Machine Learning for Fraud Detection in Digital Payment Systems: Challenges and Solutions

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

The 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.

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

Purpose: The purpose of this paper is to analyze the role of machine learning (ML) in fraudulent online payment transaction detection across industries, with a specific focus on integrating Environmental, Social, and Governance (ESG) principles to bring sustainability and ethics in fraud detection. Methodology: This study reviews the effectiveness of machine learning (ML) techniques in real-time fraud detection, presenting the efficiency of different ML techniques across industries and how those techniques can be modified to apply the principles of ESG. It checks on ethical practices concerning data, compliance with data privacy, and sustainable management, further investigating how effectively ML-driven frameworks can support fraud detection without compromising the standards of ESG. Findings: The study finds that integrating ESG principles within machine learning frameworks for fraud detection enhances both the effectiveness and ethical alignment of these systems. ML models not only support real-time fraud detection but also reinforce sustainable data management and governance practices, providing businesses with an advanced approach to mitigating cyber risks while upholding ESG commitments. Unique Contribution to Theory, Practice, and Policy: This paper contributes by advancing ESG-integrated frameworks for fraud detection, offering a sustainable and ethical model for using ML in financial systems. In practice, it provides actionable insights for industries seeking to align fraud prevention with ESG goals. The paper also suggests policy considerations, advocating for stronger ethical standards and data governance in ML applications for fraud detection.

High systolic blood pressure is one of the leading global risk factors for mortality, contributing significantly to cardiovascular diseases. Despite advances in treatment, a large proportion of patients with hypertension do not achieve optimal blood pressure control. Arterial stiffness (AS), measured by pulse wave velocity (PWV), is an independent predictor of cardiovascular events and overall mortality. Various antihypertensive drugs exhibit differential effects on PWV, but the extent to which these effects vary depending on individual patient characteristics is not well understood. Given the complexity of selecting the most appropriate antihypertensive medication for reducing PWV, machine learning (ML) techniques offer an opportunity to improve personalized treatment recommendations. This study aims to develop an ML model that provides personalized recommendations for antihypertensive medications aimed at reducing PWV. The model considers individual patient characteristics, such as demographic factors, clinical data, and cardiovascular measurements, to identify the most suitable antihypertensive agent for improving AS. This study, known as the RIGIPREV study, used data from the EVA, LOD-DIABETES, and EVIDENT studies involving individuals with hypertension with baseline and follow-up measurements. Antihypertensive drugs were grouped into classes such as angiotensin-converting enzyme inhibitors (ACEIs), angiotensin receptor blockers (ARBs), β-blockers, diuretics, and combinations of diuretics with ACEIs or ARBs. The primary outcomes were carotid-femoral and brachial-ankle PWV, while the secondary outcomes included various cardiovascular, anthropometric, and biochemical parameters. A multioutput regressor using 6 random forest models was used to predict the impact of each antihypertensive class on PWV reduction. Model performance was evaluated using the coefficient of determination (R2) and mean squared error. The random forest models exhibited strong predictive capabilities, with internal validation yielding R2 values between 0.61 and 0.74, while external validation showed a range of 0.26 to 0.46. The mean squared values ranged from 0.08 to 0.22 for internal validation and from 0.29 to 0.45 for external validation. Variable importance analysis revealed that glycated hemoglobin and weight were the most critical predictors for ACEIs, while carotid-femoral PWV and total cholesterol were key variables for ARBs. The decision tree model achieved an accuracy of 84.02% in identifying the most suitable antihypertensive drug based on individual patient characteristics. Furthermore, the system's recommendations for ARBs matched 55.3% of patients' original prescriptions. This study demonstrates the utility of ML techniques in providing personalized treatment recommendations for antihypertensive therapy. By accounting for individual patient characteristics, the model improves the selection of drugs that control blood pressure and reduce AS. These findings could significantly aid clinicians in optimizing hypertension management and reducing cardiovascular risk. However, further studies with larger and more diverse populations are necessary to validate these results and extend the model's applicability.

