Responsible Lending
and Affordability
An Experian white paper
Simon Harben, Bureau Analytics, Decision Analytics
September 2008
Executive Summary
There is more pressure than ever on the Credit Industry
to practice responsible lending in all its dealings with
consumers. In the UK this pressure is not only coming
from the industry’s governing bodies and consumer
support groups, it is coming increasingly from the
investment community and the UK Government as well.
Although this paper describes a UK-based responsible
lending approach, the lessons learned from the UK
have implications for many other developed - and
developing - consumer credit markets.
This paper starts by examining
recent trends in indebtedness and
provides an overview of the main
factors driving the responsible
lending debate in the UK.
Illustrations of how this new
responsible lending mechanism
works are provided for both mortgage
lending and unsecured lending in the
prime sector.
It then describes how an automated
responsible lending solution
can be delivered using a new
generic mechanism for estimating
disposable income and assessing
consumer affordability.
Responsible lending in the unsecured
non-prime sector is also discussed.
The ability to deliver truly automated responsible
lending decisions has implications for all consumer
credit markets and some recommendations are
given for how this approach could be applied
outside the UK.
Responsible Lending & Affordability
Contents
1.
Introduction
4
1.1
Insolvency and bankruptcy - the UK context
4
1.2
The regulators’ view
4
2.
Responsible lending and the role of credit scoring
5
3.
Assessing disposable income and affordability
6
3.1
Estimating disposable income
6
3.2
Assessing affordability
6
3.3
Automated responsible lending
6
4.
Responsible lending in the mortgage sector
7
4.1
Mortgage quotations
7
4.2
Mortgage applications
9
5.
Responsible lending in the unsecured prime sector
10
6.
Responsible lending in the unsecured non-prime sector
11
7.
Implications for application form design
12
8.
Assessing affordability outside the UK
13
8.1
Estimating disposable income
13
8.2
The Affordability Index
13
9.
Appendices
14
9.A
Appendix A - The Consumer Indebtedness Index
14
9.B
Appendix B - Using Experian’s affordability metrics
15
9.C
Appendix C - Credit rating questionnaire
16
9.D
Appendix D - Maximum expected income algorithm
16
10.
About the author
17
11.
About Experian
18
Responsible Lending & Affordability
3
1. Introduction
1.1
Insolvency and bankruptcy the UK context
The number of people experiencing
severe financial difficulties in the UK
has grown significantly over recent
years. Figure 1 shows the increase in
insolvencies in the consumer sector
in England and Wales since 1998.
Although these numbers now
seem to have peaked, there are still
25,000 individual insolvencies in
England and Wales every 3 months.
Coupled with this, the number of
UK properties being repossessed
jumped to 18,900 (up 48% year-onyear) in the first half of 2008. And
these numbers only reflect a fraction
of the UK’s consumer debt problem.
For example, the Citizens Advice
Bureau deals with well over 1million
consumers with debt problems every
year.
But what are the root causes of the
UK’s consumer debt problem? It is
now widely accepted that one of the
main contributing factors has been
the number of consumers that have
“over borrowed” - particularly on
unsecured credit. As a consequence
of this, the UK credit industry is now
looking to place even more emphasis
on ‘affordability’ when lending to
consumers. And this means that
there is a pressing need for a means
of delivering this capability into the
highly automated systems that are
now making the vast majority of
consumer lending decisions in the
UK.
1.2 The regulators’ view
Over the last 2-3 years there has
been a greatly increased focus
on ‘responsible lending’ from the
consumer credit industry’s regulatory
bodies. The Office of Fair Trading
(OFT) has a clear remit in this area
from the 2006 Consumer Credit Act
(CCA). Under the new CCA, the
OFT now looks at business practices
that appear to be: “... deceitful or
aggressive or otherwise unfair or
improper (whether unlawful of not).”
And the OFT considers
‘irresponsible lending’ to be a
species of this: “ ... the business
4
Figure 1 – The number of individual insolvencies in England and Wales
(seasonally adjusted). Source: Insolvency Service
practices which the OFT may
consider to be deceitful or
aggressive (etc.) ... include practices
in the carrying on of a consumer
credit business that appear to
the OFT to involve irresponsible
lending.”
