Credit Line Increase Model Card

Basic Information

• Person or organization developing model: Shuning Ma, [email protected]
• Model date: August, 2021
• Model version: 1.0
• 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 [email protected] for more information
• How training data was divided into training and validation data: 60% training, 20% validation, 20% test
• Number of rows in training and validation data:
  • Training rows: 18,000
  • Validation rows: 6,000

Test Data

• Source of test data: GWU Blackboard, email [email protected] for more information
• Number of rows in test data: 6,000
• 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 model
• Software used to implement the model: Colaboratory & Jupiter Notebook & sklearn
• Version of the modeling software: Ubuntu 18.04.5 LTS & Python 3.7.11 & 0.22.2.post1
• Hyperparameters or other settings of your model: DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini', max_depth=6, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, presort='deprecated', random_state=12345, splitter='best')

Quantitative analysis

• Metrics used to evaluate your final model: Confusion metrix & AUC & AIR
• State the final values of the metrics for all data: training, validation, and test data:
  • Training AUC: 0.78
  • Validation AUC: 0.75
  • Test AUC: 0.75
  • Asian-to-White AIR: 1.00
  • Black-to-White AIR: 0.84
  • Female-to-Male AIR: 1.04
  • Hispanic-to-White AIR: 0.83
• Provide any plots related to your data or final model -- be sure to label the plots!:

Ethical considerations

• Describe potential negative impacts of using your model
  • Math or software problems: The importance of PAY_0 is too high, which is a bad result in real world. It tells everthing and makes the whole model even useless.
  • Real-world risks: who, what, when or how: The Adverse impact ratio of Black-to-White and Hispanic-to-White are not very well. When using this model to give credit line increase to people, less black and hispanic people will be given credit line increase compared to white people with similar conditions.
• Describe potential uncertainties relating to the impacts of using your model
  • Math or software problems: Can easily attcked by hackers changing PAY_0 to determine results they want.
  • Real-world risks: who, what, when or how: May exposed to well-known risks such as DDOS or man-in-the-middle attacks, and packages it depends on could potentially be hacked to conceal an attack payload.
• Describe any unexpected or results: When running this model, even with the same SEED, the results change slightly everytime.