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probdefault

Likelihood of default for given dataset for a compactCreditScorecard object

Since R2019a

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Syntax

pd = probdefault(csc,data)

Description

example

pd = probdefault(csc,data) computes the probability of default for the compactCreditScorecard (csc) based on the data.

Examples

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Open Live Script

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: [1200x11 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:
    logit(status) ~ 1 + CustAge + ResStatus + EmpStatus + CustIncome + TmWBank + OtherCC + AMBalance
    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: ''
                GoodLabel: 0
              ResponseVar: 'status'
               WeightsVar: ''
        NumericPredictors: {'CustAge'  'CustIncome'  'TmWBank'  'AMBalance'}
    CategoricalPredictors: {'ResStatus'  'EmpStatus'  'OtherCC'}
            PredictorVars: {'CustAge'  'ResStatus'  'EmpStatus'  'CustIncome'  'TmWBank'  'OtherCC'  'AMBalance'}

Then use probdefault 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 probdefault function to obtain the probability of default using the newdata.

newdata = data(10:20,:);
pd = probdefault(csc,newdata)
pd = 11×1

    0.3047
    0.3418
    0.2237
    0.2793
    0.3615
    0.1653
    0.3799
    0.4055
    0.4269
    0.1915
      ⋮

Input Arguments

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Credit scorecard model, specified as a compactCreditScorecard object.

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

Dataset to apply probability of default rules, 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.

Data Types: table

Output Arguments

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Probability of default, returned as a NumObs-by-1 numerical array of default probabilities.

More About

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Default Probability

After the unscaled scores are computed (see Algorithms for Computing and Scaling Scores), the probability of the points being “Good” is represented by the following formula:

ProbGood = 1./(1 + exp(-UnscaledScores))

Thus, the probability of default is

pd = 1 - ProbGood

References

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

Version History

Introduced in R2019a

See Also

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