The 2006 CCA also includes an
‘unfair relationships test’ that can
now be applied to assess whether
a lender has treated a borrower
‘unfairly’. And, according to the
Department for Business, Enterprise
and Regulatory Reform (BERR),
‘fairness’ considerations include
a borrower’s age, experience and
financial capability. The Financial
Services Authority (FSA), which
regulates the provision of financial
services in the UK, is also pushing
for a more responsible approach to
consumer lending, particularly in the
mortgage sector:
(1) A firm must be able to show
that before deciding to enter into,
or making a further advance on, a
regulated mortgage contract, or
home purchase plan, account was
taken of the customer’s ability to
repay.
(2) A mortgage lender must make
an adequate record to demonstrate
that it has taken account of the
customer’s ability to repay for each
regulated mortgage contract that it
enters into and each further advance
that it provides on a regulated
mortgage contract. The record must
be retained for a year from the date
at which the regulated mortgage
contract is entered into or the further
advance is provided.”
(Mortgage & Home Finance: Conduct of
Business Sourcebook)
And the British Banker’s Association
(BBA) has introduced a new Banking
code that also commits to delivering
more responsible lending practices:
“Before we lend you any money or
increase your overdraft, or other
borrowing, we will assess whether we
feel you will be able to repay it.”
At the same time there is also
government recognition that
borrowers need to be responsible
too – meaning that they have to
be realistic about what level of
borrowing they can support. To this
end, government has stepped up its
efforts to educate consumers about
credit and financial management.
But this has to be viewed as a longer
term solution, and, in the meantime,
there is clearly increasing internal
and external pressure on UK lenders
to not only lend responsibly but also
to be seen to be doing so.
2. Responsible lending and the role
of credit scoring
Credit scoring has been used
successfully for many years for
assessing the creditworthiness of
new applicants for credit. For the
majority of lenders, one of the key
benefits of credit scoring was that
it could remove the need for a long
drawn out income and affordability
check, greatly speeding up the time
to process each credit application.
But credit scoring has tended to
focus on establishing an individual’s
“propensity to pay” rather than their
“ability to pay,” and while credit
scoring does deliver many of the
elements required for responsible
lending, it also has some limitations:
• Credit scoring is good at
identifying individuals that are
likely to experience financial
difficulties in the future e.g.
young people living in rented
accommodation that have a short
time in employment.
• Credit scoring is also good
at identifying consumers that
are already having repayment
problems e.g. consumers with
accounts in arrears on the Credit
Bureau.
• More recently, scoring models
have also been used to identify
consumers that are keeping up
with their credit commitments,
but that are actually highly
indebted. (Experian’s Consumer
Indebtedness Index was developed
to do just this - see Appendix A).
• But, because credit scoring has
no specific income dimension,
consumers with negative
affordability can still be given
additional credit based on their
credit score alone.
All of which implies that lenders
need to incorporate a reliable
affordability check into their
decision-making processes to
complement their credit scores and
policy rules.
And, while leaving the affordability
assessment to an underwriter to
make subjectively is one option, it is
rarely going to be the best solution.
Credit scoring has tended
to focus on establishing
‘propensity to pay’ rather
than their ability to pay.
Responsible Lending & Affordability
5
3. Assessing disposable income and
affordability
3.1 Estimating disposable income
Experian has been working on an
algorithm for estimating Effective
Disposable Income (EDI) for some
time. The traditional approach
to calculating a monthly EDI has
been to combine the following
components:
• Net Monthly Income (NMI)
[supplied via application form]
• Monthly Mortgage/Rent
[from Credit Bureau or application
form or modelled]
• Monthly Credit Commitments
[from Credit Bureau]
• Monthly Expenditure
[modelled]
In the UK, monthly expenditure
(and monthly rent) models can be
developed using data from the UK
Government’s Office for National
Statistics (ONS) Expenditure & Food
Survey. However, for highly credit
active households in particular, there
is inevitably a high degree of overlap
between monthly expenditure and
monthly credit commitments. This,
in turn, can lead to significant
underestimates of EDI for these
credit active cases - which are just
the cases that require a reliable EDI
to enable their affordability to be
assessed accurately.
Following a good deal of research
into this problem, Experian has
developed an heuristic approach
that allows for this effect by
amalgamating monthly mortgage or
rent, monthly credit commitments
(MCC) and monthly expenditure
(MEX) into ‘Monthly Outgoings’,
along the following lines:
Monthly Outgoings = Monthly
Mortgage/Rent
+ ƒ (MCC, MEX)
where the function, ƒ, is directly
related to household indebtedness.
6
Effective Disposable Income can
then be calculated using:
EDI = Net Monthly Income
- Monthly Outgoings
Section 4 provides some illustrations
of how this new EDI approach works
in practice.
