Fraud Detection Model Research Articles

SUBSCRIPTION FRAUD DETECTION USING AUTOENCODER IN CASE OF ETHIOTELECOM

Nowadays, telecom services are becoming an essential communication and business facilitators. However, the development of telecom services motivates fraudsters for illegal use. Hence, telecom fraud becomes a serious challenge in telecommunication sector by losing telecom revenue. It also results in poor quality of services for their customers. Telecom data, SMS and voice call are not free from security issues. Despite SMS, and USSD services becomes good options over installable mobile based or web based applications due to lesser cost and real time support, the methods provide fixed amount requirements which is not dynamic way to address the need of these services subscribers. The other challenge of these services is that; the services are not safe from frauds as such, especially in GSM switches. Subscription fraud is one type of fraud in today’s telecom business which is a common telecom fraud. The need of fraudsters is to make money illegally or getting telecom services with the intention of not to pay for the service they used. The prime objective of this study is to build a predictive model using deep auto-encoder to detect subscription fraud in case of Ethiotelecom. The performance of fraud detection model is 98.95% validation accuracy on ethiotelecom call detail record dataset using deep neural network autoencoder and CNN-LSTM autoencder algorithms at threshold of 0.0103 and 0173 respectively.

Research on Fraud Detection Method of Financial Data of Listed Companies Based on HMCRAN

This study investigates the application of deep learning technology to improve accuracy and efficiency in detecting financial fraud in listed companies. Using data from the China Economic and Financial Research Database on Main Board A-share listed companies with disclosed fraudulent behaviors, a hybrid multi-channel residual attention network (HMCRAN) was constructed, combining multi-channel residual networks and transformers. The study involved feature extraction, data preprocessing, model training, and performance evaluation, validating the superiority of the HMCRAN model for financial fraud detection. Experimental results demonstrate that the model excels in handling high-dimensional financial data and capturing long-term dependencies, significantly improving detection accuracy and robustness. The HMCRAN model outperforms traditional machine learning methods across various evaluation metrics, demonstrating its high practical application value.

FRAUD DETECTION IN FINANCIAL TRANSACTIONS THROUGH DATA SCIENCE FOR REAL-TIME MONITORING AND PREVENTION

This study presents a comprehensive review of the use of advanced technologies in credit card fraud detection, with a focus on machine learning, blockchain, and federated learning, to understand their transformative impact on the field. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a total of 97 articles were systematically reviewed and analyzed. The findings reveal that machine learning models, such as decision trees, support vector machines, and neural networks, have significantly improved fraud detection accuracy, reducing false positives and enhancing the ability to detect complex fraud patterns in real-time. Blockchain technology also plays a critical role by providing a decentralized, secure, and transparent framework for fraud detection, ensuring the integrity of transaction records and making fraudulent activities harder to conceal. Federated learning offers a privacy-preserving solution, enabling institutions to collaborate on fraud detection without sharing sensitive data, which is increasingly important in light of stringent regulatory requirements. Additionally, the study highlights the growing use of predictive analytics in forecasting potential fraud, allowing financial institutions to proactively prevent fraud before it occurs. Moreover, feedback loops integrated into fraud detection models allow for continuous improvement, ensuring that detection systems can adapt to new and evolving fraud tactics. Overall, the review underscores the importance of adopting these advanced technologies to build more secure, efficient, and adaptive fraud detection systems capable of safeguarding financial transactions in the modern digital economy.

Graph neural network for fraud detection via context encoding and adaptive aggregation

Financial fraud is a vital issue which causes considerable economic losses for financial institutions. Existing studies apply various methods to detect fraudsters from financial data. In particular, methods based on Graph Neural Networks(GNNs) are among the most effective. However, applying graph neural networks to fraud detection still faces challenges of capturing two types of fraudulent behaviors: the context-related behavior and camouflage-related behavior. To address the challenges, we present Context-encoding-and-Adaptive-aggregation-based Fraud Detector(CAFD), a novel GNN-based fraud detection model. To capture the context-related fraudulent behavior, we devise a context encoding module which encodes the time intervals of out-edges and out-degree for each node in the graph. In addition, we design an adaptive aggregator which identifies the camouflage-related behavior by performing random dropping on messages during the message passing process of GNNs. We evaluate the performance of CAFD with extensive experiments on two real-world datasets. Results of the experiments demonstrate that our model outperforms state-of-the-art baselines.

