Credit_Line_Increase Model Card

DNSC_6301

Basic Information

• Authors : Students at the George Washington University School of Business: Group 17 : Tivon Johnson tivonj, Harsharan Gorli Harsh-5, Andrew Levy Arl34, Yingying Liu Yingying0201
• Model date: August, 2021
• Model version: 1.0
• License: Apache 2.0 License
• Model implementation code: Credit_Line_Increase.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: Professors, Students in GWU DNSC 6301 bootcamp.
• Out-of-scope use cases: This model is for educational purposes and not intended to evaluate real-world credit worthiness.

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 3.6+, Google Colab
• Version of the modeling software: v0.2.5.
• Hyperparameters or other settings of your model: max_depth = 12

Quantitative analysis:

• **Metrics used to evaluate the model and final figures:
  • 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
• **Iteration Plot of the final model (inclusive of Training AUC, Validation AUC and Hispanic-to-White AIR:

Ethical considerations:

• **Potential negative impacts of the model:
  • The model can lead to descrimination. While the model may be accurate, accuracy does not always imply the model is unbiased. Numerous factors that can lead to delinquency unfortunately can potentially be linked to race or gender. Bias testing was implemented in order to mitigate any potential descrimination.
  • According to variable importance chart, the most recent payment is the primary factor the decision tree splits on. As we have seen in the pandemic, the people that are affected most by economic disruptions are lower income individuals, who would be most in need of potential increased credit lines.
• **Potential uncertainties relating to the impact of using the model:
  • One uncertainty could be the off-label use of the model. While it is the intention of the group to use the model specifically for extending a credit line, ouse ther groups of people can potentially use the model in instance where the model has not been tested.
  • Another uncertainty can be the accuracy of the data itself. Over time, the data itself can become dated, leading to inaccurate results.
• **Other unexpected results:
  • The model still has bias and hard to fix it based on the dataset.