3.2 Assessing affordability
While the EDI provides a useful
new tool for new business decisionmaking - particularly in the secured
lending sector - it does not appear to
provide a general predictor of ‘credit
risk’.
This is probably not that surprising
since income information rarely, if
ever, makes a significant contribution
to a credit scorecard. However, a
useful new risk predictor has been
produced by adding key application
form information and other
financial metrics to the Consumer
Indebtedness model (ref. Appendix
A). On the model development
sample, the resulting ‘affordability’
score had a Gini Coefficient of 72 - a
12% increase over the Indebtedness
score.
To make it more useable, the
affordability score was converted
into an “Affordability Index” (AI) in
the range 1-99, as follows:
1 - Very low affordability (high risk)
.
.
.
.
.
99 - Very high affordability (very low
risk)
Section 5 illustrates how the AI can
be used in practice.
3.3 Automated responsible lending
The availability of a reliable EDI and
AI means that whenever income
is supplied as part of the credit
application decision-making process,
this process can now incorporate an
automated affordability check, along
the following lines:
• Apply primary credit decision
criteria:
• Calculate the applicant’s credit
score, based on application form
and Credit Bureau information
(including an indicator of
consumer indebtedness when
available);
• Apply policy decline rules (e.g.
1 or more court judgements or
previous defaults) and a low
score cut-off;
• Decline cases with a low
Affordability Index;
• Refer cases for identity
authentication or fraud checks;
• Apply other refer rules to cases
with a moderate AI (or low EDI);
• Accept all other cases.
Appendix B provides more
information on the practical use
of Experian’s Affordability Metrics
within a lender’s application
processing environment.
4. Responsible lending in the
mortgage sector
Responsible lending is - more than
ever - a key issue for all mortgage
providers. And a key element of
responsible lending is the ability to
assess an individual’s disposable
income.
This allows Monthly Expenditure to
be generated from the MEX model
set, so Monthly Outgoings can
then be calculated as described in
Section 3.1 (with Monthly Mortgage/
Rent set to zero):
Experian’s new EDI calculation
was developed primarily for use in
the mortgage sector, specifically
to replace the traditional ‘income
multiples’ approach.
Monthly Outgoings = ƒ (MCC, MEX)
4.1 Mortgage quotations
Income multiples have been used for
many years in the mortgage industry
to give an indication of how much to
lend. The standard multiples used
to be around 3.5 times gross annual
income for single applicants and
2.75 for joint applicants. But, as UK
house prices have continued to rise,
income multiples have risen to stay
in step.
However, an increasing number of
UK lenders are now looking to move
to affordability based systems.
These reflect an individual’s income
and expenditure, which, of course, is
also the aim of the EDI calculation
described in Section 3.1.
With Net Monthly Income generated
from the stated Gross Annual
Income(s), EDI can also be
calculated using:
EDI = Net Monthly Income
- Monthly Outgoings
If a mortgage amount has been
requested as part of the quotation,
then this can be agreed if it is
reasonably close to A. If no actual
amount has been requested, then
A can be used in the mortgage
quotation, provided that it is above a
realistic threshold level.
The following figures compare
maximum mortgage amounts
calculated based on income
multiples and EDI’s for some
different applicant profiles (based on
a 25 year term and an APR of 6%).
Based on the relevant monthly
interest rate (R), the mortgage
term in months (T) and the EDI, a
maximum (repayment) mortgage
amount (A) can then be calculated
using the following equation:
A = EDI [(1 + R)T - 1] / [R (1 + R)T],
subject to the Lender’s maximum
multiple amount.
As long as a Credit Bureau
‘quotation’ search takes place as
part of the mortgage quotation
process, only the following personal
information has to be supplied to
calculate an EDI:
• Main and joint applicant gross
annual incomes
• Main and joint applicant ages
• Marital status
• Number of dependants
Responsible Lending & Affordability
7
Figure 2 – Maximum mortgage amounts for one adult with no dependants
Figure 3 – Maximum mortgage amounts for one adult with two dependants
8
Figure 4 – Maximum mortgage amounts for two adults with no dependants
Figure 5 – Maximum mortgage amounts for two adults with two dependants
For the cases with no dependants,
the EDI-based mortgage amounts
are equivalent to or exceed the
traditional multiple amounts for
incomes of £25k+. At the other
end of the scale, families with 2 (or
more) dependants have much less
disposable income and generally
have lower EDI-based mortgage
amounts as a result.
