score

Compute credit scores for given dataset for a compactCreditScorecard object

Description

example

[Scores,Points] = score(csc,data) computes the credit scores and points for the compactCreditScorecard object ( csc) based on the data. Missing data translates into NaN values for the corresponding points.

Examples

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To create a compactCreditScorecard object, first create a creditscorecard object using the CreditCardData.mat file to load the data (using a dataset from Refaat 2011).

load CreditCardData.mat 
sc = creditscorecard(data)
sc = 
  creditscorecard with properties:

                GoodLabel: 0
              ResponseVar: 'status'
               WeightsVar: ''
                 VarNames: {'CustID'  'CustAge'  'TmAtAddress'  'ResStatus'  'EmpStatus'  'CustIncome'  'TmWBank'  'OtherCC'  'AMBalance'  'UtilRate'  'status'}
        NumericPredictors: {'CustID'  'CustAge'  'TmAtAddress'  'CustIncome'  'TmWBank'  'AMBalance'  'UtilRate'}
    CategoricalPredictors: {'ResStatus'  'EmpStatus'  'OtherCC'}
           BinMissingData: 0
                    IDVar: ''
            PredictorVars: {'CustID'  'CustAge'  'TmAtAddress'  'ResStatus'  'EmpStatus'  'CustIncome'  'TmWBank'  'OtherCC'  'AMBalance'  'UtilRate'}
                     Data: [1200×11 table]

Before creating a compactCreditScorecard object, you must use autobinning and fitmodel with the creditscorecard object.

sc = autobinning(sc);
sc = fitmodel(sc);
1. Adding CustIncome, Deviance = 1490.8527, Chi2Stat = 32.588614, PValue = 1.1387992e-08
2. Adding TmWBank, Deviance = 1467.1415, Chi2Stat = 23.711203, PValue = 1.1192909e-06
3. Adding AMBalance, Deviance = 1455.5715, Chi2Stat = 11.569967, PValue = 0.00067025601
4. Adding EmpStatus, Deviance = 1447.3451, Chi2Stat = 8.2264038, PValue = 0.0041285257
5. Adding CustAge, Deviance = 1441.994, Chi2Stat = 5.3511754, PValue = 0.020708306
6. Adding ResStatus, Deviance = 1437.8756, Chi2Stat = 4.118404, PValue = 0.042419078
7. Adding OtherCC, Deviance = 1433.707, Chi2Stat = 4.1686018, PValue = 0.041179769

Generalized linear regression model:
    status ~ [Linear formula with 8 terms in 7 predictors]
    Distribution = Binomial

Estimated Coefficients:
                   Estimate       SE       tStat       pValue  
                   ________    ________    ______    __________

    (Intercept)    0.70239     0.064001    10.975    5.0538e-28
    CustAge        0.60833      0.24932      2.44      0.014687
    ResStatus        1.377      0.65272    2.1097      0.034888
    EmpStatus      0.88565        0.293    3.0227     0.0025055
    CustIncome     0.70164      0.21844    3.2121     0.0013179
    TmWBank         1.1074      0.23271    4.7589    1.9464e-06
    OtherCC         1.0883      0.52912    2.0569      0.039696
    AMBalance        1.045      0.32214    3.2439     0.0011792


1200 observations, 1192 error degrees of freedom
Dispersion: 1
Chi^2-statistic vs. constant model: 89.7, p-value = 1.4e-16

Use the creditscorecard object with compactCreditScorecard to create a compactCreditScorecard object.

csc = compactCreditScorecard(sc)
csc = 
  compactCreditScorecard with properties:

              Description: ''
        NumericPredictors: {'CustAge'  'CustIncome'  'TmWBank'  'AMBalance'}
    CategoricalPredictors: {'ResStatus'  'EmpStatus'  'OtherCC'}
            PredictorVars: {'CustAge'  'ResStatus'  'EmpStatus'  'CustIncome'  'TmWBank'  'OtherCC'  'AMBalance'}

Then use score with the compactCreditScorecard object. For the purpose of illustration, suppose that a few rows from the original data are our "new" data. Use the data input argument in the score function to obtain the scores for the newdata.

newdata = data(10:20,:);
[Scores,Points] = score(csc,newdata)
Scores = 11×1

    0.8252
    0.6553
    1.2443
    0.9478
    0.5690
    1.6192
    0.4899
    0.3824
    0.2945
    1.4401
      ⋮

Points=11×7 table
     CustAge     ResStatus    EmpStatus    CustIncome     TmWBank     OtherCC     AMBalance
    _________    _________    _________    __________    _________    ________    _________

      0.23039      0.12696    -0.076317      0.43693     -0.033752     0.15842    -0.017472
      0.23039    -0.031252    -0.076317     0.052329     -0.033752     0.15842      0.35551
      0.23039      0.37641    -0.076317      0.24473     -0.044811     0.15842      0.35551
        0.479      0.12696    -0.076317      0.43693      -0.18257    -0.19168      0.35551
     0.046408      0.37641    -0.076317     0.092433     -0.033752    -0.19168      0.35551
      0.21445      0.37641      0.31449      0.24473     -0.044811     0.15842      0.35551
     -0.14036      0.12696      0.31449     0.081611     -0.033752     0.15842    -0.017472
    -0.060323    -0.031252      0.31449     0.052329     -0.033752     0.15842    -0.017472
     -0.15894      0.12696      0.31449     -0.45716     -0.044811     0.15842      0.35551
      0.23039      0.12696      0.31449      0.43693      -0.18257     0.15842      0.35551
      0.23039      0.37641    -0.076317      0.24473     -0.044811     0.15842    -0.064636

Input Arguments

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Compact credit scorecard model, specified as a compactCreditScorecard object.

To create a compactCreditScorecard object, use compactCreditScorecard or compact from Financial Toolbox™.

Dataset to be scored, specified as a MATLAB® table where each row corresponds to individual observations. The data must contain columns for each of the predictors in the compactCreditScorecard object.

Output Arguments

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Scores for each observation, returned as a vector.

Points per predictor for each observation, returned as a table.

Algorithms

The score of an individual i is given by the formula

Score(i) = Shift + Slope*(b0 + b1*WOE1(i) + b2*WOE2(i)+ ... +bp*WOEp(i))

where bj is the coefficient of the j-th variable in the model, and WOEj(i) is the Weight of Evidence (WOE) value for the i-th individual corresponding to the j-th model variable. Shift and Slope are scaling constants that can be controlled with formatpoints.

If the data for individual i is in the i-th row of a given dataset, to compute a score, the data(i,j) is binned using existing binning maps, and converted into a corresponding Weight of Evidence value WOEj(i). Using the model coefficients, the unscaled score is computed as

 s = b0 + b1*WOE1(i) + ... +bp*WOEp(i).

For simplicity, assume in the description above that the j-th variable in the model is the j-th column in the data input, although, in general, the order of variables in a given dataset does not have to match the order of variables in the model, and the dataset could have additional variables that are not used in the model.

The formatting options can be controlled using formatpoints.

References

[1] Anderson, R. The Credit Scoring Toolkit. Oxford University Press, 2007.

[2] Refaat, M. Credit Risk Scorecards: Development and Implementation Using SAS. lulu.com, 2011.

Introduced in R2019a