among Italian banks. We find that
credit scoring was first introduced by large banks which are fully able to exploit scale
economies. Moreover, a relevant channel of diffusion of this technology is represented
by the bank group. We then analyze the effects of credit scoring on the supply of
new mortgage loans by individual banks, finding that adoption allows them to increase
their market share. Finally, we examine whether the introduction of automated credit
scoring techniques has increased or reduced credit availability in local markets. The
empirical evidence indicates that introduction of credit scoring has a positive and
significant impact on the overall supply of credit.
Keywords: Innovation, Credit Scoring, Mortgage Loans, Technology Diffusion, Retail
Banks.
JEL Classification: G21, C41.
∗
Bank of Italy, Research Department, via Nazionale 91, 00184 Rome, Italy. Email: mar-
cello.bofondi@bancaditalia.it; francesca.lotti@bancaditalia.it. We wish to thank Giorgio Gobbi and Fabio
Panetta for their valuable comments, Jean Marie Bouroche and Alessandra Gabrielli of CRIF Decision Solu-
tion for helping us in understanding how credit scoring is designed and for data provision. A special thank to
those colleagues at the Regional Research Units who helped us with the survey. Cinzia Chini and M.Cristina
Fabbri provided excellent research assistance. The usual disclaimers apply. The views expressed here do not
necessarily reflect those of the Bank of Italy.
†
Corresponding author.
11 Introduction
In this paper we study the diffusion of credit scoring technologies among Italian banks and
its consequences on credit availability to households. Automated credit scoring techniques
represent a major innovation aimed at reducing the costs of underwriting processes and
improving the accuracy of the estimates of borrowers’ default probability. Therefore, it
must be considered a new production process (Frame and White, 2004).
The first application of credit scoring techniques took place in the US, where they were
primarily used for credit cards loans. In the last decade credit scoring has become very
common in the mortgage lending industry. Two US Congress chartered companies (Fannie
Mae and Freddie Mac) contributed significantly to the diffusion of this technology. Fannie
Mae and Freddie Mac where chartered to stabilize the mortgage market and expand op-
portunities for home ownership. They accomplished their mission by creating a secondary
market for mortgage loans. In order to reduce the informational asymmetries between loans
originators and subscribers of the securities backed by the mortgage loans, these two com-
panies require the originator to use automated underwriting technologies based on credit
scoring techniques. Their experience in the mortgage loans market and the availability of a
large number of data allowed them to develop their own automated underwriting software,
that is currently sold to the originators. Nowadays, in the US mortgage loans market, the
creditworthiness of virtually all the new borrowers is evaluated by credit scoring techniques.
Starting from the mid nineties, credit analysts argued that this technology could have
been profitably applied also to small business lending since the personal credit history of small
business owners is highly predictive of the loan repayment prospects of the business. This
implied the implementation of automated underwriting in a market that was traditionally
characterized by a pervasive use of soft information. The patterns of diffusion of credit
scoring in the small business loans market and the effects on availability, price and risk
of small business credit have been studied by some recent contributions (Akhavein, Frame
and White, 2004; Berger, Frame and Miller, 2004). The main findings of these studies
are that the first adopters were mainly large banking organizations characterized by fewer
separately chartered banks, but more branches. Moreover, credit scoring is associated with
expanded quantities, higher average prices, and greater risk levels for small business credit.
These findings are consistent with a net increase in lending to relatively risky “marginal
2borrowers” who would not otherwise receive credit, but who pay relatively high prices when
they are founded.
In Italy credit scoring diffusion is still at an early stage. This is due to several reasons.
First, the lack of comprehensive Credit Bureaus providing potential borrowers’ credit his-
tories, can complicate the construction of a standardized credit scoring mechanisms. The
largest Italian Credit Bureau i.e. the Italian Credit Register (C.R.), files information about
all bad loans and performing loans above 75,000 euro. This implies that most of information
concerning consumer credit is not reported. Therefore a complete borrower’s credit history
cannot be tracked. In order to fill this gap, some private companies have started to gather
information about those loans not filed in the C.R.. Nevertheless these private credit bureau
are not as detailed as those maintained in the US. Second, in Italy the secondary market for
loans was thin until the late 90’s, and consequently it provided little incentives for the adop-
tion of standardized credit evaluation methods. Finally, the relationships between banks
and their customers, which in Italy rely heavily on soft information, make the adoption of
automated credit scoring techniques more difficult, in particular for small business lending.
