A scorecard is a mathematical model that is used to predict a certain outcome. In credit this might be 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 accounts 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.
A simple example of a 2 characteristics scorecard is displayed below:
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.
The second applicant, Johnny Debt would get 10 points for “Age” and 15 points for “Dependants” again resulting in a total score of 25. Johnny would be declined by the scorecard. The third applicant William would receive scores of 20 and 25 totalling 45 points. William 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):
Scorecards are used to predict a variety of different outcomes including:
- A new applicant defaulting
- An existing loan/facility defaulting
- A customer in arrears making a successful payment
- A customer spending more on her credit card in the next 6 months
- A consumer responding to a targeted marketing campaign
- A customer lapsing on an insurance premium
- A customer closing their account
- A customer skipping the country
- A customer moving to a competitor
- A customer having an insurance claim
All these models are built in a similar way, but with different input data and outcome predictors.
What was presented above is a credit scorecard, we refer to these as traditional models. They are built utilising mathematical processes such as mathematical programming or logistical regression. These days an increasing number of lenders are opting for machine learning scorecards. These models are different in a few ways. Firstly, they are retrainable – that means models can be built and then rebuilt quickly. This is because we deploy them on a machine learning platform which prepares the data for us allowing quick retraining. Secondly the scorecards are a lot more complex utilising sometimes as much as 30 variables with multiple levels of segmentation. You can read more about our machine learning models – the advantage and disadvantages in our blog on the subject.
If you are looking for scoring services, please be in touch with us.
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