Research and Empirical Evidence of Machine Learning based Financial Statement Analysis Methods

This study presents a novel approach called FinAnalytix which merging machine learning’s prowess in pattern recognition with financial statement analysis. This integrated algorithm combines deep neural networks and recurrent neural networks for predictive accuracy in stock return analysis, alongside logistic regression and random forest models for robust fraud detection in financial statements. The empirical evidence demonstrates FinAnalytix effectiveness in identifying abnormal financial patterns and predicting market reactions to earnings announcements. The study utilizes extensive data from listed companies, ensuring a comprehensive and practical application. FinAnalytix represents a significant advancement in the field, providing a dual approach to financial analysis for enhancing investment strategies through accurate stock return forecasts and bolstering financial integrity by detecting fraudulent activities. The simulation of the study based on the financial data of 100 sample listed companies. This research not only bridges the gap between traditional financial analysis and modern machine learning techniques but also offers a powerful tool for investors and regulatory bodies in navigating the complex financial landscape.

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.

Research on the use of communication big data and AI artificial intelligence technology to construct telecom fraud prevention behavior portrait

A solid foundation for behavior portrait construction in the fight against telecom fraud is the goal of this research. The study explores the integration of communication AI and Big Data technologies, focusing on the perspective of artificial intelligence. By using insights obtained from a telecom fraud detection model that relies on users’ behavior variations expressed through time-varying signatures, the goal of this study is to enhance fraud prevention strategies in the telecom industry. Through the examination of call detail records and customer profile information, the TeleGuard AI Fraud Prevention Framework (TGAI-FPF) aims to recognize suspicious trends and variations that are potentially suggestive of fraudulent actions. The purpose of the model is to generate behavior portraits that are capable of capturing the distinctive aspects of fraudulent conduct in telecom networks. This will be accomplished through the utilization of advanced analytics and machine learning algorithms. The study highlights the significance of leveraging big data analytics and artificial intelligence technologies to efficiently detect and thwart fraudulent activity in the telecom industry. The results of this study should fortify the defenses of telecom networks against growing fraudulent schemes and help in the development of preventative measures to combat fraud. This is the anticipated manner in which the results will add.

CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

The rise of e-commerce and digital payment systems has been accompanied by an increase in financial fraud, especially involving credit cards. Ensuring the detection of fraudulent activities is crucial to protecting users' financial assets and preserving trust in online transactions. This study introduces a novel method for detecting credit card fraud by integrating machine learning (ML) techniques with a genetic algorithm (GA) for feature selection. Feature selection plays a vital role in improving fraud detection models by pinpointing the most relevant features linked to fraudulent transactions. The effectiveness of the proposed method is assessed using a dataset from European cardholders, a widely used benchmark in this research area. In terms of performance, the Isolation Forest Algorithm exhibits slightly higher accuracy compared to the Local Outlier Factor (LOF) Algorithm. Consequently, based on accuracy alone, the Isolation Forest Algorithm is deemed more effective for this specific dataset. Keywords: Credit card fraud detection, Machine learning (ML), E-commerce.

An intelligent approach to detect and predict online fraud transaction using XGBoost algorithm

The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft with machine learning (ML), the features of credit card frauds play an important and they must be carefully selected. A fraud detection algorithm must be created in order to correctly locate and stop fraudulent activity as technology advances along with the amount of fraud cases. ML methods are essential for identifying fraudulent transactions. The implementation of fraud detection models is particularly difficult because of the sensitive nature of the data, the unbalanced class distributions, and the lack of data. In this work, an intelligent approach to detect and predict online fraud transaction using extreme gradient boosting (XGBoost) algorithm is described. The XGBoost model predicts whether a transaction is fraud or not. This model will achieve better performance interarm of recall, precision, accuracy and F1-score for credit card fraud detection.

Generative AI in Fintech: Advancing Risk Assessment and Fraud Detection in Digital Payment Technologies

This article explores the transformative potential of artificial intelligence (AI), particularly generative AI, in enhancing risk assessment and fraud detection within digital payment technologies. As financial fraud evolves in sophistication, traditional detection methods often fall short. We propose a novel approach leveraging generative AI to create synthetic data that mimics real-world scenarios, thereby training more comprehensive and adaptive fraud detection models. This article addresses limitations in historical datasets, improves the accuracy of predictive models, and reduces false positives. Furthermore, we demonstrate how this approach enables proactive detection of emerging fraud trends, allowing fintech firms to stay ahead in the ongoing arms race against fraudsters. Our findings suggest that integrating generative AI into fraud detection systems can significantly enhance the security and reliability of digital payment platforms, potentially revolutionizing risk management in the fintech industry. This article contributes to the growing body of knowledge on AI applications in financial security and provides a foundation for future developments in this critical area.

