Fraudulent Transactions Research Articles

Financial Fraud Transaction Prediction Approach Based on Global Enhanced GCN and Bidirectional LSTM

Financial Fraud Transaction Prediction Approach Based on Global Enhanced GCN and Bidirectional LSTM

TRANSFORMING BANKING SECURITY: THE ROLE OF DEEP LEARNING IN FRAUD DETECTION SYSTEMS

In the digital banking landscape, the increasing volume of online transactions has heightened the risk of fraudulent activities, necessitating the development of more effective detection systems. This study investigates the efficacy of various machine learning and deep learning algorithms in identifying fraudulent transactions, emphasizing Long Short-Term Memory (LSTM) networks. We implemented and evaluated multiple algorithms, including Logistic Regression, Random Forest, Gradient Boosting Machines (GBM), and XGBoost, on a large-scale credit card transaction dataset. Our results demonstrate that the LSTM model outperforms traditional machine learning algorithms, achieving an accuracy of 98.5%, precision of 87.2%, recall of 85.0%, and an Area Under the Curve (AUC) score of 0.94. These findings highlight the superior capability of LSTM networks to capture complex patterns in sequential transaction data, making them an asset for real-time fraud detection in banking. This research underscores the need for financial institutions to adopt advanced deep learning techniques to enhance their fraud detection systems, thereby minimizing financial losses and improving customer trust.

Leveraging Mixture of Experts and Deep Learning-Based Data Rebalancing to Improve Credit Fraud Detection

Credit card fraud detection is a critical challenge in the financial sector due to the rapidly evolving tactics of fraudsters and the significant class imbalance betweenegitimate and fraudulent transactions. Traditional models, while effective to some extent, often suffer from high false positive rates and fail to generalize well to emerging fraud patterns. In this paper, we propose a novel approach that integrates a Mixture of Experts (MoE) model with a Deep Neural Network-based Synthetic Minority Over-sampling Technique (DNN-SMOTE) to enhance fraud detection performance. The MoE modeleverages multiple specialized expert networks, each trained to detect specific types of fraud, while the DNN-SMOTE generates high-quality synthetic samples to address the class imbalance. Our experimental results on a publicly available dataset demonstrate that the proposed method achieves a classification accuracy of 99.93%, a true positive rate of 84.69%, and a true negative rate of 99.95%. The Matthews Correlation Coefficient (MCC) of 0.7883 further highlights the model’s balanced performance in detecting fraudulent transactions. These results underscore the effectiveness of combining MoE with DNN-SMOTE, offering a robust solution for real-world credit card fraud detection scenarios.

Securing transactions: a hybrid dependable ensemble machine learning model using IHT-LR and grid search

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.

Machine learning algorithms to prevent fraudulent transactions in real-time

In the rapidly evolving landscape of digital finance, the proliferation of online transactions has been accompanied by a significant increase in fraudulent activities. This paper explores the application of machine learning algorithms to detect and prevent fraudulent transactions in real-time, thereby enhancing the security and reliability of financial systems. We investigate various machine learning techniques, including supervised learning models such as Random Forest, Logistic Regression, and Neural Networks, as well as unsupervised methods like anomaly detection. By leveraging these algorithms, we aim to identify patterns and anomalies indicative of fraudulent behavior. Our approach integrates robust preprocessing techniques and real-time data analysis to ensure high accuracy and efficiency. The results demonstrate that machine learning models can significantly reduce the incidence of fraud, providing financial institutions with a powerful tool to safeguard their customers’ assets. This study underscores the potential of machine learning to transform fraud detection, offering a scalable and adaptive solution to one of the most pressing challenges in the financial sector

Etika Bisnis dalam Akuntansi Menurut Perspektif Surat Al-Baqarah Ayat 282

Sharia accounting is no longer something new nowadays. In sharia accounting, there is a cleanup of assets as a responsibility in the form of a financial report on the use of funds. However, the facts in the field are that there are many cases in the field related to fraudulent transactions in accounting reports which can cause losses to many parties. For example, PT KAI, Jiwasraya, Garuda Indonesia, etc. Of course, this is not in line with Islamic business ethics which always pay attention to the benefit of all parties. In Surah al-Baqarah verse 282 also contains values ​​related to accounting principles, namely accountability, justice, truth and honesty. For this reason, in this research the researcher used a qualitative approach with a library study method and utilized information sources from the internet. With the aim of exploring and providing solutions regarding abuses of accountability when related to Islamic business ethics based on the perspective of al-Baqarah verse 282. The result is in Surah al-Baqarah verse 282 which includes the principles of accountability, justice and truth. Therefore, this verse can be implemented in business because it is important for companies to implement sharia accounting which is based on the values ​​contained in this verse, so that they can produce healthy and responsible financial reports in accordance with Islamic business ethics.

