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What Is A Scorecard?

December 3, 2015 at 2:45 PM

A scorecard is a mathematical model that is used to predict a certain outcome (for example the probability of default). The information used in a scorecard can vary, but common fields include demographic characteristics (e.g. age of applicant, number of dependants, time spent in current job) and credit bureau data (e.g. number of personal loans registered to applicant, worst arrears status on all account in the last 6 months). 

Scorecards are used throughout developed and many developing credit markets to assist risk managers determine with which customers to do business. 

Scorecards are made up of a number of characteristics, typically 7-15.  These characteristics represent “questions”.  The answers to these questions are known as attributes.  For example “How old are you?” would be the characteristic and “28” might be the attribute.  The weighting is the “score” assigned to the attribute.  Typically the higher the score the lower the risk. 

Characteristic Attribute Weighting  
Age of applicant 18-25 10  
  26-45  20  
  46+ 30 +
Number of dependants 0 25  
  1-3 15 +
  4+ 5  
TOTAL SCORE   45  

There are 2 characteristics that appear in this simplified scorecard: “Age of applicant” and “Number of dependants”.  Each applicant will be grouped into an attribute group under each characteristic and each attribute will be assigned a weighting.  For example Lindsay Loan is 48 (so she gets assigned under the 3rd attribute group for age, namely “46+”).  She has 3 children so she falls under the 1-3 dependant attribute group.  Next to each attribute group she receives the scores 30 and 15 respectively.  Her total score is therefore 30+15=45.  

Scorecards typically have cut-offs.  Above the cut-off would represent acceptable risk and below the cut-off unacceptable risk.  If the cut-off in our above example is 33 then Lindsay would be approved. 

Let’s take another 2 applicants.

  Age Dependants
Lindsay Loan 48 3
Johnny Debt 23 1
William Default 33 0

The second applicant, Johnny Debt would get 10 points for “Age” and 15 points for dependants, resulting in a total score of 25.  Johnny would be declined by the scorecard. The third applicant Nick would receive scores of 20 and 25 totalling 45 points. Nick would be accepted. The scores could be represented on the following grid with the coloured squares representing the respective scores of the applicants in the examples (the red numbers being the scores below the cut-off):

A simple example of a 2 characteristics scorecard is displayed below:

 

 

 

Dependants

pts

25

15

5

pts

group

0

1-3

4+

Age

10

18-25

35

25

15

20

26-45

45

35

25

30

46+

55

45

35

Different Scorecards

Scorecards are used to predict a variety of different outcomes including: 

  1. A new applicant defaulting
  2. An existing loan defaulting
  3. An customer in arrears making a successful payment
  4. A customer spending more on her credit card in the next 6 months
  5. A consumer responding to a targeted marketing campaign
  6. A customer lapsing on an insurance premium
  7. A customer closing their account
  8. A customer skipping the country
  9. A customer moving to a competitor
  10. A customer having an insurance claim

All these models are built in a similar way, but with different input data and outcome predictors.

Thomas Maydon
Thomas Maydon
Thomas Maydon is the Head of Credit Solutions at Principa. With over 13 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.

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