Anomaly Detection using Unsupervised Methods: Credit Card Fraud Case Study

Rezapour, Mahdi

doi:10.14569/ijacsa.2019.0101101

Cited by 20 publications

(14 citation statements)

References 20 publications

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“…In the case of outlier and noisy data being indiscernible in datasets, several studies illustrated that by eliminating noisy and outlier data at the preprocessing step, the predictive models' performance is improved [32][33][34]. Xia (2019) [35] proposed integration between outlier removal and gradientboosting algorithm for credit scoring on peer-to-peer lending datasets.…”

Section: A Traditional Credit Scoring Modelsmentioning

confidence: 99%

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

2021

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Most existing studies on credit scoring adapted a concept of classifier ensemble for solving an imbalanced dataset. They apply resampling methods to generate multiple training subsets for constructing multiple base classifiers. However, this approach leads to several problems that degrade the classification performance, such as problems of information loss, model overfitting, and computational cost. Thus, we propose a novel ensemble approach for developing a credit scoring model based on a cost-sensitive neural network, called Cost-sensitive Neural Network Ensemble (CS-NNE). In the proposed approach, multiple class weights are adapted to original training data, enabling the multiple base neural networks to consider imbalanced classes. Following this approach, a high diversity of multiple base classifiers without consequent problems can be achieved. The approach's effectiveness is evaluated on five real-world credit datasets. Among them is a loan-requesting dataset provided by a financial institution in Thailand. The remaining datasets are publicly available and widely used by several existing studies. The experimental results showed that the proposed CS-NNE approach improves the predictive performance over a single neural network based on imbalanced credit datasets, e.g., Thai credit dataset, by achieving 1.36%, 15.67%, and 6.11% Area under the ROC Curve (AUC), Default Detection Rate (DDR), and G-Mean (GM), respectively, and achieving the best Misclassification Cost (MC). The proposed CS-NNE approach can effectively solve a class of imbalance problems and outperform many existing models. The prediction model can well compromise between classes of default (bad credit applicants) and non-default (good credit applicants), whereas existing approaches preferred a class of non-default over default loans (having high specificity and low DDR), resulting in NPL.

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Section: A Traditional Credit Scoring Modelsmentioning

confidence: 99%

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

2021

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“…To solve these imbalanced dataset issues, the initial focus is to generate artificial fraudulent data that balances the overall data presented to the models for training. Although Random oversampling technique provides a decent starting point, its main drawback is the creation of smaller decision regions which may further contribute to the overfitting problem (7) . This observation leads to the use of oversampling methods that specifically generate synthetic data that aid this domain.…”

Section: Related Workmentioning

confidence: 99%

“…Autoencoder and MLP are both unsupervised algorithms. In (7) , three unsupervised models are used for anomaly detection, more specifically Credit Card Fraud -and it is shown that Autoencoder outperforms the other two models (One-Class SVM and robust Mahala Nobis). Autoencoder is the most commonly used unsupervised model for anomaly detection but it still falls short in performance when compared to the Multi-Layer Perceptron model.…”

Section: Introductionmentioning

confidence: 99%

Optimized Stacking Ensemble (OSE) for Credit Card Fraud Detection using Synthetic Minority Oversampling Model

Veigas¹,

Regulagadda²,

Kokatnoor³

2021

IJST

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Objectives: Credit fraud is a global threat to financial institutions due to specific challenges like imbalanced datasets and hidden patterns in real-life scenarios. The objective of this study is to propose a model that effectively identifies fraudulent transactions. Methods: Methods such as Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) that artificially generate synthetic data are used in this paper to approximate the distribution of data among the two classes in the original dataset. After balancing the dataset, the individual models Multi-Layer Perceptron (MLP), k-Nearest Neighbors algorithm (kNN) and Support Vector Machine (SVM) are trained on the augmented dataset to establish an initial improvement at the data level. These base-classifiers are further incorporated into the Optimized Stacked Ensemble (OSE) learning process to fit the meta-classifier which creates an effective predictive model for fraud detection. All base-classifiers and the final Optimized Stacked Ensemble (OSE) have been implemented to critically assess and evaluate their performances. Findings: Empirical results obtained in this paper show that the quality of the final dataset is considerably improved when Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Networks (GAN) are used as oversampling algorithms. The Multi-Layer Perceptron model showed an increase of 10% in the F1 Score while kNN and SVM showed an increase of 3% each. The optimized model is built using a Stacking Classifier that combines the GAN-improved Multi-Perceptron Model with the other standard classification models such as KNN and SVM. This ensemble outperforms the existing enhanced Multi-Layer Perceptron with near-perfect accuracy (99.86%) and an increase of 16% in F1 Score, resulting in an effective fraud detection mechanism. Novelty: For the current dataset, the Optimized Stacked Ensemble model shows an increase of 16% in F1 Score as compared to the existing Multi-Perceptron model.

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“…After conducting n‐grams, the bag of words was used to break words into individual word counts variables. The machine learning algorithms have been used in all areas of day‐to‐day life, from anomaly detection, 21 and crash prediction, 22 to sentiment analysis of the investors 23 . The below sections discuss the machine learning techniques have been used for training and evaluation of model accuracy.…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

Sentiment classification of skewed shoppers' reviews using machine learning techniques, examining the textual features

Rezapour

2020

Engineering Reports

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With the speedy growth of online shopping, it has become of crucial importance for product makers to analyze, and handle a wealth of products' reviews. However, such a high volume of reviews, along with a wide variety of opinions, makes it hard for manufacturers to know exactly how they can improve their products without having an efficient approach. For this purpose, the results of sentiment classification would help the customers to retrieve the necessary information to choose an appropriate product, and the sellers to effectively collect customer feedback in order to improve their products. Like most of the read‐world problems, the shopping review data being used in this study were imbalanced, being predominately composed of positive with only a small percentage of negative reviews. Machine learning (ML) algorithms do not perform well when data are imbalanced, as they tend to get biased toward the overrepresented data category. The synthetic minority over‐sampling technique (SMOTE) was used to address this class imbalance problem. In this study, three different ML‐based algorithms, namely the Naïve Bayes (NB), Support Vector Machine, and decision tree (DT) were employed. An extensive preprocessing procedure was taken to prepare the text datasets, and details are discussed in the manuscript. The performance analysis indicated that the DT algorithm outperforms the other two methods. As positive reviews account for the majority of the reviews, sparse words removal for the data resulted in the removal of almost all negative reviews' sentiments. Hence, the model training process is here performed on positive and negative reviews separately. A combination of the review titles with their contents, separate tokenization process, applications of various N‐gram, and maintaining stops words (e.g. “not” or “but”) were some other steps considered to improve the performance of the model.

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Anomaly Detection using Unsupervised Methods: Credit Card Fraud Case Study

Cited by 20 publications

References 20 publications

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

Optimized Stacking Ensemble (OSE) for Credit Card Fraud Detection using Synthetic Minority Oversampling Model

Sentiment classification of skewed shoppers' reviews using machine learning techniques, examining the textual features

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