Experian has also derived a “Credit
Rating Questionnaire” that can
be used to generate an accurate
credit risk assessment as part of the
mortgage quotation process without
a full Credit Bureau search being
made - Appendix C provides details.
4.2 Mortgage applications
A similar approach to that described
in Section 4.1 can be taken for
mortgage ‘applications’, with a ‘full’
Credit Bureau Search being made in
this case.
The resulting EDI (and the Affordability Index) can then be used in the
final mortgage application decision
along the lines described in Section
3.3.
Responsible Lending & Affordability
9
5. Responsible lending in the
unsecured prime sector
To ensure that the EDI calculation
is as accurate as possible in this
scenario, the following personal
details are required:
• Main and joint applicant (or
partner’s) incomes. (If partner’s
income is not requested for
‘couples’ then the EDI calculation
is clearly less reliable in this
case.)
• Marital status
• Number of dependants
• Monthly mortgage / rent - this
can be estimated if it is not asked
as part of the credit application
process.
Again, Net Monthly Income is
calculated and Monthly Expenditure
is generated from the MEX model
set. The Credit Bureau search
supplies MCC to complete the
dataset for the full EDI calculation:
EDI = NMI - MMR - ƒ (MCC, MEX)
The AI can then be calculated and
cross-tabulated with the existing
application score, as Figure 6
illustrates:
Figure 6 – Cross-tabulating the Affordability index with the existing
application score
The main use of the AI is then to identify the two groups highlighted:
• High risk accepts that should be declined;
• Low risk refers that should be auto-accepted.
Using this approach on a recent sample of (prime) personal loan applications
produced the following results:
• Increase in declines of 2%
• Bad debt reduction of 12.5%
• Reduction in referrals of 27%
10
6. Responsible lending in the
unsecured non-prime sector
It is probably not surprising that
the Estimated Disposable Income
calculation described in Section
3.1 generates very few cases with
a positive EDI for households with
gross annual incomes of less than
£15k. In fact, the whole concept of
responsible lending is very difficult
to apply quantitatively to low income
families.
As an illustration of this, the
latest Expenditure & Food survey
shows clearly that average monthly
expenditure regularly exceeds
average monthly income for lower
income families with 2 or more
children:
This, allied to the fact that up to 50%
of sub-prime borrowers only have
state benefits as a source of income,
makes it impossible to develop an
automated responsible lending
solution that is sensitive enough for
the non-prime sector.
Instead, the existing - much more
‘qualitative’ - practices of using
face-to-face interviews and/or home
visits to assess affordability need
to continue, and in some cases may
need to be tightened in light of the
UK’s continuing consumer debt
problem.
Table 1 – Average monthly household expenditure. Source: ONS (EFS) and
Experian
The concept of responsible
lending is very difficult to
apply quantitively to low
income families.
Responsible Lending & Affordability
11
7. Implications for application form
design
Clearly, a reliable calculation of
disposable monthly income depends
largely on the information going into
it.
But, at the moment, there can be a
disconnect between the household
level EDI calculation and the
personal details requested on a
credit application form.
Partner/joint applicant income
information enables the disposable
income calculation to accurately
represent the complete ‘household’
position.
Age and employment type/status
are used in Experian’s new Maximum
Expected Income algorithm to check
that the supplied Incomes are not
unrealistic - see Appendix D for
details.
12
To make the EDI calculation as accurate as possible, the
following information should always be requested at the point of
application:
• Main applicant details
Age
Gross annual (or net monthly) income
Marital status
Number of dependants
Employment type/status
Time in employment
Accommodation status (non-mortgage applications only)
Monthly mortgage / rent (non-mortgage applications only)
• Partner (or joint applicant) details
Age
Income
Employment type/status
8. Assessing affordability outside
the UK
The problem of UK consumer
indebtedness has been widely
publicised, but it is certainly not
solely a UK problem. The following
provides some general guidelines
for how the EDI/AI approach can
be applied in other markets where
there is an increasing consumer
indebtedness problem.
8.1 Estimating disposable income
Although the form of the EDI
calculation described in Section
3.1 should have fairly general
application, its components may
have to be derived differently in other
markets. For example:
8.2 The Affordability Index
In the UK, the AI was developed by
adding key application form data and
other financial metrics to the existing
Consumer Indebtedness model (ref.
Section 3.2)
This approach should work equally
well outside the UK, but only in
markets that share both positive and
negative credit account information
via the local Credit Bureau(s).
