What is scoring and why you need it
Scoring
Scoring is used to rate customers according to the probability of business
event or customer action, such as timely credit return, risk of default,
retention or cross-sale.
With scored customers you can automatically take profitable decisions, such
as accept/reject credit application, increase/decrease credit limit, send/hold
cross-sale offer, etc.
Scoring technologies can be used as an objective risk management
tool, which help ensure centralized, uniform, more consistent and
reliable decision management across your organization.
Quality and profitability of scoring-based operational decisions can be
statistically monitored and gradually improved or adjusted to new market
conditions.
Scoring is the most widely used in lending for all stages of a credit
life-cycle, from borrower acquisition to customer management to debt
collection and recovery. Credit scoring examples will be used in these
materials to display scoring techniques.
Scorecard
To score customers you need a scorecard.
Variable
Points
Age
18...37
0
7
57...76
Scorecard is a mathematical model represented as a set of weights
assigned to customer’s characteristics that affect his creditworthiness or any
other target behavior modeled by the scorecard.
37...57
25
Cards
None
0
paycard
150
credit card
102
Children
To create statistically-based scorecard you need to apply statistical
(predictive analytics) method to your historical data (data about your past
and current customers).
The most widely used statistical method for scorecard development is
logistic regression.
One
None
60
0
no information
30
Two and more
98
Credit History
None
182
positive
356
avarage
155
with negative factors
0
Cut-off odds and risk groups
To make automatic decisions you need to define score cut-off (or threshold)
that will divide “Good” customers that display positive behavior (such
as good profit and timely re-payments) from “Bad” customers that most
probably will display negative behavior (such as default).
Those customers, whose score is less than the cut-off point are rejected, the
automatic decision for them will be “No”.
Those customers whose score is higher than the cut-off point are accepted,
the automatic decision for them will be “Yes”.
Based on customer score you also can segment your customers to risk
groups.
www.plug-n-score.com
Risk group is defined by odds of being “Good” relatively to
odds of being “Bad” (e.g. delinquent).
For example, a borrower that belongs to group with
odds 300:1 has very low risk of being delinquent. But if
a borrower belongs to group with odds 5:1 he/she has
unacceptable credit risk.
Even the 300:1 risk group contains 1 “Bad” customer for
every 300 “Good” customers, thus cut-off point approach
assumes that in any case you will have a small amount of
“Bad” customers in accepted segment and you will reject a
certain amount of “Good” customers.