The New Basel Capital Accord will constitute a great incentive for the diffusion of credit
scoring. In order to implement the so called Internal Rating-Based approach, banks will be
required to segment their retail portfolio according to borrowers’ credit risk. As a minimum
requirement, a bank will have to segment by credit scores, including application scoring
(score based on full information in a credit application).1
The potential benefits from the diffusion of credit scoring techniques are very large. From
the borrowers’ point of view, automated underwriting simplifies the loans application pro-
cess. Borrowers are asked a limited and standardized set of questions about their financial
and socio-economic conditions and are provided with a quick answer, allowing them to re-
duce the search costs that they would have incurred under the traditional loan evaluation
processes. Further, banks can significantly reduce the marginal cost of creditworthiness eval-
uation, allowing them to assess a larger number of potential borrowers. Credit scoring might
be used as well during renegotiations, permitting the bank to redefine contracts’ conditions.
As long as these techniques are more reliable than traditional judgmental methods, their
implementation could also reduce credit risk. Finally, from a social welfare perspective, the
1
See Basel Committee on Banking Supervision (2001) and Altman (2002).
3use of credit scoring can facilitate the creation of a secondary market for loans, increasing
credit availability by allowing banks to invest in liquid assets and to improve risk manage-
ment.2 Nevertheless there still are some potential shortcomings. First, the possibility of
screening a large number of borrowers may induce banks not to expand their credit supply,
but to select more carefully their borrowers. As a consequence we might observe a decrease
in credit availability. Second, if the automated tool is not flexible enough, there is risk of
systematic exclusion of certain categories of new borrowers (Bridges and Disney, 2001), with
a negative impact on social welfare. This has been a major concern in the US mortgage
loans market in the last few years.
Our analysis is based on a database obtained from a survey that we conducted among
104 banks that had originally reported to the Supervision Department of the Bank of Italy
the usage credit scoring techniques. We received 45 answers, 43 of them confirmed their
adoption of credit scoring and provided further information, such as the date of adoption
and the market segment to which credit scoring is applied. Complementing this data with the
information coming from the Banking Supervision Reports at the Bank of Italy, we analyze
the pattern of diffusion of credit scoring among Italian banks starting from 1993. We find
that the adoption wave started in 1998, that first adopters were mainly large commercial
banks and that a main channel of diffusion was the banking group. We also estimate a non
parametric hazard model finding that the curvature of the cumulative hazard curve is very
steep, suggesting that we still are in the first stages of the diffusion process. Afterwards we
focus our attention on the consequences of credit scoring adoption on the market of mortgage
loans, using a data set of 18,245 observations referring to 103 provinces and 240 banks over
the period 1999-2002. Our main findings are that the adoption of credit scoring implies a
faster growth rate of new mortgage loans for the adopters and that this expansion is mainly
due to the possibility of financing customers that otherwise would have not been financed.
2 An overview on the CS methodology
Credit scoring is a technique used by financial intermediaries for screening applicants. This
procedure was first adopted during the fifties, mainly by finance houses and mail order
2
Whether securitzation may help to lower interest rates is still an open issue (Heuson et al., 2000).
4firms. At that time, decision were made judgementally by credit analyst: creditworthiness
evaluations largely depended on the rules of financial houses and on the experience and
knowledge of their clerks. In the sixties, when the number of applicants for credit cards
increased, an automated system was necessary. The first consultant firm was founded in
1957 in San Francisco (Bill Fair & Earl Isaac); since then, the automated credit scoring
procedure spread quickly and became a standard for risk evaluation for consumer lending,
for mortgage lending and for business lending.
