Credit card fraud detection analysis and machine learning application
Tuğrul Meker · MEF University Institutional Repository (MEF University) · 2018
Credit card fraud transaction is a common term for theft and fraud action involving a payment card such as payment or credit card or debit card as a source of funds in transactions. With increased usage of POS channel or internet in recent years, the risks of credit card fraud have increased. Mostly, these illegal activities start with a compromise of data associated with the account number or important information that required to start the financial transaction. After, literature review and exploratory data analysis, machine learning algorithms are going to use to decide whether the transaction is fraud or not. Logistic regression, decision tree, Naive-Bayes, decision forest and linear SVC’s classifier algorithms are used in this study. With re-sampling choices (random-under, random-over sampling & SMOTE), these algorithms’ performances are compared. Logistic regression, decision tree, and random forest provide best results in terms of accuracy metrics. Grid-Search is applied to those three algorithms. Decision tree algorithm is chosen as the best algorithm for credit card fraud detection. Python 3.7 is used in this study.