Legitimate Transactions Research Articles

Machine Learning for Improved Detection and Prevention of Credit Card Fraud

Using machine learning to identify instances of credit card fraud is the primary goal of the research. In order to accomplish this, we will research and assess four distinct classification models: Decision Tree, Logistic Regression, K-Nearest Neighbours (KNN), and Support Vector Classifier (SVC). Included in the Kaggle dataset's "Time," "Amount," and "Class" sections are transaction data points that represent the likelihood of fraud. Disparities in class distribution are a big problem with this dataset, even though 99.83% of the data comes from legitimate transactions. Disparity like this has the potential to lead to biassed model training. We resolved the issue by implementing preprocessing techniques such as feature scaling and resampling. F1-Score, recall, accuracy, and precision are the measures that we use to assess the models. With scores ranging from 93% to 94%, the data demonstrate that all models are incredibly accurate. Having said that, Logistic Regression lacks precision. All things considered, Decision Tree, KNN, and SVC perform very well. The proposed model's accuracy peaked at 94%. As this study demonstrates, thorough evaluation criteria are necessary for finding the optimal model for fraud detection.

Digital Twin and IoT-Enabled Online Learning Model for Real- Time Monitoring and Control of Fraudulent Transaction in the Banking industry

Fraudulent transactions have long been a challenge in the banking industry, causing significant financial losses and operational burdens. Traditional fraud detection systems often struggle to adapt and respond in real-time to the evolving tactics of fraud. This study aims to develop a digital twin and IoT-enabled online learning model for real-time monitoring and control of fraudulent transactions in banking. The proposed system integrates digital twin technology and IoT sensors to create a virtual replica of transaction environments, enabling continuous learning and adaptation using an online learning model. Performance metrics such as detection speed, prediction accuracy, precision, recall, and F1 score were used to evaluate the system. The system was tested using a dataset of banking transactions, including legitimate and fraudulent activities. Key results include detecting fraudulent transactions 40% faster than traditional methods, reducing detection time from 5 minutes to 3 minutes. The model achieved an accuracy of 92%, an 8.24% improvement over traditional fraud detection systems. The false positive rate was reduced by 28%, meaning fewer legitimate transactions were wrongly flagged as fraudulent. Precision increased by 5.95%, reaching 89%, and recall improved by 8.75%, reaching 87%. This led to a 7.32% increase in the F1 score, reaching 88%. The system offers enhanced real-time fraud detection capabilities, reducing false positives and improving overall security and operational efficiency in the banking industry.

Investigating Credit Card Payment Fraud with Detection Methods Using Advanced Machine Learning

In the cybersecurity industry, where legitimate transactions far outnumber fraudulent ones, detecting fraud is of paramount significance. In order to evaluate the accuracy of detecting fraudulent transactions in imbalanced real datasets, this study compares the efficacy of two approaches, random under-sampling and oversampling, using the synthetic minority over-sampling technique (SMOTE). Random under-sampling aims for fairness by excluding examples from the majority class, but this compromises precision in favor of recall. To strike a balance and ensure statistical significance, SMOTE was used instead to produce artificial examples of the minority class. Based on the data obtained, it is clear that random under-sampling achieves high recall (92.86%) at the expense of low precision, whereas SMOTE achieves a higher accuracy (86.75%) and a more even F1 score (73.47%) at the expense of a slightly lower recall. As true fraudulent transactions require at least two methods for verification, we investigated different machine learning methods and made suitable balances between accuracy, F1 score, and recall. Our comparison sheds light on the subtleties and ramifications of each approach, allowing professionals in the field of cybersecurity to better choose the approach that best meets the needs of their own firm. This research highlights the need to resolve class imbalances for effective fraud detection in cybersecurity, as well as the need for constant monitoring and the investigation of new approaches to increase applicability.

Konsep Ji’alah dalam Perspektif Imam Al-Nawawi Terhadap Sistem Upah dalam Program Afiliasi Tiktok

The In today's digital era, many people are leveraging social media platforms such as TikTok to earn additional income through TikTok affiliate programs. Consequently, the use of TikTok affiliate programs is increasingly widespread and highly sought after by the public. However, there are concerns regarding the validity of the ji'alah contract, which serves as the foundation of this program. The ji'alah contract is a legitimate transaction according to Shariah law, where rewards or percentages are given to individuals who have contributed to performing a certain task. The purpose of this research is to find solutions to this issue, with a focus on the concept of ji'alah according to Imam al-Nawawi. The research method used is a literature review with a qualitative approach, involving document analysis, secondary data, and interviews with relevant parties. The findings of this research will identify the pillars of ji'alah that are in accordance with Shariah principles and relevant in the current context of TikTok affiliate programs. The implications of this research include a better understanding of the concept of ji'alah and its application in TikTok affiliate programs, as well as recommendations to ensure the compliance of these programs with Shariah principles.