Credit scoring procedures are based on statistical methods like discriminant analysis,
logistic and probit regression; in more recent developments, neural networks, genetic algo-
rithms and linear programming are used. The common idea behind these different method-
ologies, is that there exist (at least) two populations of potential borrowers, good and bad
types (with a lower and a higher average default probability, respectively). From a statis-
tical point of view, it is a problem of correct classification, i.e. to design a procedure able
to allocate a new observation into one of the two populations, minimizing a given objective
function (typically is the cost of misallocation). Accordingly, individuals are separated on
the basis of some observed characteristics belonging both to their socio-economic background
- age, income, gender, nationality, family size, etc. - and to its credit history - the number
of credit cards, how much did the applicant borrowed, if it ever delayed a repayment and so
on.3
As an example, a problem of classification of an individual in two population is sketched.
Let’s define f1 (x) and f2 (x) the probability density functions associated with the random
vector X - the observable characteristics - for the population φ1 (good types) and φ2 (bad
types). If we denote the sample space with Ω, a potential borrower with a profile must be
assigned to either φ1 or φ2 . Suppose then to split the sample space into two regions, S1 and
S2 , exhaustive and mutually exclusive, and to classify as φ1 (φ2 ) those observations for which
x ∈ S1 (S2 ). Since the probability distributions f1 (x) and f2 (x) are often overlapping, the
assignment process described above, is potentially subject to misclassification errors. To give
an example, observations belonging to the S1 (S2 ) region can be erroneously be assigned to
population φ1 (φ2 ) while coming from φ2 (φ1 ), as represented Figure 1 with the light (dark)
gray area. Each of these possible misallocation has a different cost: accordingly, one criterion
3
In the US, the Equal Credit Opportunity Act (ECOA) prevents creditors from discriminating potential
borrowers according to some characteristics, such as race, sex, marital status, nationality, and so on.
5to identify the boundary of the S regions could be the minimization of the expected cost of
misallocation. The statistical models of classification are usually based on historical data
and this rises the issue of sample attrition.4 Since the aim of all these procedures is to
give an estimate of the default, the result can be severely biased. The selection mechanism
works as follows: given a potential pool of borrowers, subdivided in good and bad types,
we can observe creditworthiness performance only of those whose application was accepted;
thus a classification process which does not take into account the source of attrition is
based only on one part of the population. In this way, an applicant’s profile is compared
against successful recipients, therefore conditioning on the sampling rule, i.e. acceptance5
Accordingly, the predictor of the default rate must take into account this rule. Besides
these problems related to the statistical significance of the sample of potential borrowers,
there is a crucial issue concerning social welfare. It may happen that a new applicant
exhibits characteristics that are “infrequent”, in the sense that they are at the boundaries
of the sample space Ω (or sometimes, even outside), and to be rejected just because similar
individuals have no credit history. From a technical point of view, these statistical models
need a population of individuals to extract the sample from: for a single bank that needs
to use these models, the population is the actual pool of its borrowers. Based on this
information, the statistical tool is calibrated: in this sense, scoring mechanism belonging
to different banks are independent. When internal information is inadequately organized or
incomplete, a “start-up” model is implemented6 this is based on a more general representative
sample (for type of loan and geographically) of more vast and generic pool of potential
borrowers.
3 Italian Banks and Technology Adoption
Recently, the Bank of Italy conducted a survey among Italian banks (excluding Cooperative
Credit Banks) about the possible consequences of the New Basel Capital Accord. Nearly
4
See Greene, 1998.
5
In probabilistic terms, if one doesn’t take into account the sample attrition problem, the estimate
will be P rob (Def ault|Acceptance), which is different from P rob (Def ault), since P rob (Def ault) and
P rob (Acceptance) are not independent.
6
In these cases, the supplier of the credit scoring technique is very likely to be an external specialized
firm.
6one hundred banks declared to have adopted credit scoring techniques. By means of a direct
contact with those banks, we obtained further information about credit scoring adoption7 ,
such as the date of adoption and the field of application. We received 45 answers: 43 of them
were complete. Then we merged this data with bank level information from the Banking
Supervision Reports at the Bank of Italy, both on the adopters and on the non adopters. The
first adoption occurred in 1989 by a subsidiary of a foreign bank. The true adoption wave
occurred from 1998 onward, as emerges from Table 1. Undoubtedly, the time to adoption is
very likely to be affected, especially at the early stages, by banks’ individual characteristics,
like size, number of branches, market share in the field of application of the credit scoring or
other non observable strategies. Concerning bank size, we would expect larger institutions
to be among the firsts to adopt credit scoring.8 Conversely, smaller banks which tend to
rely more on soft information are expected to be later adopters. A channel through which
diffusion takes place is the banking group: once the head of a group adopts automate credit
scoring techniques, it imposes the same standards to the other banks belonging to the group.