Net monthly (household) income
Applicants’ income is generally
supplied on their credit application
form, but household income should
also be requested, particularly
in developing markets where the
‘household’ is very likely to take
collective responsibility for a debt.
Monthly mortgage/rent
This can be derived from the local
Credit Bureau if it holds mortgage
data, or it can be modelled based on
national survey data. The simplest
approach is probably just to ask this
on the application form, although
the answer is not always going to be
completely reliable.
Monthly credit commitments
This is definitely more reliable if
it is derived independently via the
local Credit Bureau, but it too can be
obtained from the application form if
it is not available otherwise.
Monthly expenditure
This is also best derived
independently because it is
notoriously difficult to obtain an
accurate estimate of expenditure
from consumers themselves.
National survey data is used to
estimate this in the UK – primarily as
a function of Income and household
composition. A similar approach
is recommended elsewhere even
if this means representatives of
the local credit industry producing
and distributing their own survey
specifically for this purpose.
The approach should work
equally well outside the
UK, but only in markets
that share both positive and
negative credit information.
Responsible Lending & Affordability
13
9. Appendices
A: The Consumer Indebtedness
Index
Experian has conducted a good deal
of research into assessing consumer
indebtedness over the last 6 years.
Experian’s analysis has focussed on
credit applications from individuals
that were showing no signs of
payment difficultly, but that were
fairly heavy users of credit.
This has mainly been aimed at
answering the following question:
“Because total unsecured debt is
not a good predictor of risk (and
therefore measure of indebtedness)
on its own, what are the main factors
that should be used to measure an
individual’s indebtedness?”
The ultimate aim of the analysis
was to derive a statistical model of
Probability (Good) based on a set
of factors that were specific to this
type of credit user, with P(Good)
measuring the probability that an
individual will continue to make all
their credit repayments over the
following 2 years.
The main focus of Experian’s
indebtedness research was then
to look for new ways of combining
the Experian credit bureau data
available on these individuals into
a meaningful set of predictors of
P(Good). The P(Good) model that
results from this approach works
extremely well on the target group
of consumers, with an uplift in
predictive power of over 30%. Table 2
shows how this ‘Indebtedness score’
predicts the likelihood of repayment
problems on these apparently ‘good’
consumers.
To summarise, the best predictors of
indebtedness were found to be:
Table 2 – The indebtedness score predicts the likelihood of
repayment problems on apparently ‘good’ consumers.
Source: Experian
By contrast, Total Unsecured lending (TUL) is much ‘flatter’ (i.e. has very
limited predictive power) as the following Table shows:
Table 3 – Total Unsecured Lending is more limited in terms of
predictive power Source: Experian
14
• The number of active credit
accounts in use. Compared to a
single account with a balance of
£20,000, finding 20 accounts each
with a balance of £1,000 was found
to be a much better measure of
true indebtedness.
• The number of revolving credit
accounts in use and the limit
utilisation on these accounts.
Given the relatively high cost of
credit on most revolving accounts,
these types of indicator were
very predictive of an individual’s
indebtedness.
• The type of neighbourhood the
consumer lives in. When an
individual lives in a neighbourhood
with lots of previous debt
problems, it’s very likely that they,
too, are going to struggle with
their debts.
Experian converts the resulting
Indebtedness Score into a
‘Consumer Indebtedness Index’
(CII), so that an individual’s CII
directly corresponds to the likelihood
that they will experience credit
repayment problems as a result of
their current level of indebtedness.
The CII is now widely used as an
additional credit risk assessment
tool throughout the UK consumer
credit industry. In time it is
anticipated that widespread use of
the CII should minimise the number
of highly indebted cases that are
given additional credit. Indeed,
the Government’s Advisory Group
on Overindebtedness cited the CII
development as a key ‘partnership
action’ for enabling responsible
lending.
B: Using Experian’s affordability
metrics
Experian’s latest set of Affordability
metrics (i.e. EDI & AI) are available
as a standalone Strategy
Management module.
This can then be embedded within
any UK lender’s application process
flow, along the following lines:
Alternatively, the algorithm and scoring models that form the basis of the EDI
and AI calculations can be coded within a lender’s own Strategy Management
system or within any other business rules engine.
Responsible Lending & Affordability
15
C: Credit rating questionnaire
This has been derived by Experian
to produce a reliable Credit Rating
for use when a Credit Bureau search
cannot be taken e.g. as part of a
mortgage quotation process.
A very accurate Credit Rating from
1 (low) to 5 (high) can be generated
based on (truthful) answers to the
following questions:
• How old are you?
