International American University

Financial fraud poses a significant threat to the digital economy, with credit card fraud being a prevalent challenge. This study evaluates the performance of Logistic Regression (LR) and Extreme Gradient Boosting (XG Boost) models in detecting fraudulent transactions using financial datasets. The study uses practical data from 284,807 transactions, but only 492 are fraudulent; the imbalanced class issue is solved using the Synthetic Minority Oversampling Technique (SMOTE). Our findings show that XG Boost with Random Search selection is better than Logistic Regression in all aspects. XG Boost yielded an accuracy of 99.96%, precision of 95.11%, recall of 79.61%, and F1 score of 86.61%, while for Logistic Regression, the corresponding percentages were 99.92%, 88.1%, 60.5%, and 71.7%. The AUC statistic of 0.98 for XG Boost against 0.97 for LR classified the model as having better discriminant power. The results show that XG Boost is more suitable for real-time fraud detection. However, computational limitations and explainability issues should be considered. For future work, it is suggested that semi-supervised and supervised learning approaches be investigated and work with larger datasets to improve fraud detection in financial systems.

This study examines the application of machine learning algorithms for creditworthiness prediction within the banking sector and addresses the issue of class imbalance through sampling methodologies. The research indicates that using the Stacking Ensemble algorithm with random oversampling can predict creditworthiness with an impressive 93% accuracy. The method consistently achieves excellent precision, recall, and F1-score values, indicating that it can produce accurate predictions while maintaining a balanced evaluation. Random oversampling helps models improve their predictive accuracy and reduce class imbalance. The research findings underscore the feasibility of this technique for financial institutions, facilitating informed lending decisions and improving credit risk assessment methodologies. This research enhances the field by identifying the most effective machine learning methods for accurate creditworthiness evaluation. Using XAI tools like Shapash provides financial organizations with valuable insights into assessing loan risks and enhancing their lending operations.

In the fast-changing environment of financial complexity, efficient risk management is vital for economic stability as well as for growth. In this study, we present a robust AI-powered predictive analytics framework to improve financial risk classification in U.S. markets. The framework utilizes advanced machine learning techniques, a hybrid CatBoost and SVM model that allows it to solve challenges like class imbalance in a high-dimensional dataset while maintaining interpretable models. To probe errors, we use techniques such as Principal Component Analysis (PCA) and Synthetic Minority Oversampling Technique (SMOTE) for data quality and fairness in classification. Comprehensive experiments on a financial risk dataset are conducted to evaluate the framework at which it achieves high accuracy (95.93%) and F1-score (0.95) when compared to traditional machine learning models such as Logistic Regression and Random Forest. Furthermore, a feature importance analysis identifies important predictors of financial risk such as Total Debt-to-Income Ratio, Loan Duration, and Interest Rate, providing actionable on decision-making. Additionally, the proposed approach is not only highly scalable but it is also interpretable and adaptable to the dynamic demands of financial institutions. This study serves as a benchmark for predicting analytics for dealing with risk-associated challenges, leading to informed decision-making to ensure economic stability by integrating AI and machine learning in financial systems.

Digital workplace transformation has considerably more profound implications when redesigning a workplace than deciding which technologies to use or not. Not only will the equipment utilized in work activities change in the future due to digital work, but working activities and processes themselves will frequently alter as well. Employee psychological requirements, such as the desire for relatedness, competence, and autonomy, are significant in this setting because they influence the employees' willingness to embrace their new workplace. More precisely, employees will be more inclined to embrace digital transformation if they believe that working in a digital environment would make it easier for them to achieve higher performance, greater happiness, and personal well-being. Therefore, this study aims to ascertain if employee participation, well-being, and support for digital work contribute to the workplace's digital transformation. By doing this, we bridge a gap in the research and provide evidence that the interpersonal relatedness of employees in a digital work environment significantly impacts their performance and overall well-being. Employee intentions to support the shift to a digital workplace are subsequently increased. To meet the difficulties of digital transformation, we hope this study will assist pre-digital organizations in reevaluating their plans in light of employee participation. The report also invites researchers to use missing value analysis to examine whether and how those parameters are formed differently in light of the COVID-19 pandemic scenario in the northern state of the USA.

The increasing prevalence of malware presents a critical challenge to cybersecurity, emphasizing the need for robust detection methods. This study uses a binary tabular classification dataset to evaluate the impact of feature selection, feature scaling, and machine learning (ML) models on malware detection. The methodology involves experimenting with three feature scaling techniques (no scaling, normalization, and min-max scaling), three feature selection methods (no selection, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA)), and twelve ML models, including traditional algorithms and ensemble methods. A publicly available dataset with 11,598 samples and 139 features is utilized, and model performance is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Results reveal that the Light Gradient Boosting Machine (LGBM) achieves the highest accuracy of 97.16% when PCA and either min-max scaling or normalization are applied. Additionally, ensemble models consistently outperform traditional ML models, demonstrating their effectiveness in enhancing malware detection. These findings offer valuable insights into optimizing preprocessing and model selection strategies for developing reliable and efficient malware detection systems.