This is shown in Table 1 (column 6): in 2002, 77% of the adopters in our sample were part
of a group. The weight of adopter banks increased over time: at the end of 2002 they
accounted for the 40% of outstanding mortgage loans (from 18% in 1999) and for the 51%
of outstanding consumer credit (from 26% in 1999). The large proportion of debt extended
by adopters banks is due to the fact that, even if the number of adopters still seems small,
they represent the larger Italian banks. Columns 4 and 5 of Table 1 reports the number of
adopters from 1993 on, categorized by bank types.9 The pattern of diffusion is similar across
bank types, even though early adopters were primarily Commercial Banks. In 2002, 67%
of adopters were Commercial Banks and 33% Cooperative Banks. We also classify bank in
term of size, according to the amount of total loans: top banks (over 45 billions of euro),
big banks (from 20 to 45 billions of euro), medium sized banks (from 7 to 20 billions of
euro), small banks (from 1 to 7 billions of euro) and minor banks (less than 1 billion of
euro). In Figure 3, adopters are classified according to their size: top and medium sized
banks are the pioneers in using automated credit scoring techniques. With the additional
information gathered directly through the banks, we could identify the field of application
7
The questionnaire employed is in Italian, and it is available from the authors upon request.
8
Larger banks can benefit from scale economies, moreover, they are likely to have a more codified customer
database, necessary to implement a customized credit scoring mechanism.
9
Cooperative Credit Banks are not taken into account since they are not part of our sample.
7of the automated credit scoring techniques. In Figures 4-5-6, the patterns of adoption of
credit scoring for consumer credit (credit cards and personal loans), mortgage loans and
small business lending are reported. The pattern of diffusion resembles the one occurred in
the US: credit scoring was firstly implemented to assess the creditworthiness of borrowers
applying for credit cards and consumer credit. The strong links between the probability of
default and the personal credit histories of the borrowers make this type of loans particularly
suitable for the use of automated underwriting. Lately banks started to apply credit scoring
to mortgage loans as well. The use of this new technology in the small business credit market
is still not very disseminated, indicating how soft information can still play a fundamental
role especially in small business lending practices. In order to better examine the speed and
of adoption and the stage of the diffusion process, we estimate a non parametric hazard
model, subdividing the period from 1989 to 2002 (in days) and evaluating, for every bank
at every time interval, what the likelihood of adoption is, conditional on the fact that credit
scoring was not used before.
Formally, the hazard rate can be defined as:
P r (t ≤ T ≤ t + ∆t|t ≤ T ) f (t)
λ (t) = lim = (1)
∆t→0 ∆t 1 − F (t)
where f (t) is the density function whose cumulative density function is F (t). For each
time t, the hazard rate λ (t) is the probability of adopting the new technology in the period
t + ∆t, given that the bank did not adopt credit scoring at time t. For our purpose, we use
the integrated (or cumulated) hazard function, defined as:
Z t
Λ (t) = λ (t) dt (2)
0
which can be consistently estimated through the Nelson-Aalen estimator (Aalen, 1978 and
Nelson, 1972), defined as:
k µ
X ¶
δj
H (t) = (3)
j=1
nj
where δj represents the number of banks which adopted credit scoring in the j th time
interval, nj is the number of non adopters at the beginning of the j th time interval and k is
8the number of time intervals considered. In Figure 8, the Nelson-Aalen cumulative hazard
function is reported.10 The starting point, i.e. the time in which the first adoption occurred,
is August 1989: up to that time we observe a nearly flat line. Only after 3 thousands days
since the first adoption (i.e. more than eight years), this innovation started to spread, that
means that the true process of diffusion took place from 1998 to now. When the adoption
process is concluded, we would expect to observe a complete hazard curve S−shaped: in the
period from 1998 to 2002 the curvature of the hazard curve is very steep, suggesting that we
still are in the first stages of adoption (Geroski 2000).