An Investigation of Machine Learning Applications in the Financial Fraud Detection

Financial fraud presents itself in various forms, and it often involves intricate financial transaction networks, making it challenging to detect the perpetrators and identify the characteristics of the fraud. In recent years, machine learning has gained widespread application within the financial sector. Therefore, various financial fraud detection models have been developed based on diverse machine learning methodologies. In the method part, this paper provides an overview of the machine learning process and then discusses the application of machine learning models in various financial fraud scenarios. Financial fraud in the insurance field has been further refined into automobile and medical insurance fraud. In automobile insurance fraud detection, some studies applied implicit Naive Bayes Model to analyze observable features and estimate hidden variables, and some studies used resampler to solve data imbalance and adopted 7 kinds of machine learning models for analysis. In health insurance fraud detection, many studies train medical data with multiple models, including AdaBoost, Logistic Regression and Support Vector Machine. In credit card fraud detection, studies use five algorithms, including random forest and decision tree et al., and some construct a model combining Decision Tree (DT) and Logistic Regression (LR). In bank fraud detection, some studies introduce Value-at-Risk to combine it with machine learning algorithms, and some studies propose a decentralized model training method based on federated learning. In the discussion section, this paper addresses the current limitations of the research, such as poor interpretability, uneven distribution of data sets, and issues related to customer privacy, and proposes corresponding solutions.

Optimizing fraud detection in financial transactions with machine learning and imbalance mitigation

AbstractThe rapid advancement of the Internet and digital payments has transformed the landscape of financial transactions, leading to both technological progress and an alarming rise in cybercrime. This study addresses the critical issue of financial fraud detection in the era of digital payments, focusing on enhancing operational risk frameworks to mitigate the increasing threats. The objective is to improve the predictive performance of fraud detection systems using machine learning techniques. The methodology involves a comprehensive data preprocessing and model creation process, including one‐hot encoding, feature selection, sampling, standardization, and tokenization. Six machine learning models are employed for fraud detection, and their hyperparameters are optimized. Evaluation metrics such as accuracy, precision, recall, and F1‐score are used to assess model performance. Results reveal that XGBoost and Random Forest outperform other models, achieving a balance between false positives and false negatives. The study meets the requirements for fraud detection systems, ensuring accuracy, scalability, adaptability, and explainability. This paper provides valuable insights into the efficacy of machine learning models for financial fraud detection and emphasizes the importance of striking a balance between false positives and false negatives.

An Intelligent Financial Fraud Detection Model Using Knowledge Graph-Integrated Deep Neural Network

Financial fraud detection has been an urgent technical demand in cyberspace. It highly relies on clear extraction and deep representation toward complex relationships inside financial social networks. As consequence, this study combines both knowledge graph and deep learning to deal with such issue. Thus, an intelligent financial fraud detection model based on knowledge graph guidance and deep neural network is proposed in this paper. First, a new knowledge graph based on financially related systems is constructed, which includes multiple entities and relationships. Then, an adversarial learning-based neural network structure is formulated to extract financial attributes. Finally, the detection results can be output according to the extracted factors. Empirically, the proposal is implemented on a real-world dataset for performance evaluation. The experimental results show that it has higher accuracy and effectiveness compared to traditional fraud detection methods. The proposed detection model can not only identify known fraudulent behaviors, but also predict potential fraud patterns based on consumer habits, thereby improving the security and reliability of financial transactions. It can also update the knowledge graph in real-time, enabling it to cope with emerging fraud methods and variants.

