Fraud Detection: A Hybrid Approach With Logistic Regression, Decision Tree, and Random Forest

This study aims to determine the effect of debit card transactions, credit card transactions and e-money transactions to currency demand in Indonesia. This research using model estimation Vector Error Correction models (VECM). The data was collected using secondary data obtained from the website of Bank Indonesia and Bureau Central of Statistic in Indonesia. The data were obtained from January 2009 to August 2017 consisting of debit card transaction, credit card transaction, e-money transaction, and cash amount data in circulation data. The findings showed that debit card transactions have a significant negative effect on the demand for currency in Indonesia in the long term, credit card transactions have a significant positive effect on the demand for currency in Indonesia in the long term, while e-money transactions have a significant positive effect on currency demand in Indonesia in the short and long term. Keywords: E-money; Currency demand; Debit card; Credit card JEL Codes: E41 ; E51

The swift progression of technology has increased the complexity of cyber fraud, posing an escalating challenge for the banking sector to reliably and efficiently identify fraudulent credit card transactions. Conventional detection approaches fail to adapt to the advancing strategies of fraudsters, resulting in heightened false positives and inefficiency within fraud detection systems. This study overcomes these restrictions by creating an innovative stacking hybrid machine learning (ML) approach that combines decision trees (DT), random forests (RF), support vector machines (SVM), XGBoost, CatBoost, and logistic regression (LR) within a stacking ensemble framework. This method uses stacking to integrate diverse ML models, enhancing predictive performance, with a meta-model consolidating base model predictions, resulting in superior detection accuracy compared to any single model. The methodology utilizes sophisticated data preprocessing techniques, such as correlation-based feature selection and principal component analysis (PCA), to enhance computing efficiency while preserving essential information. Experimental assessments of a credit card transaction dataset reveal that the stacking ensemble model exhibits higher performance, achieving an F1-score of 88.14%, thereby efficiently balancing precision and recall. This outcome highlights the significance of ensemble methods such as stacking in attaining strong and dependable cyber fraud detection, emphasizing its capacity to markedly enhance the security of financial transactions.

The use of credit cards for electronic commerce purposes has been on the increase in recent time. Credit card being the most acceptable and popular mode of payment, the number of fraud cases associated with it is also on the rise. As a result of the extensive nature of credit card transaction details, it is challenging to identify fraud in a credit card system in recent years, which implies that the identification of credit cards' fraud accurately, quickly, and effectively is a gray area of research. In this article, an automated template-based credit cards' fraud prediction (CCFP) model is developed using dynamic time warping (DTW) technique. This proposed CCFP technique is novel, as it has not been previously used to predict fraud in credit cards' systems. The performance of this proposed DTW-CCFP model is verified using the dataset that contains the credit card transactions of European cardholders. In addition, the performance of this proposed DTW-CCFP model is compared with three different machine learning (ML) prediction models: logistic regression (LR), decision tree (DT) and random forest (RF). The results were documented using two different performance metrics: sensitivity and false discovery rate (FDR). The proposed DTW-CCFP model outperforms the LR, DT and RF techniques. Of interest, the proposed DTW-CCFP model achieves this feat using a small portion (less than 5%) of the dataset to build the template in comparison to the LR, DT and RF techniques, which uses between 20%-30% of the dataset for training to achieve a fairly reasonable result

In today's socio-economic scenario, people rely heavily on credit cards.Moreover, credit cards are a requisite financial tool that enables its holders to make assets.It is true that credit cards, as a new method of payment, have become socially amenable to the masses.But nowadays, improvements in technology lead to growth in illegal activities.During credit card transactions many fraudsters can breach security and make fraudulent transactions to withdraw or transfer funds from one's account or e-wallets.The relevant literature presents many machine learning-based approaches for credit card detection, such as Logistic Regression, Decision Tree, CatBoost, Random Forest, Support Vector Machine, KNN, RNN, and CNN.The main focus has been to apply the recent development of deep learning algorithms for this purpose.Comparative analysis of both machine learning and deep learning algorithms was performed to find efficient outcomes.A machine learning algorithm was first applied to the dataset, which improved the accuracy of detection of the fraud to some extent.The proposed approaches can be implemented effectively for the real-world detection of credit card fraud.

Credit card fraud has caused a huge loss to both banks and consumers in recent years. Thus, an effective Fraud Detection System (FDS) is important to minimize the loss for banks and cardholders. Based on our analysis, the credit card transaction dataset is very skewed, there are much fewer samples of frauds than legitimate transactions. Furthermore, due to the data security and privacy, different banks are usually not allowed to share their transaction datasets. These problems make FDS difficult to learn the patterns of frauds and also difficult to detect them. In this paper, we propose a framework to train a fraud detection model using behavior features with federated learning, we term this detection framework FFD (Federated learning for Fraud Detection). Different from the traditional FDS trained with data centralized in the cloud, FFD enables banks to learn fraud detection model with the training data distributed on their own local database. Then, a shared FDS is constructed by aggregating locally-computed updates of fraud detection model. Banks can collectively reap the benefits of shared model without sharing the dataset and protect the sensitive information of cardholders. Furthermore, an oversampling approach is combined to balance the skewed dataset. We evaluate the performance of our credit card FDS with FFD framework on a large scale dataset of real-world credit card transactions. Experimental results show that the federated learning based FDS achieves an average of test AUC to 95.5%, which is about 10% higher than traditional FDS.