4 Consequences on credit availability: empirical evi-
dence
We focus our empirical analysis on the mortgage loans market. The US experience suggests
that this is a market where the implementation of credit scoring techniques may have sub-
stantial consequences. The market for mortgage loans in Italy expanded rapidly in the past
four years: in 2002, 37 billions of euro of new mortgage loans were extended, 33% more than
in 1999. In the same period, also the implementation of credit scoring in the assessment of
credit worthiness of borrowers applying for a new mortgage loan expanded rapidly: accord-
ing to the results of our survey, in 1999 in every Italian province, on average, there were at
least 4.7 banks using credit scoring, in 2002 they were 10.9 (Table 2).
We conduct our empirical analysis on a sample referring to the period 1999-2002 and
to all Italian banks (excluding Cooperative Credit Banks) operating in the mortgage loans
market and for which data were available. Our sample indicates a significant growth of the
diffusion of credit scoring techniques. In 1999 the share of new mortgage loans extended by
adopting banks was equal to 29.5%, that became 31.5% in 2002. In the same period, the
number of provinces where more than 40% of new mortgage loans were extended by adopters
rose from 17% to 28%. In 1999 there where 40 provinces with a share smaller than 20%, in
2002 only 15.
The adoption of automated credit scoring mechanisms can have effects on credit avail-
ability both at the market and at the bank level. For this purpose we estimate two empirical
10
Taking into account that the sample is right censored; time is expressed in days.
9models: the first aims at taking into account its effect on the expansion of new mortgage
loans extended by the single banks, while the second at assessing the impact of credit scoring
on market size.
4.1 Consequences on banks’ mortgage loans expansion
We analyze the impact of credit scoring adoption on new mortgage loans extended by bank
i in province j at time t (LN M ORTi,j,t ). We assume, as shown in equation (1), that that
(LN M ORTi,j,t ) depends on whether the bank has adopted credit scoring at time t − 1
(DCSi,j,t−1 ), on banks characteristics and on two dummy variables one for province j and
one for time t.
LN M ORTi,j,t = f (DCSi,j,t−1 , SIZEi,j,t−1 , CAP IT ALi,j,t−1 , LN COST Si,j,t−1 , (4)
BRAN CHESi,j,t−1 , BRM KTi,j,t−1 , DGRi,j,t−1 , DP ROVj , DT IM Et )
Bank’s i size (SIZEi,j,t−1 ) is measured in terms of total assets, its capital (CAP IT ALi,j,t−1 )
defined as the ratio of equity capital to total assets. Cost efficiency is accounted for by the ra-
tio between operating expenses and total assets (LN COST Si,j,t−1 ); we also add network size
as a regressor, measured with the total number of branches of bank i (BRAN CHESi,j,t−1 ).
The presence in the market is captured by the variable BRM KTi,j,t−1 , which represents the
number of branches of bank i in province j. Finally, a dummy variable indicating whether
bank i belongs to a group is included. All bank-level variables refer to period t − 1 to
avoid possible endogeneity problems. The province dummy variable controls for all pos-
sible province fixed effects (i.e. different demand, market structure and overall economic
conditions), while the time dummies captures common time shocks. We use a similar spec-
ification to answer a different question: does mortgage loans supply depend upon the time
since credit scoring adoption? We expect the supply of mortgage loans to depend positively
on time since adoption, but at declining rates over time. For this reason, we substitute the
dummy variable DCSi,j,t−1 with the numbers of years since adoption (T IM Ei,j,t−1 ) and a
squared term (T IM ESQi,j,t−1 ). We estimate the different specifications using a panel data
base of 18,245 observations referring to 103 provinces and 240 banks over the period 1999-
102002. A full description of the variables can be found in the appendix, while descriptive
statistics are shown in Table 3. The estimation method is OLS regression with province
fixed-effects.11 The results are reported in Table 5. Column 1 displays the estimates for
equation (4): the coefficient of the credit scoring adoption dummy variable is positive and
significant (the coefficient is 0.303). Assuming that the expansion of new mortgage loans de-
pends linearly on time since adoption (column 2), we obtain a positive coefficient, implying
one more year of use of credit scoring increases by 0.5% the extension of mortgage loans. Our
hypothesis about a possible non linearity of this relationship is confirmed by the coefficient
of the squared term introduced in equation (4), T IM ESQ, which is negative and significant.
