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

• URL: http://arxiv.org/abs/2303.06514v1
• Date: Sat, 11 Mar 2023 22:59:37 GMT
• Title: Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data
• Authors: AlsharifHasan Mohamad Aburbeian and Huthaifa I. Ashqar
• Abstract summary: 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.
• Score: 0.8223798883838329
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search [2.4374097382908477]
  We introduce a state-of-the-art hybrid ensemble (ENS) Machine learning (ML) model that intelligently combines multiple algorithms to enhance fraud identification. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, and ENS models, respectively.
  arXiv  Detail & Related papers  (2024-02-22T09:01:42Z)
• Credit Card Fraud Detection with Subspace Learning-based One-Class Classification [18.094622095967328]
  One-Class Classification (OCC) algorithms excel in handling imbalanced data distributions. These algorithms integrate subspace learning into the data description. These algorithms transform the data into a lower-dimensional subspace optimized for OCC.
  arXiv  Detail & Related papers  (2023-09-26T12:26:28Z)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• Credit card fraud detection - Classifier selection strategy [0.0]
  Using a sample of annotated transactions, a machine learning classification algorithm learns to detect frauds. fraud data sets are diverse and exhibit inconsistent characteristics. We propose a data-driven classifier selection strategy for characteristic highly imbalanced fraud detection data sets.
  arXiv  Detail & Related papers  (2022-08-25T07:13:42Z)
• Challenges and Complexities in Machine Learning based Credit Card Fraud Detection [0.0]
  Volume of transactions, uniqueness of frauds and ingenuity of the fraudster are main challenges in detecting frauds. The advent of machine learning, artificial intelligence and big data has opened up new tools in the fight against frauds. However, the developments in fraud detection algorithms has been challenging and slow due to the massively unbalanced nature of fraud data.
  arXiv  Detail & Related papers  (2022-08-20T07:53:51Z)
• Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection [106.33204064461802]
  We review the effectiveness of a feature-level fusion of super-app customer information, mobile phone line data, and traditional credit risk variables for the early detection of identity theft credit card fraud. We evaluate our approach over approximately 90,000 users from a credit lender's digital platform database.
  arXiv  Detail & Related papers  (2021-11-05T19:10:35Z)
• Credit card fraud detection using machine learning: A survey [0.5134435281973136]
  We study data-driven credit card fraud detection particularities and several machine learning methods to address each of its intricate challenges. In particular, we first characterize a typical credit card detection task: the dataset and its attributes, the metric choice along with some methods to handle such unbalanced datasets.
  arXiv  Detail & Related papers  (2020-10-13T15:35:32Z)
• Real-Time Anomaly Detection in Edge Streams [49.26098240310257]
  We propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges. We further propose MIDAS-F, to solve the problem by which anomalies are incorporated into the algorithm's internal states. Experiments show that MIDAS-F has significantly higher accuracy than MIDAS.
  arXiv  Detail & Related papers  (2020-09-17T17:59:27Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Improving Face Recognition by Clustering Unlabeled Faces in the Wild [77.48677160252198]
  We propose a novel identity separation method based on extreme value theory. It greatly reduces the problems caused by overlapping-identity label noise. Experiments on both controlled and real settings demonstrate our method's consistent improvements.
  arXiv  Detail & Related papers  (2020-07-14T12:26:50Z)
• ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation [50.779498955162644]
  We propose ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process. Results show that the proposed framework outperformed the existing state-of-the-art framework, namely FakeGAN, in terms of AP by 7%, and 5% on the Yelp and TripAdvisor datasets.
  arXiv  Detail & Related papers  (2020-06-11T16:15:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.