A Comparative Performance Analysis of Several Machine Learning Classifiers on the Credit Card Data

Credit card is considered as one of the most popular paying methods for online and regular purchases, due to the advancement in communication and electronic commerce systems. Thus, the fraud associated with these transactions increased significantly. The great utilization of electronic payment is highly affected by this fraudulent transactions, which requires urgent detection to solve this issue. Therefore, effective and efficient approaches to detect fraud in credit card transactions are needed. To catch the fraudulent transaction, a good fitting model is needed, hence researchers recommends the use of various Machine Learning (ML) techniques, because of its beneficial characteristics. The main aim of the research work is to implement an ensemble based ML techniques for Credit Card Fraud Detection (CCFD). The strength of our model is a combination of the forces of the three subsystems; Recursive Feature Elimination (RFE), CCFD's using ensemble classifiers, and Synthetic Minority Oversampling (S MOTE) to deal with the problem of unbalanced data to identify the most effective prediction features. The proposed model run typical tests on two real databases of public credit card transactions, including fraudulent and official ones. Based on the comparison of other ML methods, the extra tree classifier has performed better and achieved high efficiencies such as 96% of accuracy and 57.95% of F1-measure.

With healthcare being critical aspect, health insurance has become an important scheme in minimizing medical expenses. Medicare is an example of such a healthcare insurance initiative in the United States. Following this, the healthcare industry has seen a significant increase in fraudulent activities owing to increased insurance, and fraud has become a significant contributor to rising medical care expenses, although its impact can be mitigated using fraud detection techniques. To detect fraud, machine learning techniques are used. The Centers for Medicaid and Medicare Services (CMS) of the United States federal government released “Medicare Part D” insurance claims is utilized in this study to develop fraud detection system. Employing machine learning algorithms on a class-imbalanced and high dimensional medicare dataset is a challenging task. To compact such challenges, the present work aims to perform feature extraction following data sampling, afterward applying various classification algorithms, to get better performance. Feature extraction is a dimensionality reduction approach that converts attributes into linear or non-linear combinations of the actual attributes, generating a smaller and more diversified set of attributes and thus reducing the dimensions. Data sampling is commonly used to address the class imbalance either by expanding the frequency of minority class or reducing the frequency of majority class to obtain approximately equal numbers of occurrences for both classes. The proposed approach is evaluated through standard performance metrics such as F-measure and AUC score. Thus, to detect fraud efficiently, this study applies autoencoder as a feature extraction technique, synthetic minority oversampling technique (SMOTE) as a data sampling technique, and various gradient boosted decision tree-based classifiers as a classification algorithm. The experimental results show the combination of autoencoders followed by SMOTE on the LightGBM (short for, Light Gradient Boosting Machine) classifier achieved best results.

Identification of financial transactions as suspicious or fraudulent transactions that are indicated as money laundering is mostly done manually so that it is not optimal. Data mining techniques can be a solution to overcome the limitations of the manual method. The main challenge in applying data mining techniques for financial fraud detection is an imbalanced dataset, where the proportion of fraud class is much smaller than non-fraud. This causes the model to produce unbalanced precision and recall, resulting in a low f1score. It means that the model can predict one class well, but not with another class. In this paper, the approach to fraud detection in financial transactions is carried out with classifier optimization based on Support Vector Machine (SVM). Optimization is performed by tuning the kernels and hyperparameters combined with the Random Under Sampling (RUS) technique. Specifically, RUS is used to handle imbalanced datasets and cut model training time. With this combination technique, the classifier can detect fraud more effectively with an increase in precision of 40.82% and f1-score of 22.79% compared to the previous study. A combination technique can be an approach to cover weaknesses left behind by a single method.

With the rapid evolution of the technology, the world is turning to use credit cards instead of cash in their daily life, which opens the door to many new ways for fraudulent people to use these cards in a bad way. According to the Nilson report, global card losses are expected to exceed $35 billion by 2020. To ensure the safety of users for these credit cards, the credit card’s provider should provide a service to protect users from any risk they may face. Consequently, we present our approach to predict legitimate or fraud transactions on the IEEE-CIS Fraud Detection dataset provided by Kaggel. Our model is BiLSTM- MaxPooling-BiGRU-MaxPooling which based on bidirectional Long short-term memory (BiLSTM) and bidirectional Gated recurrent unit (BiGRU). We also applied six machine learning classifiers which are: Naïve base, Voting, Ada boosting, Random Forest, Decision Tree, and Logistic Regression. Comparing the results from machine learning classifiers and our model the results show that our model achieved better as we got 91.37% score.