The value of the coefficient (−0.022) implies that the growth rate of new mortgage loans is
increasing during the first 4 years since adoption and declining afterwards.
4.2 Consequences on credit availability at the market level
The empirical evidence provided above is consistent with the hypothesis that the adoption
of credit scoring implies a faster growth rate of new mortgage loans. This may happen
in two possible ways: either adopters increase their market share at the expenses of their
competitors, or credit scoring allows to finance borrowers that otherwise would not have been
financed. In the former case, adoption would have consequences only on adopter banks. In
the latter, the whole market would result enlarged. In order to identify which of the two
effects is the prevailing one, we model new mortgage loans extended in province j at time t
(LN M ORT M KTj,t ) as a function of province level variables and information about credit
scoring diffusion, as described in equation (5).
LN M ORT M KTj,t = f (W EIGHTj,t−1 , HERFj,t−1 , RAT ESj,t−1 , (5)
GDPj,t−1 , REALESTj,t−1 , DT IM Et )
Equation (5) measures credit scoring diffusion as the ratio between the number of adopt-
ing banks on the total number of active banks in province j at time t − 1 (W EIGHTj,t−1 ).
We control for province specific effects with: the degree of competition measured by the
11
We did not include in the model bank-fixed effect which would have captured all the variations in the
characteristics of interest, since variance over time was relatively small.
11Herfindahl Index - computed on loans - (HERFj,t−1 ); the average loan rate (RAT ESj,t−1 )
as a proxy for the prevailing risk conditions; the log of real per capita GDP (GDPj,t−1 ), as an
indicator of the local economic conditions and the real estate average price (REALESTj,t−1 ),
to account for changes in the demand for real estates. All province-level variables refer to
period t − 1 in order to avoid possible endogeneity problems. Subsequently we exploit the in-
formation embedded in the variable W EIGHT , substituting it with the number of adopting
banks (SCORIN Gj,t−1 ) and the total number of banks in the market (N BAN KSj,t−1 ).12
In an alternative specification we replaced the N BAN KS variable with the Herfindahl In-
dex. We estimate the three specifications described above using a panel database of 421
observations referring to 103 Italian provinces over the period 1999-2002. A full description
of the variables can be found in the appendix, while descriptive statistics are shown in Table
4. We estimate a simple OLS regression since the variance over time is very small compared
to the overall variance.13 The results are reported in Table 6. The impact of the share of
adopters on the availability of new mortgage loans is positive and highly significant. The
coefficient is 8.354, implying a semielasticity, computed at the mean value, of 0.25 (column
1). Therefore, the hypothesis of credit market expansion as a consequence of credit scoring
adoption is corroborated by empirical evidence. The advantages of credit scoring, namely
the possibility of assessing creditworthiness of potential borrowers quickly and at relatively
lower marginal cost, allow banks to increase credit supply. This finding is robust to different
measures, like the number of adopting bank tout court (given the number of active banks
in the market), which has a positive and significant impact on new mortgage loans. The
coefficient, equal to 0.066, implies a semielasticity, computed at the mean value, of 0.30
(column 2). The coefficient of the Herfindahl Index, even if is not statistically significant,
indicates that a higher degree of competition can increase the availability of new mortgage
loans. The province controls are all highly significant and consistent across specifications.
12
In this specification we dropped the HERF variable to avoid collinearity problems with the NBANKS
variable.
13
Actually, we estimated also a fixed effects and random effects model, but the within variance was too
small to justify the use of this estimation technique. For this reason we preferred a simple OLS estimation
with time dummies.
