1. Building a scoring
tool – scoring model
vs scoring card.
What is scoring?
I understand scoring as a statistical procedure
of bad and good clients separation in a credit
allowance process.
Why scoring is used for?
I think scoring can be used for many objectives
like e.g.:
1. automatisation of your credit decision,
2. systematisation of taking decision,
3. pacing different clients,
4. costs reduction with staff training,
5. centralization of credit politics,
6. gathering clients data for analysis,
7. satisfy supervisor requirements.
How to built scoring?
Based on Fair Isaac school I could distinguish
following steps:
1. Preparation - setting goals and objectives,
data base creation and data collection;
2. Data cleaning - checking missing values,
double entries, outliers, redundant information,
no meaningful information, low volatility etc;
3. Rough data preparation – examining
correlation and sensitivity of selected variables;
4. Choosing an appropriate statistical technique
for scoring model – regression, classification or
segmentation and building the model;
5. Pre-validation – verification of the model
from the statistical point of view;
6. Optimising the model from the business
point of view – making strategy decisions on
how many risky clients we want to have;
7. Building a scoring card - transition of model
assessment into easy and understandable
numeric output, scaling the result into
different range of scores can produce different
score cards;
8. Final model validation based on score card
performance;
9. Model deployment – integrating scoring
model into the core system;
10. Model monitoring, periodical validation
and developing – models are good when there
are created but economic environment is
dynamic. Thus model should be tracked on
instantly.
Summary:
I am sound experienced with scoring “manufacturing”, my experience are based on Fair Isaac
approach, actually known as FICO (NYSE). In this brief draft I will cover following topics:
1. short description and characteristic of scoring process
2. process of how to build a scoring card
3. explaining scoring and rating process
4. calibration process
5. reject inference assumptions
6. cut off politics
7. how to turn scoring model into credit strategy

2. What is the difference between scoring
model and rating model?
Well, for me the difference is very clear. There
in no rating model, there is only rating
classification and rating result. Using scoring
model one can valuate a client and get his
scoring result. Based on this banks segregate
clients into groups with separate rating value.
As a scoring is a number range thus ratings are
often alphabet items (e.g. AAA, AA+) or
descriptive.
What exactly means score above 720?
The most famous and biggest scoring producers
are companies like: Equifax®, Experian®,
FICO®. This companies created an industry
standard for scoring calibration , starting from
500 points - the lowest score. Thus there are
following grades:
Thus 720 credit score means that client has
excellent or very good rating depending on
presented score cards example.
What tools or software are used for scoring?
In banks a standard tool is a SAS® software, for
scoring modelling are needed following SAS®
modules like: BASE®, STAT®, IML®, ETS®.
But models can also be build with other
statistical software like IMB SPSS Statistic®,
Dell/StatSoft STATISTICA ®, STATA® or open
source R.
Are there any special tools for scorecards
and to calibrate scoring range?
Scorecards can be builded with above
mentioned software packages but a good idea is
using dedicated for scorecards software like:
SAS Enterprice Miner®, IBM SPSS Modeler®,
Dell/StatSoft STATISTICA Scorecard®.
What are types of scoring?
There are two types of scoring: application and
behaviour. The first is for new clients and
consist of a kind of questionnaires based on
clients demographic and social variables. The
second is for existed clients for whose one can
have some previous credit history.
Actually is a new approach to use behavioural
scoring for new clients. This approach base on
getting data from third companies e.g. library
and building credit scoring based on types of
books clients read. This approach seems for me
to be more interesting because institution can
promote and target sales to special segments of
clients.
What does a reject inference mean?
It is a process of building score cards where one
is trying to optimise the model with a credit
strategy. First one should compare relation
between bad and good clients on the market
and in an examined data sample. Data set for
modelling should be comparable with an overall
market tendency i.e. ratio between bad and
good clients should not be different. Then one
can agree to attract a part of more risky clients
to its portfolio to increase a total number of
clients or increase its market share with a new
product. Practically it is a choosing a cut off
point on a model scoring scale.
How to set up a right cut off point?
For set up a cut off points I use confusion
matrix tool and based on it accuracy ratio
indicators. This tool calculate effectiveness of
the model when shifting acceptance level by
one or more scoring points change model total
acceptance level and model bad rate.
In the score table we can see a cut off point
settled at 675 and 660 score points for score
card 1 and 2 respectively.
.

3. How we can manage credit strategy with
scoring models?
Selecting a cut off one is a straight
implementation a credit strategy to the model.
Taken strategy should answer if possible default
of more risky clients can be covered with
increased profit from higher number of all new
clients.
What is exactly model validation process?
There are three moments when validation is in
a process. Pre-validation just after construction
of the model. Final validation after creation of
scoring cards. And periodical validation of
models after a some working experience.
The stages of validation are as follows:
1. qualitative - checking objectives and
assumptions of the implemented model;
2. quantitative – performing statistical tests
using last available data.
Test should answer the following questions on:
1. goodness of fit model to data;
2. model predictive power;
3. variable prediction stability;
4. accuracy of score card calibration.
Moreover I use to prepare adequate scoring
model reports like:
1. approval rate by score;
2. misalignment report;
3. scorecard accuracy.
For appropriate model analysis and have
enough information how a score card is working
I recommend to perform:
1. portfolio vintage analysis;
2. portfolio delinquency migration analysis;
3. portfolio roll rate analysis.
What kind of statuesque techniques can be
used for scoring model?
For scoring models I use statuesque methods
like: regression, classifications or clustering.
Also a new approach with survival analysis can
be used.
If a really scoring model can be useful and
accurate?
There are really good examples of working
scoring models and used statistical analysis.
With this “artificial intelligence” approach it is
possible to predict many of clients behaviour.
There was a good example of this methodology
when one of selling companies provide to one
of his clients an offer for pregnant women. It
was o big surprise for the client because she did
not know yes she was in a need.
E-Mail: radoslaw.haraburda@gmail.com
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