Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset

• URL: http://arxiv.org/abs/2206.13152v1
• Date: Mon, 27 Jun 2022 09:57:08 GMT
• Title: Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset
• Authors: Fran\c{c}ois de la Bourdonnaye, Fabrice Daniel
• Abstract summary: This paper evaluates numerous state-of-the-art resampling methods on a large real-life online credit card payments dataset. We show they are inefficient because methods are intractable or because metrics do not exhibit substantial improvements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Various problems of any credit card fraud detection based on machine learning come from the imbalanced aspect of transaction datasets. Indeed, the number of frauds compared to the number of regular transactions is tiny and has been shown to damage learning performances, e.g., at worst, the algorithm can learn to classify all the transactions as regular. Resampling methods and cost-sensitive approaches are known to be good candidates to leverage this issue of imbalanced datasets. This paper evaluates numerous state-of-the-art resampling methods on a large real-life online credit card payments dataset. We show they are inefficient because methods are intractable or because metrics do not exhibit substantial improvements. Our work contributes to this domain in (1) that we compare many state-of-the-art resampling methods on a large-scale dataset and in (2) that we use a real-life online credit card payments dataset.

Related papers

• Credit Card Fraud Detection: A Deep Learning Approach [4.0361765428523135]
  Substantial amount of money has been lost by many institutions and individuals due to fraudulent credit card transactions. This paper aims to understand & implement Deep Learning algorithms in order to obtain a high fraud coverage with very low false positive rate.
  arXiv  Detail & Related papers  (2024-09-20T11:13:16Z)
• Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly [79.07074710460012]
  adversarial vulnerability of deep neural networks (DNNs) has drawn great attention. An increasing number of transfer-based methods have been developed to fool black-box DNN models. We establish a transfer-based attack benchmark (TA-Bench) which implements 30+ methods.
  arXiv  Detail & Related papers  (2023-11-02T15:35:58Z)
• Efficient Methods for Non-stationary Online Learning [67.3300478545554]
  We present efficient methods for optimizing dynamic regret and adaptive regret, which reduce the number of projections per round from $mathcalO(log T)$ to $1$. Our technique hinges on the reduction mechanism developed in parameter-free online learning and requires non-trivial twists on non-stationary online methods.
  arXiv  Detail & Related papers  (2023-09-16T07:30:12Z)
• 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)
• Efficient Deep Reinforcement Learning Requires Regulating Overfitting [91.88004732618381]
  We show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks.
  arXiv  Detail & Related papers  (2023-04-20T17:11:05Z)
• Credit Card Fraud Detection Using Enhanced Random Forest Classifier for Imbalanced Data [0.8223798883838329]
  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.
  arXiv  Detail & Related papers  (2023-03-11T22:59:37Z)
• 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)
• 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)
• Deep Learning Methods for Credit Card Fraud Detection [3.069837038535869]
  This paper presents a study of deep learning methods for the credit card fraud detection problem. It compares their performance with various machine learning algorithms on three different financial datasets. Experimental results show great performance of the proposed deep learning methods against traditional machine learning models.
  arXiv  Detail & Related papers  (2020-12-07T14:48:58Z)
• 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)
• Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning [80.20302993614594]
  We provide a statistical analysis to overcome drawbacks of Laplacian regularization. We unveil a large body of spectral filtering methods that exhibit desirable behaviors. We provide realistic computational guidelines in order to make our method usable with large amounts of data.
  arXiv  Detail & Related papers  (2020-09-09T14:28:54Z)

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.