Financial fraud has emerged as one of the most critical challenges in the digital economy, with increasingly sophisticated attack strategies threatening the security of online transactions. This study examined the application of artificial intelligence (AI) and machine learning (ML) models for enhancing fraud detection accuracy and operational efficiency. Six algorithms—Logistic Regression, Decision Tree, Random Forest, XGBoost, Stacking Ensemble, and Graph Neural Network (GNN)—were evaluated using a large, imbalanced dataset of financial transactions. Model performance was assessed based on accuracy, precision, recall, F1-score, and AUC-ROC, with results demonstrating the superiority of ensemble-based and graph-oriented methods. The GNN achieved the highest performance, leveraging its ability to model complex relational structures in transactional data. Feature importance analysis via SHAP values indicated that transaction amount, frequency, and previous fraud history were the most influential predictors. Computational cost analysis revealed a trade-off between model complexity and inference latency, with lightweight models offering faster response times but lower detection capability. The findings suggested that integrating explainable AI and ensemble learning could significantly improve fraud detection while ensuring regulatory compliance and operational transparency. This research contributes to both academic literature and practical financial security systems by offering empirical evidence and actionable insights for the deployment of advanced fraud detection technologies.

In this study, we present a deep learning-based approach for real-time credit card fraud detection in banking systems, with a primary focus on Long Short-Term Memory (LSTM) networks. Using a highly imbalanced credit card transaction dataset, we implemented comprehensive preprocessing, feature engineering, and model evaluation strategies to enhance the detection accuracy. Our experimental results reveal that the LSTM model significantly outperformed traditional machine learning algorithms such as Logistic Regression, Decision Tree, and Random Forest. The LSTM achieved an accuracy of 99.38%, precision of 99.40%, recall of 99.22%, and F1-score of 99.31%, demonstrating its superior capability to detect fraud while minimizing false positives. Through comparative analysis, we establish that deep learning not only improves predictive performance but also adapts better to temporal patterns inherent in financial transactions. This research underscores the transformative potential of AI-driven fraud detection in modern banking infrastructures, ensuring enhanced security, operational efficiency, and customer trust.

Financial transactions using credit card are being observed to be commonly accepted by the users as well as by the financial institutions. At the same time, a good number of fraudsters adopt various techniques to create inconvenience to the users and in the process the financial institutions lose goodwill or trustworthiness. However, a number of methodologies have been proposed by various researchers to counter and revoke the fraudulent activities in credit card transactions. The crux of the problem is that the fraudulent activities need to be curbed instantly if at all can be identified, when the fraudsters initiate them. In this study, a real time fraud detection system based on service oriented architecture (SOA) has been proposed to analyze the fraudulent activities in credit card transactions. The architecture is designed on the basis of Apache Kafka tool, which is used for real time streaming of transactional data to detect the fraudulent transactions. This process considers different services which form the backbone of SOA. Further, five different machine learning classifiers namely support vector machine (SVM), multilayer perceptron (MLP), random forest regressor, autoencoder and isolation forest have been considered to identify the fraudulent activities instantly with the help of SOA based real time architecture.

Credit card fraud has become a major concern in the digital economy, where the rapid growth of online transactions increases the risk of unauthorized financial activities. Effective fraud detection systems are essential to safeguard consumers and financial institutions from significant economic losses. This study investigates the use of machine learning models, specifically Logistic Regression and Random Forest, to classify credit card transactions as legitimate or fraudulent.The dataset is preprocessed through feature scaling and class-imbalance handling techniques such as SMOTE to improve model performance. Experimental evaluation shows that the models achieve high accuracy, with Random Forest performing comparatively better due to its ability to capture complex patterns in transactional data. The results demonstrate the potential of machine learning–based approaches in enhancing the reliability and speed of fraud detection, enabling timely intervention and improved financial security.

Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today's culture are credit card scams. This kind of fraud typically happens when someone uses someone else's credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models' reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft.