Cryptocurrency fraud detection through classification techniques

Ethereum and its native cryptocurrency, Ether, have played a worthy attention in the development of the blockchain and cryptocurrency space. Its programmability and smart contract capabilities have made it a foundational platform for decentralized applications and innovations across various industries. Because of its anonymous and decentralized structure, the hotheaded expansion of cryptocurrencies in the payment space has created both enormous potential and concerns related to cybercrime, including money laundering, financing terrorism, illegal and dangerous services. As more financial institutions attempt to integrate cryptocurrencies into their networks, there is an increasing need to create a more transparent network that can withstand these kinds of attacks. In this work, we are using different classification techniques, such as logistic regression (LR), random forest (RF), k-nearest neighbors (KNN), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) for Ethereum fraud detection. The dataset we are using includes rows of legitimate transactions done using the cryptocurrency Ethereum as well as known fraudulent transactions. The “XGBoost” model, which is noteworthy, detects variations that might attract notice and prevent potential issues in this chore.

Machine Learning Approaches for Credit Card Fraud Detection

Credit card fraud detection is a critical challenge in the financial sector, necessitating the adoption of advanced machine learning algorithms for timely and accurate identification of fraudulent transactions. In this project, we investigate the efficacy of four prominent machine learning algorithms - Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees (DT), and Random Forest (RF) in detecting credit card fraud. Through a comprehensive analysis, we evaluate the performance of these algorithms in terms of accuracy, precision, recall, and F1 score using real-world credit card transaction datasets. SVM, known for its ability to construct complex decision boundaries, excels in separating fraudulent and legitimate transactions. KNN leverages the proximity-based approach to identify similarities with known instances of fraud, while Decision Trees offer interpretable insights into fraudulent patterns. Random Forest combines the predictive power of multiple decision trees to produce robust and accurate predictions. Our findings shed light on the strengths and weaknesses of each algorithm, providing valuable insights for developing effective fraud detection systems in the financial industry

Legitimate Transaction? Regulating Commercial International Marriage Brokers in South Korea

Marriages between South Korean men and women from less affluent Asian countries have been popular since the 1990s, and commercial international marriage brokers have played an important role in the trend. This article argues that the laws and regulations governing marriage brokers, such as Marriage Brokers Business Management Act (MBBMA) and consumer protection mechanisms, have reinforced the rights of citizen-husbands and legitimized claims from the men’s movement. As a result, the state’s regulation of commercial matchmaking endorses a form of commodified intimacy and protects the rights of male client-cum-“head of the family,” despite the consequences of commodifying the personhood of migrant women and legitimizing the violence of denying their personal autonomy. Data include public documents and policies from the Ministry of Gender Equality and Family, Korea Institute for Healthy Family, Korea Consumer Agency, Fair Trade Commission, and MBBMA. Analysis of these public texts reveals the legal and policy language that sanitizes and disguises unequal gender roles and discrimination against foreigners.

Next-Generation Cyber Threat Detection and Mitigation Strategies: A Focus on Artificial Intelligence and Machine Learning

The principal objective of this research was to examine strategies for detecting and mitigating cyber threats in the next generation, by underscoring Artificial Intelligence (AI) and Machine Learning (ML). This study provides a comprehensive overview of the role of AI, ML, and deep learning (DL) in the domain of cybersecurity. Furthermore, this study highlights the benefits of integrating deep learning into cybersecurity practices. The researcher explored the effectiveness of consolidating AI and ML techniques into the Feedzai security system to reinforce the detection of fraudulent activities. To validate the methodology, the investigator experimented by employing the supervised machine learning random forest algorithm on a dataset comprising historical transaction records in CSV format. The results of the research ascertained that by employing Feedzai's AI-based software combined with the random forest algorithms, future financial institutions can achieve real-time fraud detection and accurate identification of legitimate transactions. The Random Forest framework had the highest accuracy rate, at 83.94%. By contrast, the Naïve Bayes framework had an accuracy rate of 79.23%, and the KNN model had the lowest accuracy rate, of 78.74%. These results ascertained that the Random Forest system was the most effective for pinpointing cyber-attacks.

