# Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data

arXiv: https://arxiv.org/abs/2303.06514 · HTML: https://mlsanity.com/paper/2303.06514/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective](https://mlsanity.com/paper/1611.06439/) (2016)
- [Challenges and Complexities in Machine Learning based Credit Card Fraud Detection](https://mlsanity.com/paper/2208.10943/) (2022)

## Built on by (most cited first, 3 of 3)

- [Enhancing Customer Contact Efficiency with Graph Neural Networks in Credit Card Fraud Detection Workflow](https://mlsanity.com/paper/2504.02275/) (2025)
- [Malware Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models](https://mlsanity.com/paper/2503.02144/) (2025)
- [An ML-based Approach to Predicting Software Change Dependencies: Insights from an Empirical Study on OpenStack](https://mlsanity.com/paper/2508.05034/) (2025)

## Abstract (arXiv)

The credit card has become the most popular payment method for both online and offline transactions. The necessity to create a fraud detection algorithm to precisely identify and stop fraudulent activity arises as a result of both the development of technology and the rise in fraud cases. This paper implements the random forest (RF) algorithm to solve the issue in the hand. A dataset of credit card transactions was used in this study. The main problem when dealing with credit card fraud detection is the imbalanced dataset in which most of the transaction are non-fraud ones. To overcome the problem of the imbalanced dataset, the synthetic minority over-sampling technique (SMOTE) was used. Implementing the hyperparameters technique to enhance the performance of the random forest classifier. The results showed that the RF classifier gained an accuracy of 98% and about 98% of F1-score value, which is promising. We also believe that our model is relatively easy to apply and can overcome the issue of imbalanced data for fraud detection applications.