Detection of Credit Card Fraud with Machine Learning Methods and
Resampling Techniques

Detection of Credit Card Fraud with Machine Learning Methods and
Resampling Techniques

machine learning, ensemble learning, classification, SMOTE, credit card fraud

Financial institutions in the form of banks provide facilities in the form of credit cards, but with the development of technology,
fraud on credit card transactions is still common, so a system is needed that can detect fraud transactions quickly and
accurately. Therefore, this study aims to classify fraudulent transactions. The proposed method is Ensemble Learning which
will be tested using the Boosting type with 3 variations, namely XGBoost, Gradient Boosting, and AdaBoost. Then, to maximize
the performance of the model, the dataset used is optimized with the Synthetic Minority Oversampling Technique (SMOTE)
function from the Imblearn library in the data train to handle imbalanced dataset conditions. The dataset used in this study is
entitled "Credit Card Fraud Detection" with a total of 284807 data which is divided into two classes: Not Fraud and Fraud.
The proposed model received a recall of 92% with Gradient Boosting, where the results increased by 10.37% compared to the
previous study using Random Forest with a recall result of 81.63%. This is because the use of SMOTE in the data train greatly
influences the classification of Not fraud and fraud classes.

Moh. Badris Sholeh Rahmatullah1
, Aulia Ligar Salma Hanani2
, Akmal M. Naim3
, Zamah Sari4
, Yufis Azhar5

University of Muhammadiyah Malang

27-12-2022

Fajar bagus W

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Indonesia

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