Improvement The Accuracy of Convolutional Neural Network with Using Undersampling Method on Unbalanced Credit Card Dataset

In this study, we address the challenge of imbalanced data in credit card fraud detection by proposing a novel approach that leverages Convolutional Neural Networks (CNNs) and undersampling techniques. The imbalance in the dataset, typical of real-world financial transactions, often leads to biased models favoring the majority class. To mitigate this, we employ undersampling to balance the classes, thereby enhancing the CNN's ability to learn from minority instances crucial for fraud detection. Our method is validated on a large unbalanced credit card dataset, demonstrating significant improvements in accuracy compared to traditional CNN models trained on imbalanced data. We evaluate our approach using standard performance metrics, including precision, recall, and F1-score, showcasing its effectiveness in accurately identifying fraudulent transactions while minimizing false positives. Furthermore, we pro-vide insights into the CNN's decision-making process through visualization techniques, shedding light on its ability to discern fraudulent patterns within the data. Our findings highlight the importance of addressing class imbalance in fraud detection tasks and underscore the efficacy of undersampling in enhancing the performance of deep learning models, particularly CNNs, in handling imbalanced datasets.

Cryptocurrency Transaction Fraud Detection Based on Imbalanced Classification With Interpretable Analysis

This study introduces an interpretable imbalanced data classification method for detecting cryptocurrency transaction fraud. We address data imbalance using SMOTE oversampling and data augmentation through contrastive learning. Next, we introduce a Transformer-based deep learning model that learns sample relevance. The model undergoes pre-training with a contrastive loss and fine-tuning through Bayesian optimization to effectively extract high-dimensional, higher-order, and fraud-related features. We employ a SHAP-based interpreter along with attention scores to elucidate the role of various transaction features in fraud detection. Comparative results demonstrate the model's remarkable recall performance in identifying cryptocurrency transaction fraud. Furthermore, it achieves an excellent F1 value, striking a balance between accuracy and recall. This research not only enriches financial fraud detection but also enhances cryptocurrency transaction security, promotes market development, and contributes to economic stability and social security.

Applying Bayesian Networks for Detecting Bank Fraudulent Transactions in Nigeria

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.

Enhancing Bank Credit Card Transaction Fraud Detection with Machine Learning Techniques

Abstract: Because of the increasing prevalence and sophistication of credit card theft, standard detection measures frequently fail. This study investigates the use of several machine learning algorithms to improve fraud detection in bank credit card transactions. The research aims to develop enhanced fraud detection systems by employing an integrated strategy involving data preprocessing, the application of various classification algorithms, and performance evaluation. The study thoroughly examines algorithms such as Random Forest, Logistic Regression, and Neural Networks, focusing on their predictive capabilities and practical applications. The findings indicate that machine learning provides a robust framework for increasing the accuracy and efficiency of fraud detection systems, consequently assisting financial institutions in protecting customer transactions and strengthening security measures. Moreover, by prioritizing robust model selection and feature engineering, banks can significantly enhance their fraud detection performance. This research could greatly influence how financial institutions handle fraud, potentially reducing losses, improving operational efficiency, and securing transaction environments. The insights gained from this study are also valuable for informing broader financial security strategies and guiding future research in the field.

Research and Analysis of Connected Transactions and Firms' Listing Denials

Abstract: In recent years, IPO declarations of proposed listed companies in China's A-share market have been rejected more and more frequently due to the issue of connected transactions, which has aroused concerns. The purpose of this paper is to explore in depth the reasons for the rejection of the A-share listing of proposed listed companies and to propose countermeasures to solve the problem of abnormal connected transactions. This article first defines the scope of connected transactions and their problems, analyzes some cases of proposed listed companies being rejected due to connected transactions in recent years, and deeply explores the problems of hidden connected relationships, connected transaction fraud, and connected transaction de-connectedness, etc., and demonstrates the specific manifestations and impacts of these problems through example analysis. Given the findings, the article puts forward a series of recommendations and insights, including maintaining the operational independence of enterprises, improving the sustainable profitability of enterprises, and tightly controlling the truthfulness of corporate information disclosure. These suggestions aim to help enterprises avoid the problems of connected transactions, enhance the success rate of IPO, and promote the healthy development of the capital market.