Systematic literature review of machine learning based software development effort estimation models

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

Fraud detection has become a critical concern for financial institutions seeking to safeguard their assets and maintain client trust in an increasingly digitized financial landscape. This study examines the application of machine learning (ML) techniques to enhance fraud detection systems within financial institutions. By leveraging computational algorithms and data analytics, organizations can identify patterns and anomalies in transaction data that conventional rule-based approaches often fail to detect. The efficacy of multiple ML paradigms, including supervised, unsupervised, and reinforcement learning, in identifying fraudulent activities is evaluated through a systematic review of existing literature and comparative analysis of model performance across benchmark datasets. The study highlights the critical role of feature engineering and data preprocessing in building robust ML models, as the quality of input data significantly influences predictive accuracy. The integration of real-time data processing, which enables organizations to respond to emerging threats promptly, is also examined. Key challenges are discussed, including high false positive rates, class imbalance inherent in fraud datasets, and the necessity for continuous model adaptation to track evolving fraud patterns. The findings indicate that ML-based approaches not only improve fraud detection rates but also enhance operational efficiency and customer satisfaction. This paper serves as a foundational reference for practitioners and researchers aiming to advance the application of machine learning for fraud detection in the financial sector.

The increase of Credit Card (CC) fraud in recent years requires the development of fraud detection systems that are both efficient and robust. This paper explored the utilization of machine learning models, with a particular emphasis on ensemble methods, to advance the detection of CC fraud. We present an ensemble model that incorporates different classifiers to address the dataset imbalance issue that is present in most CC datasets. We employed synthetic over-sampling and under-sampling techniques in certain machine learning algorithms to tackle the same issue. To address the issue of under sampling, we implemented ensemble random sampling, which was subsequently followed by Two-Stage Thresholding (TST) to eliminate outliers. The well-known Synthetic Minority Over-sampling Technique (SMOTE) was used for over-sampling. Data was utilized to train the model and subsequently generate predictions by utilizing testing data following the pre-processing of the dataset. The proposed ensemble model outperformed conventional methods in addressing the challenges associated with CC fraud detection, as demonstrated by a comparative analysis. Our findings suggest that ensemble approaches are effective at fighting fraud, paving the way for more adaptive and resilient fraud detection systems.

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.

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

This research investigates how machine learning (ML) algorithms can be applied to financial fraud detection as a way to improve predictive analysis and reduce business risk of fraudulent activities. Financial fraud is a major threat according to Association of Certified Fraud Examiners (2023) which may cost corporations and consumers more than $5 trillion. Many traditional fraud detection systems have relied on rule-based methods which can be restricted with predefined criteria and not able to adapt to evolving fraud patterns. For this research, we use advanced ML models such as Random Forest, Gradient Boosting, Neural Networks which are trained to analyze the large datasets for anomaly detection and to measure the predictive accuracy with real time analysis. In achieving such rates and with the objectives of accuracy, precision and recall met, we employ a dataset of over 1 million financial transactions from verified sources and apply algorithms which we then evaluate against the metrics. Most notably, we show that ML driven models integrate with conventional methods to reduce false positives by 30%, leading to operational efficiency and cost savings. In addition to providing academic knowledge by validating the robustness of ML techniques, this study provides actionable insights for financial institutions which are looking to implement scalable, data driven fraud prevention system. Through addressing technical and operation challenges, our research demonstrates the practicality of applying ML in lowering financial risk.

Transfer learning enables prediction of steel corrosion in concrete under natural environments

The majority of payment solutions in the field of electronic money in Ukraine are implemented on top of card payment systems. At the same time, a number of factors, including the digitization of the economy, orientation towards the European vector of development, the need to counteract the outflow of funds from the country, determine the urgency of developing a competitive national electronic money payment system. The more complex functionality of the electronic money institutions (EMI) determines the more complex structure of the organization of cash flows in the system. At the same time, European legislation clearly regulates the need for the payment system to comply with a number of conditions. In particular, at any moment the sum of balances on all internal wallets of the system must be identical to the balance on the system bank account. The issue of developing a general model of the movement of all types of funds in EMI in real time is still unresolved, which increases the risk of errors at the EMI design stage and ultimately leads to an increase in costs for the full cycle of development of the payment system. The purpose of the article is to develop a model of the movement and distribution of cash flows in the electronic money payment system in real time, which ensures compliance with the conditions of the regulator and the requirements of economic security. The article examines the general structure and main types of payment operations in the electronic money system, derives and substantiates the ratio that determines the liquidity of such a system. The set of basic and auxiliary operations in real modern electronic money systems is considered. A static model for ensuring the integrity of cash flows in electronic payment systems has been built. A model of cash flows in the electronic money system is proposed, which allows considering all basic and technical transactions of the system within one scheme in real time. The proposed model provides accounting of transactions of various types on various electronic wallets, which facilitates not only control over transactions, but also subsequent modernization and adaptation of the system to changing market needs.

Sensitivity evaluation of machine learning-based calibrated transportation mode choice models: A case study of Alexandria City, Egypt