Credit Line Increase Model Card

Basic Information

• Person or organization developing model: Agnes, agnes@gmc.com
• Model date: August, 2022
• Model version: 1.0.2
• License: MIT
• Model implementation code: DNSC_6301_Project.ipynb

Intended Use

• Primary intended uses: This model is an example probability of default classifier, with an example use case for determining eligibility for a credit line increase.
• Primary intended users: Students in GWU DNSC 6301 bootcamp.
• Out-of-scope use cases: Any use beyond an educational example is out-of-scope.

Training Data

• Data dictionary:

Name	Modeling Role	Measurement Level	Description
ID	ID	int	unique row indentifier
LIMIT_BAL	input	float	amount of previously awarded credit
SEX	demographic information	int	1 = male; 2 = female
RACE	demographic information	int	1 = hispanic; 2 = black; 3 = white; 4 = asian
EDUCATION	demographic information	int	1 = graduate school; 2 = university; 3 = high school; 4 = others
MARRIAGE	demographic information	int	1 = married; 2 = single; 3 = others
AGE	demographic information	int	age in years
PAY_0, PAY_2 - PAY_6	inputs	int	history of past payment; PAY_0 = the repayment status in September, 2005; PAY_2 = the repayment status in August, 2005; ...; PAY_6 = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; ...; 8 = payment delay for eight months; 9 = payment delay for nine months and above
BILL_AMT1 - BILL_AMT6	inputs	float	amount of bill statement; BILL_AMNT1 = amount of bill statement in September, 2005; BILL_AMT2 = amount of bill statement in August, 2005; ...; BILL_AMT6 = amount of bill statement in April, 2005
PAY_AMT1 - PAY_AMT6	inputs	float	amount of previous payment; PAY_AMT1 = amount paid in September, 2005; PAY_AMT2 = amount paid in August, 2005; ...; PAY_AMT6 = amount paid in April, 2005
DELINQ_NEXT	target	int	whether a customer's next payment is delinquent (late), 1 = late; 0 = on-time

• Source of training data: GWU Blackboard, email jphall@gwu.edu for more information
• How training data was divided into training and validation data: 50% training, 25% validation, 25% test
• Number of rows in training and validation data:
  • Training rows: 15,000
  • Validation rows: 7,500

Test Data

• Source of test data: GWU Blackboard, email jphall@gwu.edu for more information
• Number of rows in test data: 7,500
• State any differences in columns between training and test data: None

Model details

• Columns used as inputs in the final model: 'LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6'
• Column(s) used as target(s) in the final model: 'DELINQ_NEXT'
• Type of model: Decision Tree
• Software used to implement the model: Python, scikit-learn
• Version of the modeling software: 3.7.13, 1.0.2
• Hyperparameters or other settings of your model:

DecisionTreeClassifier  {'ccp_alpha': 0.0,'class_weight': None,'criterion': 'gini',
                         'max_depth': 12,'max_features': None,'max_leaf_nodes': None,
                         'min_impurity_decrease': 0.0,'min_samples_leaf': 1,'min_samples_split': 2,
                         'min_weight_fraction_leaf': 0.0,'random_state': 12345,'splitter': 'best'}

Quantitative Analysis

Correlation Heatmap

Variables have a negative correlation because they move in opposite direction. 

Iteration plot tree depth vs. training and validation AUC, and AIR

With a depth-12, the model exhibits high perfomance and minimal bias: good trade-off between best validation performance and best fairness..

• Best Model AUC:

Training AUC	Validation AUC	Test AUC
0.783722	0.749610	0.743847

• Best Model AIR:

Hispanic-to-White AIR	Black-to-White AIR	Asian-to-White AIR	Female-to-Male AIR
0.83	0.85	1.00	1.02

Ethical considerations

• Describe potential negative impacts of using your model:

  • Math or software problems: Please polish: Having a 60% accuracy rate, which mean a 40% errors will bring more discrimination.

  • Real-world risks: who, what, when or how: Please polish: less Hispanic and black people will get the credit product.

• Describe potential uncertainties relating to the impacts of using your model:

  • Math or software problems: As potential uncertainties we have inaccurate information and model failure.

  • Real-world risks: who, what, when or how? Using this model inaccurately may cause commercial or social chaos for both the company and the customers.

• Describe any unexpected or results: The model may not grasp the context of the task it performs.