A Supervised Ml Algorithm for Detecting and Predicting Fraud Credit Card Transactions

A Credit card fraud presents an escalating threat in today's digital economy, leading to significant financial losses for consumers, businesses, and financial institutions. Traditional detection methods are increasingly inadequate due to the rise in online transactions and the evolving complexity of fraudulent activities. This research aims to create a reliable supervised machine learning model designed to detect and predict fraudulent credit card transactions in real-time. Our approach leverages advanced algorithms and extensive datasets to enhance transaction security, thereby mitigating financial risks. Current systems, like FICO's Fraud Detection System (FDS), utilize a range of supervised learning techniques, including Decision Trees, Random Forests, and Neural Networks. However, they face limitations, including data quality issues, high false positive rates, and the need for continuous retraining to adapt to evolving fraud patterns. To address these challenges, we propose an innovative anomaly detection method based on the Isolation Forest algorithm. Unlike traditional methods that rely on distance and density measures, Isolation Forests isolate anomalies by randomly selecting features and split values, making it more efficient in identifying fraudulent transactions, especially in imbalanced datasets. The proposed system boasts low linear time complexity and minimal memory requirements, enabling the construction of high-performing models even with large datasets. The research demonstrates that the Isolation Forest approach significantly outperforms traditional models in detecting credit card fraud, offering a promising solution for enhancing security in the financial ecosystem.

An AutoEncoder enhanced light gradient boosting machine method for credit card fraud detection

Online financial transactions bring convenience to people’s lives, but also present vulnerabilities for criminals to embezzle users’ accounts and trick users into credit card fraud. Although machine learning methods have been adopted to detect anomalous transactions, it’s hard for a single machine learning method to achieve satisfying results with the increasing scale and dimensionality of financial datasets. In addition, for anomaly detection of financial data, there is an obvious imbalance between normal records and abnormal. In this situation, the experimental results cannot be objectively evaluated only by the traditional metrics, such as precision, recall, and accuracy. This paper proposes an AutoEncoder enhanced LightGBM method for credit card detection. The method inherits the advantages of each component, using an AutoEncoder for feature reconstruction on the dataset, and integrating the LightGBM algorithm for improving the GBDT (Gradient Boosting Decison Tree) to detect abnormal data more accurately and efficiently. Besides the traditional evaluation metrics, F-measure, area under curve (AUC), Matthew’s correlation coefficient (MCC), and balanced classification rate (BCR) are also adopted as the evaluation metrics. Two financial datasets were used to validate the performance and robustness of the proposed model. Results obtained from the credit card fraud dataset containing 31 features indicate that our model significantly outperforms other models with a recall of 94.85%, representing a 10.70% improvement compared to the best detection performance model with a recall of only 86%. Additionally, our model’s BCR score is also significantly better than other models, with a BCR score of 97%, as opposed to the best detection performance model’s BCR score of 92%, representing a 5% improvement by our model. Various sampling methods and model combinations were considered in this study. It was found that the SMOTE algorithm combined with the proposed model produced the best results, with an AUC value of 96.83% and an F-measure score of 80.27%. The Santander bank transaction record dataset is a high dimensional large dataset containing 200 features. Experimental results on this dataset reveal that compared to other models, our model significantly improved recall and F-measure results, raising the recall to 94.14% and the F-measure score by 11.51%, surpassing the second-best-performing model. Overall, these findings demonstrate the robustness and superiority of our model in detecting fraudulent transactions and highlight the effectiveness of the SMOTE algorithm in combination with the proposed model.

Research on risk detection and management based on machine learning

With the rapid advancement in financial risk management, machine learning techniques are playing an increasingly crucial role in credit assessment and risk detection. They provide financial institutions with more scientific and precise platforms and methodologies for risk management. This paper explores various traditional machine learning methods in risk assessment, introduces the XGBoost ensemble learning method, and integrates traditional machine learning with Long Short-Term Memory (LSTM) neural networks to enhance the generalization capability of risk control and credit assessment models. This approach offers new perspectives and improvement directions for risk assessment standards and practices in the financial industry, affirming the potential application of machine learning technologies in future risk management.The results show that the random forest and decision tree have excellent accuracy and recall rates for distinguishing fraudulent transactions. After the introduction of LSTM neural networks, the accuracy and recall rates of fraudulent transaction recognition models reach around 99%, indicating that these models have good adaptability for fraud risk recognition.

Deep adaptive ensemble learning for imbalanced credit card fraud detection

ABSTRACT Traditional techniques for credit card fraud detection often struggle with highly imbalanced datasets, which compounds the complexity of fraudulent transaction detection. To tackle this issue, the current paper puts forth a novel technique known as deep adaptive ensemble learning (DAEL). Initially, we employ the ensemble-based under-sampling method to bring equilibrium to the data. This is followed by the application of a deep autoencoder for the purpose of feature extraction and dimensionality reduction. Then, a bidirectional long short-term memory model is trained using the features extracted from the autoencoder. Finally, an adaptive weighting technique is applied to unify multiple models. The empirical results demonstrate that the proposed DAEL technique significantly surpasses individual models and conventional ensemble learning techniques in performance.