• What is your employment status?
(Full-time; Part-time etc)
• How many credit accounts have
you applied for in the last 6
months?
• How many credit accounts do you
have on which you owe money?
• Have you missed payments on any
of your credit accounts in the last
6 months? (No; Only 1; 2 or more
etc)
• In the last 6 years have you
defaulted on a credit account,
incurred a CCJ or had an IVA or
been made bankrupt?
• Are you on this year’s Electoral
Roll at your current address?
• Would you say that most of your
credit accounts have been opened
in the last 18 months?
• Do you pay the full balance on all
your credit and store cards every
month?
• What is your Postcode?
A final Credit Rating (from 1 to
5) is then generated based on a
credit scoring model that scores the
responses.
16
D: Maximum expected income
algorithm
As part of its responsible lending
research, Experian has developed
a mechanism for checking whether
or not a stated income is ‘realistic’
by calculating a Maximum Expected
Income (MEI).
This mechanism estimates Gross
Annual Income (GAI) using two
new regression models, which were
developed on the following types of
credit applicant:
Age 18-30 & Stated Gross Annual
Income £30k+
Age 31+ & Stated Gross Annual
Income £30k+
Both models are based on a
combination of application form and
Credit Bureau characteristics.
The MEI is then calculated using
GAI, as follows:
MEI = MAX (GAI * 1.2, GAI + 10000)
(In practice, the MEI can be adjusted
further within a lender’s own
decisioning system to achieve their
target referral rate precisely.)
The MEI algorithm returns a
maximum expected Gross Annual
or Net Monthly Income depending
on the ‘income type’ supplied. And,
as long as the key information is
supplied for each applicant, MEI is
calculated for both Main and Joint
Applicants.
10. About the author
Simon Harben graduated from
Manchester University with an MSc
in Statistics in 1974.
In 1984 he was a founder member of
the scoring and consultancy team
that formed the nucleus for Experian
Decision Analytics.
Simon is now Head of Bureau
Analytics within Experian Decision
Analytics, and is closely involved
with all new analytics developments
within EDA. He has particular
responsibility for the development of
Experian UK’s bureau-based credit,
fraud and identity scoring models.
For more information about
Experian’s responsible
lending and Affordability
Solutions, please contact
your Experian Account
Director.
Responsible Lending & Affordability
17
11. About Experian
Experian is a global leader in
providing information, analytical and
marketing services to organisations
and consumers to help manage the
risk and reward of commercial and
financial decisions.
Combining its unique information
tools and deep understanding of
individuals, markets and economies,
Experian partners with organisations
around the world to establish and
strengthen customer relationships
and provide their businesses with
competitive advantage.
For consumers, Experian delivers
critical information that enables
them to make financial and
purchasing decisions with greater
control and confidence. Clients
include organisations from financial
services, retail and catalogue,
telecommunications, utilities, media,
insurance, automotive, leisure, ecommerce, manufacturing, property
and government sectors.
Experian plc is listed on the
London Stock Exchange (EXPN)
and is a constituent of the FTSE
100 index. Experian has corporate
headquarters in Dublin, Ireland
and has operational headquarters
in Costa Mesa, California and
Nottingham, UK. The Group employs
approximately 15,500 people in 38
countries worldwide, supporting
clients in over 65 countries around
the world. Continuing sales for
the year ended 31 March 2008 were
$4,059m (£2,020m / €2,858m).
For more information, visit
www.experianplc.com.
The word ‘Experian’ is a registered
trademark in the EU and other
countries and is owned by Experian
Ltd and/or its associated companies.
For more information, visit
the company´s website on
www.experian-da.com.
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About Experian’s Decision Analytics
division
Decision Analytics is the
international division of Experian
specialising in providing credit risk
and fraud management consulting
services and products.
Over more than 30 years, it has
developed its best practice
analytical, consulting and product
capabilities to support organisations
to manage and optimise risk; prevent,
detect and reduce fraud; meet
regulatory obligations; and gain
operational efficiencies throughout
the customer relationship.
With clients in more than 60
countries and offices in more than
30, the Decision Analytics division
of Experian delivers experience and
expertise developed from working
with national and international
organisations around the world
across a wide range of industries and
business size.
www.experian-da.com
© Experian 2007. The word “EXPERIAN” and the
graphical device are trade marks of Experian and/or
its associated companies and may be registered
in the EU, USA and other countries. The graphical
device is a registered Community design in the EU.
All rights reserved.