Combined Feature Set with Logistic Regression Model to Detect Credit Card Frauds in Real Time Applications

Online payment methods are gaining popularity and are widely used, both in-store and online. Because to the Internet and smart mobile devices, conducting such transactions is quick, simple, and stress-free. However, online payment fraud is common due to the open nature of the internet, which allows criminals to use techniques such as eavesdropping, phishing, infiltration, denial-of-service, database theft, and man-in-the-middle assault. Online payment fraud is on the rise, and it is a big contributor to global economic losses. Financial services, healthcare, insurance, and other industries have long been plagued by fraud. Online fraud has developed in tandem with the use of digital payment systems such as credit/debit cards, PhonePe, Gpay, and Paytm. Furthermore, fraudsters and criminals are adept at evasion strategies, allowing them to steal more. Developing a secure system for client authentication and fraud protection is tough since there is always a workaround. This means that fraud detection systems play an important role in preventing financial crimes. Over time, victims of internet transaction fraud have incurred tremendous financial losses. The growth of cutting-edge technologies and global connection has led to a surge in online fraud. To reduce these expenses, it is critical to develop effective fraud detection systems. Machine learning and statistical tools make detecting dishonest money deals much easier. The scarcity of data, the sensitive nature of the data, and the uneven class distributions make it challenging to implement efficient fraud detection models. Given the delicate nature of the information, it is difficult to draw conclusions and construct more accurate models. This study offers a Linked Feature Set with Combined Feature Set with Logistic Regression (CFS-LoR) Model for accurate detection of online payment frauds. In comparison to extant models, the proposed model exhibits a highly accurate detection capability.

The best machine learning model for fraud detection on e-platforms: a systematic literature review

The internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on eplatforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms, and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems.

The best machine learning model for fraud detection on e-platforms: a systematic literature review

The internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on e-platforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms, and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems.

Enhancing Security in Digital Payments: A Comparative Evaluation of Machine Learning Models for Credit Card Fraud Detection

Nowadays, credit card transactions are exponentially increasing as a form of online payment. That creates technical pressure on the financial institution to incur losses due to credit card fraud. It is no wonder that credit card fraud is making people feel insecure and unsafe about using the services provided by banks. Data mining reasons on offline and online transactions can cause fraud detection. Number one is the changes in the behaviour of the frauds that are changing, and number two is the fraudulent dataset, which is asymmetric. In addition, the variables and techniques used by the researcher can impact fraud detection activities for credit cards. Therefore, the paper intends to investigate the suitability of the k-nearest-neighbour, decision tree, support vector machine, logistic regression, and Catboost. The data set has a total of 284807 transactions coming from the European credit card holder. Evaluation of the performance can be measured through the specificity, accuracy, sensitivity, precision, and lastly, the recall rate. After completing the comprehensive cross-validation, it was discovered that the catboost’s accuracy was extraordinary, which amounted to 93.39%, leaving the other classification matrix far behind.

A Hybrid Neural Network-Based Fast Financial Fraud Detection Model

With the increasing number of financial transactions, financial fraud has become increasingly serious for financial institutions and the public. The core idea of this model is to integrate multiple neural network structures and utilize their respective advantages to improve the performance of fraud detection. Firstly, we employed the convolutional neural network with interpretable blocks (CNNIB) convolutional neural network (CNN) to extract key features from the data to capture patterns and patterns in fraud cases. Secondly, we introduced the autoencoder generative adversarial network (AE-GAN) adversarial network to perform feature analysis on sequence data to capture temporal features in transaction sequences. Finally, we used differential detection for classification to determine whether transactions were fraudulent. An independent detection module was established to accelerate the recognition of financial fraud, and parameter indicators were optimized. Finally, a hybrid neural network model was established. The experimental results indicate that our model has achieved significant results in quickly detecting financial fraud; compared with traditional single neural network models, hybrid neural network models have significant improvements in accuracy and efficiency. In addition, we conducted in-depth analysis of the model and revealed its performance stability under different training set sizes and data distributions. Our research findings provide an effective tool for financial institutions to quickly identify financial fraud.

Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data

Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by comparing four commonly used machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree, and Logistic Regression. Additionally, we utilized the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance. The XGBoost Classifier proved to be the most successful model for fraud detection, with an accuracy of 99.88%. We utilized SHAP and LIME analyses to provide greater clarity into the decision-making process of the XGBoost model and improve overall comprehension. This research shows that the XGBoost Classifier is highly effective in detecting banking fraud on imbalanced datasets, with an impressive accuracy score. The interpretability of the XGBoost Classifier model was further enhanced by applying SHAP and LIME analysis, which shed light on the significant features that contribute to fraud detection. The insights and findings presented here are valuable contributions to the ongoing efforts aimed at developing effective fraud detection systems for the banking industry.

An Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud Detection

An Adaptive Heterogeneous Ensemble Learning Model for Credit Card Fraud Detection