A Comparative Study of Credit Card Fraud Detection Using the Combination of Machine Learning Techniques with Data Imbalance Solution

Ahmed, Faroque; Shamsuddin, Rittika

doi:10.1109/cds52072.2021.00026

Cited by 5 publications

(7 citation statements)

References 17 publications

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“…technique that shows the relationship between dependent and independent variables. When the dependent variable is categorical or binary and the predictors are continuous, then categorical linear regression is easily described [38,39]. LR uses a nonlinear sigmoid function to find the best-fit parameters.…”

Section: Logistic Regression (Lr) It Is a Basic Common Classificationmentioning

confidence: 99%

“…It also enables the application of prior knowledge and logic to questionable assertions. This technique makes the assumption that the features in the data are conditionally independent [39]. The NB classifier performs based on the conditional probabilities, as shown in Eq.…”

Section: Logistic Regression (Lr) It Is a Basic Common Classificationmentioning

confidence: 99%

“…KNN The KNN classifier examines the pattern space among its nearest neighbours. A new instance that should be near when it assigns an unknown sample to the space [39]. To examine the closeness among neighbouring instances, an algorithm uses the Euclidean distance function in Eq.…”

Section: Logistic Regression (Lr) It Is a Basic Common Classificationmentioning

confidence: 99%

“…DT Another extensively used method for classification purposes is the DT. A tree has some interior knobs that reflect tests on a characteristic; each branch indicates the occurrence of that test, a concurrent terminal node grasps a target label [39]. Here, the dataset is oftentimes partitioned using either a breadth-first greedy (BFG) or a depth-first greedy (DFG) strategy, and the process is stopped after all of the features have been assigned to a given class label.…”

Section: Logistic Regression (Lr) It Is a Basic Common Classificationmentioning

confidence: 99%

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RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Tekkali

Natarajan

2023

Complex Intell. Syst.

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All financial sectors are facing the most common frauds, which are digital transactional frauds. Fraudsters have always engaged in illegal activities such as stealing personal information and logging in with unauthorised credentials. Many machine learning algorithms predict whether the transaction is factual or nonfactual but fail to decrease the processing time. Hybrid models are used in this case to identify the fraud in a quick and efficient manner. This article demarcates to construct a novel model, RDQN, i.e., deep reinforcement learning, that combines with the rough set theory. This article has three steps, including data pre-processing to determine the quality of the data, which affects the learning ability of the model, determining the structural relationship and gaining useful features from the data set using rough set theory, and doing a hybridization of DNN (deep neural network) and Q learning, which is called DQN. It uses the MISH activation function and the ReLU activation function in different layers for training dynamics in the neural network. The proposed model classifies and predicts that the transaction belongs to the category implemented by the agents by activating the reward function. The reinforcement-learning agent’s performance improves based on reward assessment. This reward function gives a more precise value for each transaction, and no fraudster can escape from the agent’s sight. This novel approach improves accuracy and reduces processing time by considering the best feature selection during the process.

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Section: Logistic Regression (Lr) It Is a Basic Common Classificationmentioning

confidence: 99%

Section: Logistic Regression (Lr) It Is a Basic Common Classificationmentioning

confidence: 99%

Section: Logistic Regression (Lr) It Is a Basic Common Classificationmentioning

confidence: 99%

Section: Logistic Regression (Lr) It Is a Basic Common Classificationmentioning

confidence: 99%

See 2 more Smart Citations

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Tekkali

Natarajan

2023

Complex Intell. Syst.

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“…The authors in [70] conduct a comparison among Deep Learning, Logistic Regression and Gradient Boosted Tree. In [71][72][73], the authors implemented LR, SVM, k-NN, NB, RF, DT, MLP methods and found that they were all robust while tree-related models have the best performance. By using an auto-encoder, the authors in [70] create features with domain expertise.…”

Section: Consumer Credit Riskmentioning

confidence: 99%

Machine learning-driven credit risk: a systemic review

Shi

Tse

Luo

et al. 2022

Neural Comput & Applic

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Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

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Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

Almarshad,

Gashgari,

Alzahrani

2023

IEEE Access

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Credit card use poses a significant security issue on a global scale, with rule-based algorithms and traditional anomaly detection being two of the most often used methods. However, they are resourceintensive, time-consuming, and erroneous. Given fewer instances than legal payments, the dataset imbalance has become a serious issue. On the other hand, the generative technique is considered an effective way to rebalance the imbalanced class issue, as this technique balances both minority and majority classes before the training. In a more recent period, GAN is considered one of the most popular data generative techniques, as it is used in significant data settings. Hence, the research under study explores a classification system to detect fraudulent credit card transactions that are being trained using the European Cardholders 2013 dataset. It has 30 features, 28 of which are hidden due to sensitive information. Fraud activity accounts for less than 1% of the entire transaction volume of $284807. Additionally, GANs is a generative model based on game theory, in which a generator G and a discriminator D compete with one another. The generator's goal is to make the discriminator uncertain. Distinguishing between instances from the generator and those from the original dataset is the discriminator's goal, and we can increase classifiers' discriminating strength by training GANs on a set of fraudulent credit card transactions. According to the outcome, our model outperformed the earlier experiments with an AUC score of 0.999. Additionally, it creates artificial data using GANs, enabling the production of a sizable volume of high-quality data. In terms of innovation and performance, this technique substantially improves over earlier research.

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A Comparative Study of Credit Card Fraud Detection Using the Combination of Machine Learning Techniques with Data Imbalance Solution

Cited by 5 publications

References 17 publications

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

RDQN: ensemble of deep neural network with reinforcement learning in classification based on rough set theory for digital transactional fraud detection

Machine learning-driven credit risk: a systemic review

Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

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