Fraud detection is an industry where incremental gains in predictive accuracy can have large benefits for banks and customers. Banks adapt models to the novel ways in which “fraudsters” commit credit card fraud. They collect data and engineer new features in order to increase predictive power.
This project, conducted by MSDS students Gabriel Rushin, Cody Stancil and Muyang Sun, compared the algorithmic impact on the predictive power across three supervised classification models: logistic regression, gradient boosted trees and deep learning.
Rushin, Stancil and Sun also explored the benefits of creating features using domain expertise and feature engineering using an autoencoder—an unsupervised feature engineering method.
They conclude that creating features using domain expertise offers a notable improvement in predictive power. Additionally, the autoencoder offers a way to reduce the dimensionality of the data and slightly boost predictive power.