Fraud in financial transactions is a critical issue for businesses, governments, and consumers, causing substantial financial losses and eroding trust in financial systems. Rule-based systems and other conventional fraud detection techniques have trouble identifying complex fraudulent activity. This research applies various machine learning (ML) algorithms to detect fraud in financial transactions. The research compares the performance of supervised ML techniques, such as random forest, logistic regression, and decision tree classifier, using a publicly available Kaggle dataset. It evaluates the models based on accuracy, precision, recall, and F1 scores. To address data imbalance in both undersampling and oversampling techniques, with a focus on oversampling techniques such as Synthetic Minority Over-Sampling Method (SMOTE) to enhance model performance. Results show that the Decision Tree Classifier, when used with oversampling, outperforms other models, achieving a 99% accuracy in detecting fraudulent transactions. This highlights the effectiveness of using ensemble and hybrid models in combination with oversampling to enhance the identification of fraud. The findings emphasize the importance of using advanced ML techniques and robust data preprocessing for detection of financial fraud.

In ordinary credit card datasets, there are far fewer fraudulent transactions than ordinary transactions. In dealing with the credit card imbalance problem, the ideal solution must have low bias and low variance. The paper aims to provide an in-depth experimental investigation of the effect of using a hybrid data-point approach to resolve the class misclassification problem in imbalanced credit card datasets. The goal of the research was to use a novel technique to manage unbalanced datasets to improve the effectiveness of machine learning algorithms in detecting fraud or anomalous patterns in huge volumes of financial transaction records where the class distribution was imbalanced. The paper proposed using random forest and a hybrid data-point approach combining feature selection with Near Miss-based undersampling technique. We assessed the proposed method on two imbalanced credit card datasets, namely, the European Credit Card dataset and the UCI Credit Card dataset. The experimental results were reported using performance matrices. We compared the classification results of logistic regression, support vector machine, decision tree, and random forest before and after using our approach. The findings showed that the proposed approach improved the predictive accuracy of the logistic regression, support vector machine, decision tree, and random forest algorithms in credit card datasets. Furthermore, we found that, out of the four algorithms, the random forest produced the best results.

Over the last few years, credit card fraud (CCF) has emerged as a serious concern in financial risk detection.Classification algorithms are among the robust methods to detect, analyze, and predict such frauds with lowcomplexity, pre-detection, and loss estimation. However, class imbalance—where the number of legitimatetransactions far exceeds the number of fraudulent ones—adversely affects a classifier’s performance, and theresults become divergent for different performance measures due to their multifaceted outcomes. This poses a keychallenge in ranking such algorithms. Moreover, fraud data is inherently imbalanced with non-static behavior, soa single classifier or a group of classifiers cannot provide satisfactory results for all imbalance ratios. Motivatedby this observation, we analyze the impact of class imbalance through 10 classifiers and 8 performance measures,which are finally ranked by Multi Criteria Decision Making (MCDM) techniques for a unified approach. Resultsare analyzed for low skewed datasets, where classification algorithms outperformed for low skewed CCF datasets.Based on our findings, we present a unified approach for ranking the classification algorithms in relation todifferent imbalance ratios in credit card datasets. This study can be very useful for financial institutions toincrease their fraud-catching rate.

An effective fraud detection using competitive swarm optimization based deep neural network

The escalating threat of fraud in financial institutions is a global issue, with the Malaysian sector being no exception. This study focuses on the implementation and efficacy of Data Mining and Machine Learning methodologies in identifying and mitigating fraudulent activities within these institutions. The paper critically reviews existing literature, bridging the gap between advanced technology application and fraud management. Fraudulent transactions in the financial sector are dynamic and sophisticated, requiring advanced detection techniques. Traditional approaches often struggle to manage this complexity effectively, demonstrating a need for more advanced and adaptive strategies. This is where Data Mining and Machine Learning techniques, renowned for their predictive and analytical prowess, can significantly contribute. Data Mining, the process of uncovering patterns and correlations within large datasets, is a useful tool for detecting anomalies that may suggest fraud. The study assesses various data mining techniques, such as clustering, classification, and association, and explores their application in detecting fraudulent transactions. Findings indicate that these techniques can substantially enhance fraud detection rates while minimizing false positives. Furthermore, Machine Learning, an artificial intelligence subset, has shown immense potential in fraud detection. Its ability to learn from and make decisions based on data makes it a viable solution for fraud detection. This paper explores both supervised and unsupervised learning algorithms and their efficacy in identifying fraud in the Malaysian financial sector. Results suggest that machine learning models, when correctly implemented, can significantly improve the accuracy of fraud detection. The review underscores the importance of employing advanced technologies like Data Mining and Machine Learning to combat financial fraud effectively. It also suggests future research directions, emphasizing the need for context-specific, localized models considering Malaysia's unique socio-economic environment. Moreover, the development of hybrid models, integrating both data mining and machine learning, could offer improved results. In conclusion, this study sets a precedent for further exploration into the application of advanced analytical tools in fraud detection in the Malaysian financial sector. The potential these technologies offer for improving accuracy and adaptability in fraud detection systems is substantial and warrants thorough investigation. Keywords: Fraud Detection, Malaysian Financial Institutions, Data Mining, Machine Learning, Financial Fraud Management