A novel credit card fraud detection using supervised machine learning model

Financial fraud, especially in credit card transactions, is a growing concern. To tackle this, data mining techniques are used to automatically analyze large and complex financial datasets. Detecting credit card fraud is tricky because the patterns of normal and fraudulent behavior keep changing, and the data about fraud is much less common compared to legitimate transactions Several techniques were tried on a dataset from European cardholders, including Decision Tree, Random Forest, SVC, XGBoost, K-Nearest Neighbors, and Logistic Regress The dataset had information from 284,786 credit card transactions. To address the challenges, six advanced data mining approaches (Logistic Regression, K-Nearest Neighbors, Support Vector Classifiers, Decision Tree, Random Forests, and XGBoost) are evaluated. A comparative analysis is conducted to identify the best-performing model.

Machine learning-based ransomware classification of Bitcoin transactions

Ransomware presents a significant threat to the security and integrity of cryptocurrency transactions. This research paper explores the intricacies of ransomware detection in cryptocurrency transactions using the Bitcoinheist dataset. The dataset encompasses 28 distinct families classified into three ransomware categories: Princeton, Montreal, and Padua, along with a white category representing legitimate transactions. We propose a novel hybrid supervised and semi-supervised multistage machine learning framework to tackle this challenge. Our framework effectively classifies known ransomware families by leveraging ensemble learning techniques such as Decision Tree, Random Forest, XGBoost, and Stacking. Additionally, we introduce a novel semi-supervised approach to accurately identify previously unseen ransomware instances within the dataset. Through rigorous evaluation employing comprehensive classification metrics, including accuracy, precision, recall, F1 score, RoC score, and prediction time, our proposed approach demonstrates promising results in ransomware detection within cryptocurrency transactions.

Fraud Detection under Siege: Practical Poisoning Attacks and Defense Strategies

Machine learning (ML) models are vulnerable to adversarial machine learning (AML) attacks. Unlike other contexts, the fraud detection domain is characterized by inherent challenges that make conventional approaches hardly applicable. In this article, we extend the application of AML techniques to the fraud detection task by studying poisoning attacks and their possible countermeasures. First, we present a novel approach for performing poisoning attacks that overcomes the fraud detection domain-specific constraints. It generates fraudulent candidate transactions and tests them against a machine learning-based Oracle , which simulates the target fraud detection system aiming at evading it. Misclassified fraudulent candidate transactions are then integrated into the target detection system’s training set, poisoning its model and shifting its decision boundary. Second, we propose a novel approach that extends the adversarial training technique to mitigate AML attacks: During the training phase of the detection system, we generate artificial frauds by modifying random original legitimate transactions; then, we include them in the training set with the correct label. By doing so, we instruct our model to recognize evasive transactions before an attack occurs. Using two real bank datasets, we evaluate the security of several state-of-the-art fraud detection systems by deploying our poisoning attack with different degrees of attacker’s knowledge and attacking strategies. The experimental results show that our attack works even when the attacker has minimal knowledge of the target system. Then, we demonstrate that the proposed countermeasure can mitigate adversarial attacks by reducing the stolen amount of money up to 100%.

Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches

Background: This paper tackles the critical challenge of detecting fraudulent transactions within the Ethereum blockchain using machine learning techniques. With the burgeoning importance of blockchain, ensuring its security against fraudulent activities is crucial to prevent significant monetary losses. We utilized a public dataset comprising 9,841 Ethereum transactions, characterized by attributes such as gas price, transaction fee, and timestamp.Methods: Our approach is bifurcated into two core phases: data preprocessing and predictive modeling. In the data preprocessing phase, we meticulously process the dataset and extract pivotal features from transactions, setting the stage for efficient predictive modeling.Findings: For predictive modeling, we employed several machine learning algorithms to discern between fraudulent and legitimate transactions. Our evaluation encompassed algorithms like decision trees, logistic regression, gradient boosting, XGBoost, and an innovative hybrid model that melds random forests with deep neural networks (DNN).Novelty: Our findings underscore that the proposed model boasts a precision rate of 97.16%, marking a substantial leap in fraudulent transaction detection on the Ethereum blockchain in comparison to prevailing methodologies. This paper augments the current efforts aimed at bolstering the security of blockchain transactions using sophisticated analytical strategies..