125 Conclusions
In this paper we analyzed the pattern of diffusion of credit scoring techniques among Italian
banks. We estimate a non parametric model finding that, though the early adopters started
to implement this technology in the late 80’s, the true adoption wave started in 1998. In
the period from 1998 to 2002 the curvature of the hazard function is very steep, suggesting
that we still are in the first stages of adoption. The patterns of diffusion are significantly
different across dimensional classes: larger banks turns out to be among the first adopters,
and this is consistent with the fact that those banks can benefit from scale economies more
than their smaller counterparts. Moreover, a relevant channel of diffusion of this technology
is represented by the bank group. From a credit availability point of view, we address two
main issues. The first is related to the effects of credit scoring adoption on the supply of
new mortgage loans by single banks. The results of our econometric analysis shows that
adopter banks actually widen their supply of new mortgage loans more than non adopters.
Time since adoption is also important, in particular in the first years: after four years (on
average) the propulsive effect of credit scoring ceases to be so relevant. The second issue
concerns whether the introduction of automated credit scoring techniques have increased
or reduced overall credit availability in local markets. The empirical evidence indicates
that introduction of credit scoring has a positive and significant impact on market size: a
growth in the number of banks using this technique increases new mortgage loans in a given
market. Further issues concerned with the diffusion of credit scoring are the consequences
of the adoption of this technology on banks’ risk-taking and risk-management, we intend to
address them in future research.
13References
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14Figure 1: Example of two populations with overlapping distributions (univariate case).
φ φ
15Figure 2: Adopters of Credit Scoring for Loans: classification according to bank size. 16
Figure 3: Adopters of Credit Scoring for Consumer Credit: classification according to bank size. 17
Figure 4: Adopters of Credit Scoring for Mortgage Loans: classification according to bank size. 18
Figure 5: Adopters of Credit Scoring for Small Business Lending: classification according to bank size. 19
Figure 6: Nelson-Aalen cumulative hazard function: automated credit scoring adoption
(starting point 1989, censored at December 2002). Time expressed in days.
20Table 1: Number of banks which adopted credit scoring techniques and year of adoption.
Cumulated Number of Adopters
Year Number of
Adopters Total Commercial Cooperative Banks belonging
Banks Banks to a group
1993 3 3 2 1 3
1994 1 4 3 1 4
1995 1 5 4 1 5
1996 1 6 5 1 6
1997 2 8 6 2 8
1998 4 12 9 3 11
1999 8 20 14 6 17
2000 14 34 23 11 28
2001 5 39 25 14 31
2002 4 43 29 14 33
Table 2: Number of banks using credit scoring for mortgage loans by province.
Minimum Maximum Average
1999 2 11 4.7
2000 4 16 7.3
2001 3 17 8.8
2002 5 23 10.9
21Table 3: Descriptive statistics at the bank-province level.
Variable Mean N Max Min Std. Dev.
LN M ORT -0.57 18728 7.18 -13.82 2.36
DCS 0.1 18728 1 0 0.3
T IM E 0.25 18728 8 0 0.95
T IM ESQ 0.96 18728 64 0 5.57
SIZE 8.77 18681 12.26 1.61 1.81
CAP IT AL 6.01 18466 9.53 0 1.75
COST S 0.05 18448 0.69 0 0.03
BRAN CHES 306.15 18728 2235 0 371.71
BRM KT 5.1 18728 440 0 16.28
DGR 0.83 18728 1 0 0.38
HERF 0.09 18728 0.24 0.04 0.04
RAT ES 7.17 18728 11.27 4.16 1.22
GDP 8.93 18728 11.47 7.01 0.88
REALEST 7.38 18728 8.19 6.71 0.3
Table 4: Descriptive statistics at the province level.
Variable Mean N Max Min Std. Dev.
LN M ORT M KT 4.92 412 8.61 1.9 1.09
W EIGHT 0.03 412 0.1 0 0.02
SCORIN G 4.55 412 17 0 3.47
N BAN KS 136.18 412 393 61 45.8
HERF 0.09 412 0.24 0.03 0.04
RAT ES 7.35 412 11.27 4.16 1.23
GDP 8.69 412 11.47 7.01 0.77
REALEST 7.31 412 8.19 6.71 0.28
22Table 5: Consequences of the adoption of credit scoring on mortgage loans supply at the
bank-province level. Ordinary least square estimation with province fixed effects. Dependent
variable: logarithm of new mortgage loans by bank and province. Standard errors in brackets.
