cs229 Interesting Projects - FinTech

Project #1 - Loanliness: Predicting Loan Repayment Ability by Using Machine Learning Methods

Key Notes

• Evaluating and predicting the repayment ability of the loaners is important for the banks to minimize the risk of loan payment default

Consumer Financial Protection Bureau rules

• Expected income or assets
• Employment status
• Expected monthly payment
• Monthly payment on the simultaneous loans
• Monthly payment of the mortgage
• Current debt status
• Residual income
• Credit history

Data Pre-processing

• Feature concatenation
• Feature Encoding and Normalization
• Invalid/Empty Entry Replacement
• Polynomial feature transformation

Summary - Data pre-processing, classification algorithms lessons

Code - Link

Project #2 - Detecting Credit Card Fraud with Machine Learning

Key Notes

Class imbalance solutions

• Undersampling – balances the data by randomly choosing observations from the majority class to exclude
• Oversampling – balances the data by randomly oversampling the minority class
• Both – a hybrid method that employs both undersampling and oversampling
• ROSE – a synthetic data generation method that balances the data by creating artificial samples of the minority class in the neighborhood of existing examples

Models

• Logistic Regression with Quadratic Terms and LASSO Regularization
• Simple Logistic Regression
• Random Forests
• Neural Networks

Summary - Handling imbalanced data, Applying different ML algos

Project #3 - Algorithmic Trading using LSTM-Models for Intraday Stock Predictions

Key Notes

• Feature Extraction - min-max-scalar

Keep Thinking!!!