Probabilistic XGBoost Threshold Classification with Autoencoder for Credit Card Fraud Detection

Due to the imbalanced data of outnumbered legitimate transactions than the fraudulent transaction, the detection of fraud is a challenging task to find an effective solution. In this study, autoencoder with probabilistic threshold shifting of XGBoost (AE-XGB) for credit card fraud detection is designed. Initially, AE-XGB employs autoencoder the prevalent dimensionality reduction technique to extract data features from latent space representation. Then the reconstructed lower dimensional features utilize eXtreame Gradient Boost (XGBoost), an ensemble boosting algorithm with probabilistic threshold to classify the data as fraudulent or legitimate. In addition to AE-XGB, other existing ensemble algorithms such as Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), Random Forest, Categorical Boosting (CatBoost), LightGBM and XGBoost are compared with optimal and default threshold. To validate the methodology, we used IEEE-CIS fraud detection dataset for our experiment. Class imbalance and high dimensionality characteristics of dataset reduce the performance of model hence the data is preprocessed and trained. To evaluate the performance of the model, evaluation indicators such as precision, recall, f1-score, g-mean and Mathews Correlation Coefficient (MCC) are accomplished. The findings revealed that the performance of the proposed AE-XGB model is effective in handling imbalanced data and able to detect fraudulent transactions with 90.4% of recall and 90.5% of f1-score from incoming new transactions.

Deep Learning and Smart Contract-Assisted Secure Data Sharing for IoT-Based Intelligent Agriculture

The recent development of Internet of Things (IoT) and Unmanned Aerial Vehicles has revolutionized traditional agriculture with intelligence and automation. In a typical Intelligent Agriculture (IA) ecosystem, massive and real-time data are generated, analyzed, and sent to the Cloud Server (CS) for the purpose of addressing complex agricultural issues, such as yield prediction, water feed calculation, and so on. This helps farmer and associated stakeholders to take correct decision that improves the yield and quality of agricultural product. However, the distributed nature of IA entities and the usage of insecure wireless communication open various challenges related to data sharing, monitoring, storage, and further makes the entire IA ecosystem vulnerable to various potential attacks. In this article, we exploit deep learning and smart contract to propose a new IoT-enabled IA framework for enabling secure data sharing among its various entities. Specifically, first we develop new authentication and key management scheme to ensure secure data transmission in IoT-enabled IA. The encrypted transactions are then used by the CS to analyze and further detect intrusions by a novel deep learning architecture. In CS, the smart contract (SC)-based consensus mechanism is executed on legitimate transactions that verifies and adds the formed blocks into blockchain by a peer-to-peer CSs network. In comparison to existing competing security solutions, a rigorous comparative research demonstrates that the proposed approach provides greater security and more utility characteristics.

Optimizing Neural Networks for Imbalanced Data

Imbalanced datasets pose pervasive challenges in numerous machine learning (ML) applications, notably in areas such as fraud detection, where fraudulent cases are vastly outnumbered by legitimate transactions. Conventional ML methods often grapple with such imbalances, resulting in models with suboptimal performance concerning the minority class. This study undertakes a thorough examination of strategies for optimizing supervised learning algorithms when confronted with imbalanced datasets, emphasizing resampling techniques. Initially, we explore multiple methodologies, encompassing Gaussian Naive Bayes, linear and quadratic discriminant analysis, K-nearest neighbors (K-NN), support vector machines (SVMs), decision trees, and multi-layer perceptron (MLP). We apply these on a four-class spiral dataset, a notoriously demanding non-linear classification problem, to gauge their effectiveness. Subsequently, we leverage the garnered insights for a real-world credit card fraud detection task on a public dataset, where we achieve a compelling accuracy of 99.937%. In this context, we compare and contrast the performances of undersampling, oversampling, and the synthetic minority oversampling technique (SMOTE). Our findings highlight the potency of resampling strategies in augmenting model performance on the minority class; in particular, oversampling techniques achieve the best performance, resulting in an accuracy of 99.928% with a significantly low number of false negatives (21/227,451).

Credit Card Fraud Detection Using Machine Learning and Blockchain

Abstract: This paper proposes a secure fraud detection model that combines machine learning algorithms and blockchain technology. According to the study it notes that while blockchain is considered a secure method of integration into finance, fraud and anomalies are still common in the network. To address this, the we use two machine learning algorithms, XGboost and KMeans classifier, to classify transactions based on patterns of fraudulent and legitimate transactions .The transaction data is also classified by supervised machine learning algorithms like Random forests and Decision Tree Classifier . The proposed model integrates these algorithms with blockchain technology to detect fraudulent transactions in the Etherium network . The paper includes a security analysis of the proposed smart contract and an attacker model to protect the system from potential attacks and vulnerabilities. The precision and AUC of the models are also calculated to measure the accuracy of the system. Overall, the paper proposes an innovative approach to addressing fraud and anomalies in the network using machine learning and blockchain technology. The integration of these technologies has the potential to improve the security of online transactions and e-banking systems.

