Credit Line Increase Model Card

Basic information:

• Person or organization developing model:
  • Arun Rajan, arunrajan@gwu.edu,
  • Claudio Escudero, claudioescudero@gwu.edu,
  • Kyle Lyon, kylelyon@gwu.edu,
  • Elias Moreno, emoreno@gwu.edu
• Model date: August 29, 2021
• Model version: 1.0
• License: MIT
• Model implementation code: DNSC_6301_Final_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.
• Intended users: Students in GWU DNSC 6301 bootcamp.
• Out-of-scope uses: Any use beyond an educational example is out-of-scope.

Training data:

• Source of training data: GWU Blackboard, kylelyon@gwu.edu

• 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 rows: 7,500

• 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

Test Data:

• Source of test data: GWU BlackBoard / 202103_Analytics Edge and Data Ethics_DNSC_6301_10 / Electronic Reserves
• Number of rows in test data: 7500
• 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_6, BILL_AMT1:BILL_AMT6, PAY_AMT1: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: Google Colab
• Version of the modeling software:
  • scikit-learn
  • Google Colab with Python 3.6.9
• Hyperparameters or other settings of your model: Test Size, Tree Depth, Adverse Impact Ratio - AIR (0.8) and Cut Off - change to 18%

Quantitative Analysis:

• Metrics used to evaluate your final model:
  • AIR
  • Bias remediation
  • Accuracy
  • Variable importance
  • Average Impact Ratio test
  • Model performance
  • Fairness
• 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: 0.74
  • Asian-to-White AIR: 1.00
  • Black-to-White AIR: 0.85
  • Female-to-Male AIR: 1.02
  • Hispanic-to-White AIR: 0.83
• Plots:
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Ethical Considerations:

• Describe potential negative impacts of using your model:
  • Math or software problems
  • Our model is not prepared to handle missing data. If missing data is presented, it would fail and risk becoming biased. There may also be syntax errors that would damage the code.
• Real-world risks: who, what, when or how
  • There may be a real world risk of favoring a certain race over the other, or gender over the other. This happens due to discrimination and mitigation.
  • Risks include biased decision making with regards to the extension of credit line to customers. This can happen if the code is manipulated or there is biased data entered into the model. There is a risk also that the original code was developed with unrecognized bias.
• Describe potential uncertainties relating to the impacts of using your model:
  • Math or software problems
  • The software can continue to provide data that is incorrect and if it is not properly recognized and fixed this can go on and be built into the code.
• Describe any unexpected or results:
  • None