The dramatic adoption of Bitcoin and other cryptocurrencies in the USA has revolutionized the financial landscape and provided unprecedented investment and transaction efficiency opportunities. The prime objective of this research project is to develop machine learning algorithms capable of effectively identifying and tracking suspicious activity in Bitcoin wallet transactions. With high-tech analysis, the study aims to create a model with a feature for identifying trends and outliers that can expose illicit activity. The current study specifically focuses on Bitcoin transaction information in America, with a strong emphasis placed on the importance of knowing about the immediate environment in and through which such transactions pass through. The dataset is composed of in-depth Bitcoin wallet transactional information, including important factors such as transaction values, timestamps, network flows, and addresses for wallets. All entries in the dataset expose information about financial transactions between wallets, including received and sent transactions, and such information is significant for analysis and trends that can represent suspicious activity. This study deployed three accredited algorithms, most notably, Logistic Regression, Random Forest, and Support Vector Machines. In retrospect, Random Forest emerged as the best model with the highest F1 Score, showcasing its ability to handle non-linear relationships in the data. Insights revealed significant patterns in wallet activity, such as the correlation between unredeemed transactions and final balances. The application of machine algorithms in tracking cryptocurrencies is a tool for creating transparent and secure U.S. markets. As virtual currencies gain increased acceptance and transactions become increasingly sophisticated, machine algorithms can provide processing capabilities for enhancing supervision and compliance operations. Complicated algorithms can be programmed to search through massive sets of transactional information, identifying trends that could be indicative of fraud and compliance failures. With the use of past data, such algorithms can become trained to detect abnormalities in real-time, and regulators and financial institutions can respond promptly to suspicious activity.

This study aims to examine the influence of teamwork on health outcomes and health management practices. Specifically, the study investigated the Influence of teamwork on patient outcomes, such as effectiveness of care, patient satisfaction, and reduced medical errors. The study also explored the relationship between teamwork and health management practices, including communication, coordination, and collaboration among healthcare professionals at Clemenceau Medical Center in Dubai. The study employed a quantitative approach. The findings of this study contributed to the existing literature on the importance of teamwork in healthcare and provided insights into how effective teamwork can lead to positive health outcomes and improved health management practices (N = 20, r = .849**, p < 0.01). Hence, hospital managers and leaders must consider this finding, as it emphasizes the importance of promoting and fostering effective teamwork among healthcare professionals. The study uncovered the benefits of teamwork in healthcare settings, including improved financial outcomes, reduced staff turnover and absenteeism, and higher quality of care.

This paper assesses green practices and corporate environmentalism in international business management. It examines sustainability activities in different industries, emphasizing the strategies companies adopt to achieve sustainability and economic performance simultaneously. Drawing on corporative environmental reports, sustainability audits and green performance indicators, this study reveals the motivators and barriers to implementing environmentally sustainable practices. The study sheds the light on how firms can adopt eco-efficient technologies, waste management, and resource optimization to enhance their competitive advantage and meet organizational environmental responsibilities. The proposed research advances the knowledge of corporate stewardship and offers practical recommendations for organizations interested in improving environmental responsibility.

This paper presents an analysis of the ways in which Blockchain and Internet of Things (IoT) systems can be combined for the betterment of supply chain management as well as a discussion of the increased security, transparency and/or continuity of business that may result from such an integration. In the current world where the global supply chains are continuing to expand and integrate, it has been noted that there is need to protect real time information flows and enhance the integrity and traceability of information. Using Blockchain’s decentralized and immutable structure together with IoT’s data-collection aspect, this research explores ways in which supply chain processes could benefit from these technologies while minimizing the risks posed by data theft, forgery, and disruptions. The research employs published data analysis and a case study, encompassing cross-industry benchmarks, research, and empirical evaluation of Blockchain-IoT applications in industries. Survey data was obtained from recent implementations in industries including pharmaceutical, F&amp;B and logistics. Quantitative tools and techniques were applied to evaluate the integrity and the reliability of Blockchain-IoT systems operations. Some insights discovered reveals that the integration between Blockchain and IoT fosters better real-time monitoring, minimizes counterfeiting and promotes better traceability of an average of 40% based on observed instances. Literature is advanced in this paper by presenting grounded data on Blockchain and IoT’s ability to maintain enduring and reliable supply chain security. The research implies that when the technologies are well deployed, they act as a key driver in facilitating secure and efficient supply chain. The presented research provides guidance on the adaptation of Blockchain and IoT to enhance the stability of the supply chain; it maps a tactical plan that industry participants can follow to achieve sustainable, secure supply chain operations.

Abstract 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.