Credit card fraud detection strategies with consumer incentives

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

Bank fraud is an increasingly prevalent issue, causing substantial financial losses for both financial institutions and customers. Also, with the increasing prevalence of bank fraud, especially facilitated by online banking and digital payments, there is a pressing need for potential expertise deployment and advanced fraud detection techniques in real-world banking systems. This study specified the Bayesian Network models for detecting fraudulent bank transactions. Bayesian Networks offer a probabilistic graphical modeling approach that can effectively capture complex relationships and dependencies within financial data. Thus, the research aimed at developing a custom Bayesian network model trained on a large dataset of bank transactions comprising over one million bank transactions to classify instances as fraudulent or non-fraudulent. Python 3.11.10 was used for the graphical representations. Down-sampling was employed to reduce the dataset from its initial size of 1 million observations to 17,650 observations, ensuring a balanced representation of both fraud and non-fraud instances. Various estimation techniques: Maximum Likelihood, Bayesian Networks, and Expectation Maximization were employed and evaluated to learn the model parameters. The model's performance was measured using metrics: accuracy, precision, recall, F1-score, and ROC-AUC. The Bayesian Networks estimator achieved an overall accuracy of 66.18%, precision of 67.73%, F1-score of 64.06%, ROC-AUC of 66.13%, Recall of 60.77%, Sensitivity of 60.77% and Specificity of 71.50%. Also, the Maximum Likelihood achieved an overall Accuracy of 66.77%, Precision of 69.22%, F1-score of 63.96%, ROC-AUC of 66.71%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 73.97%. Likewise, the Expectation Maximization achieved an overall Accuracy of 66.83%, Precision of 69.31%, F1-score of 64.00%, ROC-AUC of 66.77%, Recall of 59.45%, Sensitivity of 59.45% and Specificity of 74.09%. On the confusion matrix, the model correctly classified 1272 instances as Non-Fraudulent transactions (True Negative). Also, it was observed that the model incorrectly classified 507 instances as fraudulent transactions when they were Non-Fraudulent (False Positive). The model similarly incorrectly classified 687 instances as Non-Fraudulent transactions when they were Fraudulent (False Negative). Finally, it correctly classified 1064 instances as fraudulent transactions (True Positive). These results demonstrated the Bayesian Network's ability to identify fraudulent transactions while minimizing false alarms accurately. The findings highlight the potential of Bayesian Networks as a robust framework for fraud detection in the banking sector, contributing to enhanced security and reduced financial losses. The developed model can be introduced into existing banking systems to strengthen fraud prevention strategies.

Abstract: Ability of debit card companies to detect and prevent fraudulent transactions is crucial to safeguard customers from unauthorized charges. Data Science, particularly Machine Learning, plays a pivotal role in addressing this challenge. This project aims to demonstrate the application of machine learning in Credit debit Fraud Detection by modeling a dataset of past credit card transactions, distinguishing fraudulent ones from legitimate ones. The objective is to achieve a fraudulent transactions while minimizing false positives. Debit Card Fraud Detection is a classic classification issue. A research focuses on data analysis, preprocessing, and the utilization of XGBoost on Credit Card Transaction data. To prevent overfitting, grid search is employed to fine-tune the models' hyperparameters. The performed of XGBoost and P-XGBoost is compared with further usual machine learning techniques. Surprisingly, P-XGBoost best XGBoost in fraud detection, presenting a viewpoint for effectively identifying fraudulent behavior while ensuring the privacy of clients.

Fraud detection is an enduring topic that pose a threat to banking, insurance, financial sectors and information security systems such as intrusion detection systems (IDS), etc. Data mining and machine learning techniques help to anticipate and quickly detect fraud and take immediate action to minimize costs. This paper starts with the definition of intrusion detection system and its types, focuses on the implementation of a set of well-known machine learning classification algorithms (Decision Trees, Naive Bayes and Artificial Neural Networks), which can reduce the existing disadvantages of the intrusion detection systems. Experimental results on NSL-KDD dataset infer that our ANN-MLP method (Multilayer Perceptron) yields average better performance by calculating "confusion matrix" that in turn helps us to calculate performance measure such as, "Detection Rate Accuracy", "precision" and "recall".

The detection of anomalous events in large multivariate data is sought in many domains. Analysis of data is an important fraud detection procedure in detecting suspicious events and prevent attempts to defraud. While now the data is becoming more complicated and difficult as data scales and complexities increase than ever before, the rich insights within the data may be difficult to identify by traditional means and often remain hidden. People require powerful tools to extract valid conclusions from the data while maintaining trustworthy and interpretable results. Hence, various fraud detection approaches have started to exploit Visual Analytics (VA) techniques to reveal the hidden knowledge in such fraudulent activities. Interactive data visualization tools have substantial potential for making the detection of fraudulent transactions more efficient and effective by allowing the investigator to change the representation of data from text and numeric into graphics and filter out subsets of transactions for further fraud investigation. However, little research to date has directly examined the efficacy of data visualization techniques for fraud detection especially telecommunication fraud. In this paper, we present an overview of several fraud detection solutions that use data visualization techniques to detect fraudulent transactions in the telecommunication domain. The paper concludes by discussing how academic research might proceed in investigating the efficacy of interactive data visualization tools for fraud detection.