Hyundai Card Late Payment Prediction I-Tag

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Business Process Modeling Tool Selection

Ved Shaji Sethu

Health & Safety

Ved Alan Crooks

THE PROJECT APPROACH

Ved Javier Espinosa Gallardo

PERSONAL PROTECTIVE EQUIPMENT

Ved esra bastacıoğlu

Unpickle Best Model

Database Insert

Database Query

Pickle Best Model

I-Tags Represent Risk to Future Missed Payments

Store Predictions as I-Tags

Prediction Task

Load Unseen Data Task

Airflow Prediction DAG

Notify

Model Development

• Explore multiple algorithms and optimize with cross validation of model performance and grid/random search for optimal hyper parameters

Database Query

Transformation Pipeline Development

• Develop data transformation functions to be implemented by Airflow
• Implement tranformation functions in a transformation pipeline to be executable on both training and test data separately
• important for proper isolation during future training data refreshes (avoid training data getting into test data)

Exploratory Data Analysis

Jupyter Notebook

Evaluate & Select Best Model

• un-pickle training models
• predict test data labels
• evaluate performance
• select and pickle best model for production

Train Model N

Train Model 2

Train Model 1

• un-pickle training data frame
• implement training function to produce model
• pickle model

Transform Task

• Receive path from x-com and un-pickle data frame
• Execute Scikit-Learn transformation pipeline
• Pickle transformed training and test data frames

Load Task

Database-Prototype: Local Postgres Container

Database-Production: Hyundai Card Hive DB

Pickle Pandas data frame and transfer its path to next task

Hyundai Card Borrower Data

Find similar tags to those in the prototype to implement a real prediction model

Mock Borrower Data

Develop prototype while awaiting permission to access Hyundai Card database

Idea: Identify Potential Future Missed Payments

Prediction of late credit card payments to aid Hyundai Card with

• prior to a missed payment, market repayment plans to high risk customers who may be struggling to make monthly payments
• capturing borrower changes in financial circumstances over time
• repayment forecasting

Airflow Training DAG

Develop I-tag that indicates borrow risk of a future missed credit card payment