probdefault

Likelihood of default for given dataset for a compactCreditScorecard object

Syntax

pd = probdefault(csc,data)

Description

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

Examples

Calculate the Probability of Default for a compactCreditScorecard Object with New Data

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:
    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
    0.3049
      ⋮

Input Arguments

csc — Compact credit scorecard model

compactCreditScorecard object

Credit scorecard model, specified as a compactCreditScorecard object.

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

data — Dataset to apply probability of default rules

table

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

pd — Probability of default

array

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

More About

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