Money Laundering: Factors Leading to Money Laundering in Gold Investment Company in Malaysia

Money laundering has always been one of the biggest frauds in the world and it has also been a serious issue in Malaysia. Money laundering can destabilize the economy of a country by introducing large amounts of illicit funds into the financial system, which can distort markets, increase inflation and undermine the integrity of financial institutions. This activity can be defined simply as a process of cleaning ‘dirty’ money to make it seem like the funds come from legitimate transactions. It is classified as a criminal activity since it uses a legal business to conceal the sources of their illegal incomes. Anti-Money Laundering, Anti-Terrorism Financing and Proceeds of Unlawful Activities Act 2001 (AMLA) was established to discuss the issue of money laundering in Malaysia. Some of the famous cases regarding money laundering are 1MDB, Shafee’s case and Genneva Malaysia Sdn. Bhd. This study focuses on the case of Genneva Malaysia Sdn. Bhd. that uses the gold bullion investment to cover up their money laundering activity. The stages in money laundering are placement, layering and integration. These three stages are usually being used by all criminals involved in this fraud activity. There are several factors leading to money laundering discussed in this paper which are pressure, opportunity, rationalisation and capability. This study is using literature review matrix method to understand influences of money laundering in Malaysia. This paper proposed some recommendations on how money laundering can be reduced especially in Malaysia.

Fraud Detection in Banking Data by Machine Learning Techniques

As technology advanced and e-commerce services expanded, credit cards became one of the most popular payment methods, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in fraud requires high banking transaction costs. As a result, detecting fraudulent activities has become a fascinating topic. In this study, we consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions. We use Bayesian optimization in particular to optimize the hyperparameters while preserving practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as CatBoost and XGBoost to improve the performance of the LightGBM method by accounting for the voting mechanism. Finally, in order to improve performance even further, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one. We perform some experiments on real-world data to test the proposed methods. To better cover unbalanced datasets, we use recall-precision metrics in addition to the standard ROC-AUC. CatBoost, LightGBM, and XGBoost are evaluated separately using a 5-fold cross-validation method. Furthermore, the majority voting ensemble learning method is used to assess the performance of the combined algorithms. LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79, according to the results. By using deep learning and the Bayesian optimization method to tune the hyperparameters, we also meet the ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a significant improvement over the cutting-edge methods we compared it to.

Semi-supervised Method to Detect Fraudulent Transactions and Identify Fraud Types while Minimizing Mounting Costs

Financial fraud is a complex problem faced by financial institutions, and existing fraud detection systems are often insufficient, resulting in significant financial losses. Researchers have proposed various machine learning-based techniques to enhance the performance of these systems. In this work, we present a semi-supervised approach to detect fraudulent transactions. First, we extract and select features, followed by the training of a binary classification model. Secondly, we apply a clustering algorithm to the fraudulent transactions and use the binary classification model with the SHAP framework to analyze the clusters and associate them with a particular fraud type. Finally, we present an algorithm to detect and assign a fraud type by leveraging a multi-fraud classification model. To minimize the mounting cost of the model, we propose an algorithm to choose an optimal threshold that can detect fraudulent transactions. We work with experts to adapt a risk cost matrix to estimate the mounting cost of the model. This risk cost matrix takes into account the cost of missing fraudulent transactions and the cost of incorrectly flagging a legitimate transaction as fraudulent. In our experiments on a real dataset, our approach achieved high accuracy in detecting fraudulent transactions, with the added benefit of identifying the fraud type, which can help financial institutions better understand and combat fraudulent activities. Overall, our approach offers a comprehensive and efficient solution to financial fraud detection, and our results demonstrate its effectiveness in reducing financial losses for financial institutions.

Legality of Using Blockchain to Support INSTEX and Other Special Purpose Vehicles to Enable Humanitarian Trade with Sanctioned States

This article studies the availability, legality and risks associated with utilizing blockchain technology to support the EU’s special purpose vehicle, the Instrument in Support of Trade Exchanges (INSTEX), to enable humanitarian trade with Iran within the Joint Comprehensive Plan of Action (JCPOA). Such findings can further be applicable to other special purpose vehicles outside of INSTEX, which would help the EU to maintain foreign policy independence when third country unilateral sanctions apply. It does this by examining why INSTEX stalled and how blockchain could solve such problems. This requires an examination of the reach of US sanctions on Iran. Sanctions regimes commonly exempt financial transactions for humanitarian purposes, but it can be extremely difficult to conduct such legitimate transactions in times of sanctions. This risks human catastrophe of the most vulnerable and can undermine global pandemic relief programmes. Rather than undermine sanctions, the purpose herein is to facilitate humanitarian trade with the maximum level of control and oversight by exploring Blockchain options and legality. blockchain, special purpose vehicles, SPVs, humanitarian payments, single-purpose digital currency, sanctions, fintech, digital currencies, Sanctions Compliance