Adoption of mobile banking to promote financial inclusion among rural farming community: Drivers and satisfaction level perspective

This study investigates the factors driving mobile banking adoption and user satisfaction to enhance financial inclusion among rural farming communities, using survey data from 400 farmers. An unpaired t-test reveals a significant difference in receiving international remittance, fund transfers, mobile recharge, and allowance during and before COVID-19, but no significant difference in cash-in, cash-out, and utility bill payments. During the pandemic, most mobile banking transactions increased in volume and frequency. The result also reveals that the majority of rural people (approximately 88 %) enjoy mobile banking services. The Index value of farmers' satisfaction level towards mobile banking transactions indicates that respondents were satisfied with various mobile banking transactions but were dissatisfied with the higher service fees. Logit estimation shows that age, education, income, farm size, and distance from the bank branch were the primary factors driving the adoption of mobile banking among rural farming communities. However, expansion of agent service, reduction of service charges, and protection from fraudulent transactions are essential for expanding the adoption of mobile banking services in rural areas and accelerating their financial inclusion.

A Hybrid Deep Learning Approach with Generative Adversarial Network for Credit Card Fraud Detection

Credit card fraud detection is a critical challenge in the financial industry, with substantial economic implications. Conventional machine learning (ML) techniques often fail to adapt to evolving fraud patterns and underperform with imbalanced datasets. This study proposes a hybrid deep learning framework that integrates Generative Adversarial Networks (GANs) with Recurrent Neural Networks (RNNs) to enhance fraud detection capabilities. The GAN component generates realistic synthetic fraudulent transactions, addressing data imbalance and enhancing the training set. The discriminator, implemented using various DL architectures, including Simple RNN, Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), is trained to distinguish between real and synthetic transactions and further fine-tuned to classify transactions as fraudulent or legitimate. Experimental results demonstrate significant improvements over traditional methods, with the GAN-GRU model achieving a sensitivity of 0.992 and specificity of 1.000 on the European credit card dataset. This work highlights the potential of GANs combined with deep learning architectures to provide a more effective and adaptable solution for credit card fraud detection.

An enhanced AI-based model for financial fraud detection

The research seeks to identify complex fraudulent activities. Artificial intelligence (AI) techniques, such as machine learning and deep learning, have shown significant potential in enhancing the accuracy and efficiency of fraud detection models. This study introduces a novel AI-based fraud detection model that combines both supervised and unsupervised learning methods. The proposed machine learning system uses these techniques to detect fraudulent transactions. The supervised learning component is trained using a labeled dataset that includes both fraudulent and non-fraudulent transactions. The dataset used in the research contains 284,807 credit card transactions. After preparing the data, four Python-based models were developed. The K-Nearest Neighbors (KNN) model successfully predicted 99.94% of credit card transactions as valid or fraudulent. A random forest (RF) model was also used to assess the legitimacy of transactions, achieving an accuracy score of 99.96% correctly classifying nearly all data points. The Support Vector Machine (SVM) model achieved 99.94% accuracy, misclassifying only 51 cases. The logistic regression (LR) model attained an accuracy of 99.92% with 70 misclassifications and 99.91% with 77 misclassifications. These models demonstrate high accuracy and efficiency.

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.

An Ensemble Method for the Fraud Detection in Transactions

In today's world, bank fraud has become one of the significant threats to the financial stability and security of clients of financial institutions. The development of technologies, in particular in the field of machine learning, opens up wide opportunities for building effective systems for detecting and preventing fraud in the banking sector [1, 2]. Detecting fraudulent transactions is an important task that requires thoughtful and technological solutions. One of these methods is the use of machine learning approaches and methods. This paper proposes the use of an ensemble method that combines several machine learning models at once. This approach will reduce the probability of false positives and increase classification accuracy. In addition, for the optimal operation of the model, preprocessing of the data will be carried out, in particular, their normalization, balancing of classes, as well as the selection of features. During the research, it is important not only to achieve high accuracy, but also to reduce as much as possible the number of fraudulent transactions that will be mistakenly classified as normal [3]. This is related to the business requirements of the banking sector, as each such transaction causes losses to the system's reputation, as well as direct financial losses. Within the framework of the study, it is substantiated that the use of this approach gives better classification results than single models due to the compensation of the shortcomings of each of them. The choice of this approach is also due to high practicality, compatibility with financial systems, as well as ease of integration. This paper analyzes the proposed model, its advantages and disadvantages in comparison with analogues. The ensemble method helps to combine the advantages of simple models and reduce the impact of their shortcomings on the final result. In general, the choice of software should depend on the technical requirements of the project and to obtain better results, different models and approaches should be analyzed.