Statistically different from zero, respectively at: *** 99%, ** 95% and * 90% significance
level. Time and province dummies not reported for brevity.
(1) (2) (3)
DCS 0.303*** - -
(0.046)
T IM E - 0.054*** 0.173***
(0.013) (0.036)
T IM ESQ - - -0.022***
(0.006)
SIZE 0.549*** 0.563*** 0.546***
(0.031) (0.031) (0.032)
CAP IT AL -0.508*** -0.520*** -0.503***
(0.029) (0.030) (0.030)
COST S 6.326*** 6.406*** 6.314***
(0.618) (0.622) (0.622)
BRAN CHES 0.002*** 0.002*** 0.002***
(0.000) (0.000) (0.000)
BRM KT 0.054*** 0.054*** 0.054***
(0.004) (0.004) (0.004)
DGR -0.632*** -0.617*** -0.648***
(0.050) (0.051) (0.052)
CON ST -2.659*** -2.709*** -2.643***
(0.217) (0.218) (0.216)
N. obs. 18,245 18,245 18,245
F-test 45.73*** 44.86*** 45.21***
R-squared 0.313 0.313 0.313
23Table 6: Consequences of the adoption of credit scoring on mortgage loans availability at
the province level. Ordinary least square estimation. Dependent variable: logarithm of new
mortgage loans by province. Standard errors in brackets. Statistically different from zero,
respectively at: *** 99%, ** 95% and * 90% significance level. Time dummies not reported
for brevity.
(1) (2) (3)
W EIGHT 8.354*** - -
(2.370)
SCORIN G - 0.066*** 0.058***
(0.017) (0.017)
N BAN KS - -0.003*** -
(0.001)
HERF -0.400 - -0.272
(0.499) (0.491)
RAT ES -0.137*** -0.136*** -0.143***
(0.021) (0.022) (0.022)
GDP 1.024*** 1.079*** 0.938***
(0.033) (0.064) (0.040)
REALEST 0.703*** 0.703*** 0.664***
(0.117) (0.119) (0.119)
CON ST -8.001*** -8.680*** -7.431***
(0.791) (0.938) (0.829)
N. obs. 412 412 412
F-test 413.53*** 455.29*** 397.46***
R-squared 0.875 0.877 0.875
24Appendix
Table A1 - Data Description. All the variables are yearly and refer to the period from January 1999 to December 2002.
LN M ORTi,j,t The log of the new mortgage loans extended by bank i in province j at time t.
LN M ORT M KTj,t The log of the new mortgage loans extended in province j at time t.
T IM Ei,t−1 Number of years since bank i adopted credit scoring for mortgage loans at time t − 1.
T IM ESQi,t−1 The square of the number of years since bank i adopted credit scoring for mortgage loans at time t − 1.
W EIGHTj,t−1 Ratio between the number of adopting banks on the total number of active banks in province j at time t − 1.
DCSi,t−1 Dummy variable that takes value 1 if bank i had credit scoring at time t − 1 and 0 otherwise.
SCORIN Gi,t−1 Number of banks in province j using credit scoring at time t − 1.
SIZEi,t−1 Log of total assets of bank i at time t − 1.
CAP IT ALi,t−1 Log of bank i capital at time t − 1.
25
COST Si,t−1 Ratio between operating expenses and total assets of bank i at time t − 1.
N BAN KSj,t−1 Number of banks in province j at time t − 1.
BRAN CHESi,t−1 Number of branches of bank i at time t − 1.
BRM KTi,j,t−1 Number of branches of bank i at time t − 1 in province j.
HERFj,t−1 Herfindahl index computed on loans in province j at time t − 1.
RAT ESj,t−1 Average interest rates on loans in province j at time t − 1.
GDPj,t−1 Log of the per capita real GDP in province j at time t − 1.
REALESTj,t−1 Average real estate price in province j at time t − 1.
DGRi,t−1 Dummy variable that takes value 1 if bank i was part of a group at time t − 1 and 0 otherwise.You can also read