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

This study proposes an adaptive, interpretable real-time fraud detection and prevention system designed for high-risk financial environments, capable of processing over 1.6 million imbalanced credit card transactions with low latency. The objective is to build a unified framework that integrates predictive accuracy, explainability, and adaptability. The methodology follows four phases: exploratory data analysis to reveal structural and behavioral fraud patterns, feature engineering with domain-informed attributes and ADASYN oversampling to mitigate the 1:174 imbalance, training of multiple models (XGBoost, LightGBM, Random Forest, Gradient Boosting, and MLP), and an ensemble architecture evaluated with SHAP-based explainability. The system introduces three key contributions: stability-aware SHAP caching that reduces explanation latency to 41.2 ms, reinforcement learning–based threshold tuning that dynamically adapts to evolving fraud patterns, and out-of-distribution detection to enhance resilience against data drift. Results demonstrate strong performance, with XGBoost achieving 99.86% accuracy, 96.36% precision, 80.59% recall, F1-score of 0.878, and ROC-AUC of 0.9988, outperforming other models. The full system attained 93.2% accuracy, 90.2% F1-score, and 96.1% AUC at the system level, successfully blocking 91% of fraudulent transactions while maintaining a false positive rate of 7.8%. Novelty lies in combining explainability and adaptivity in a production-ready architecture, where reinforcement learning enables continuous threshold self-regulation and SHAP stability analysis validates interpretability across models. These findings show that high fraud detection accuracy and transparency are not mutually exclusive, offering a scalable blueprint for financial institutions and other critical domains requiring real-time, explainable, and adaptive decision-making.

This study explores the transformative role of artificial intelligence (AI), particularly machine learning (ML), in enhancing the detection and prevention of money laundering and fraud within the U.S. financial industry. The study aims to analyze how AI-driven techniques can significantly improve the accuracy, efficiency, and scalability of fraud detection systems. The study focuses on examining various machine learning algorithms, including supervised techniques like logistic regression and decision trees, as well as unsupervised methods such as clustering and anomaly detection. These techniques are utilized to analyze historical data, detect patterns, and identify suspicious transactions or fraudulent behaviors in real-time. The research method includes a comprehensive review of existing case studies and literature on AI applications in fraud detection, highlighting successful implementations of ML models in financial institutions. The findings reveal that machine learning models, such as random forests and support vector machines, have proven effective in detecting and preventing fraudulent activities with high precision and recall rates. Furthermore, the integration of AI with real-time data analysis capabilities enables continuous monitoring and immediate detection of irregularities. The study concludes that financial institutions in the U.S. must leverage AI advancements to enhance risk management systems, improve fraud detection, and mitigate the risks of money laundering. By adopting machine learning algorithms, financial organizations can stay ahead of emerging threats, ensuring the security of their operations and customer assets. Keywords: Artificial Intelligence, Machine Learning, Money Laundering, Fraud Detection, U.S. Financial Industry.

Artificial Intelligence in Financial Fraud Detection: A Comprehensive Approach to Enhancing Financial Security The rise of artificial intelligence (AI) offers an opportunity to significantly strengthen financial security by combating financial fraud, which has become increasingly complex and widespread. Traditional detection methods are often insufficient in identifying and preventing fraudulent activities, prompting a shift towards AI-based solutions. This study explores the application of AI, particularly machine learning algorithms, in improving the accuracy and efficiency of fraud detection. By analyzing large financial datasets, AI can detect anomalies that may indicate fraudulent behavior more effectively than traditional approaches. This research adopts a two-phase methodology. The first phase involves a thorough review of existing financial fraud detection methods, comparing traditional techniques with AI-based models to identify gaps. Various machine learning approaches, including supervised, unsupervised, and deep learning algorithms, are reviewed for their effectiveness in detecting fraud. The second phase involves developing and testing an AI model to identify fraudulent patterns within transactional data. The model uses machine learning algorithms to process vast datasets and detect deviations from typical financial behaviors, flagging potentially fraudulent activities. The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud. This reduction in false positives is vital for financial institutions, as it reduces unnecessary investigations and saves valuable resources. Additionally, enhanced fraud detection protects both institutions and consumers from financial losses.The findings of this study aim to provide financial institutions with practical insights into the implementation of AI-driven fraud detection systems. Furthermore, the research highlights the need for continuous refinement of AI models to adapt to the evolving nature of financial fraud. By leveraging AI technologies, financial institutions can revolutionize their approach to fraud detection, making financial systems more secure and responsive to emerging threats.

Systems can cluster data and provide incredibly accurate outcomes under machine learning. Using theXGBoost algorithm, this study investigates machine learning for fraud detection in an effort to improve corporateprocedures and lower fraudulent activity in big businesses. We suggest an XGBoost-based model for online creditcard fraud detection that has been validated through case studies from two banks. This model is intended for realtimefraud detection. The results show how well XGBoost detects fraud while striking a good balance between recalland precision, greatly increasing the effectiveness of financial systems. Analysis using Python demonstrates howmachine learning models can handle and stop fraud on dynamic datasets in real time. This study concludes bydemonstrating that machine learning algorithms such as XGBoost may be used to dynamically manage fraudulentactions, efficiently handle online credit card fraud detection in banks, and continually improve the fraud detectionand prevention system..