Digital Commons Network^™

Effective Strategies For Deterring Unethical Behavior In U.S. Financial Institutions, Samuel Akinlade Sanni

Walden Dissertations and Doctoral Studies

Unethical employee behavior in U.S. financial institutions continues to erode organizational trust and firms’ brand reputation. Leaders of financial institutions are concerned that unethical employee behavior can negatively impact firms’ profitability, competitiveness, and sustainability. Grounded in Kaptein’s corporate ethics virtue (CEV) model, the purpose of this qualitative pragmatic inquiry study was to explore how organizational ethical cultures influence employees’ behaviors. Data were collected through semistructured interviews with a sample of eight leaders in New York financial institutions, with a minimum of 5 years of successful experience in building a positive ethical environment, and by analyzing publicly available documents to support …

Stranger Disputes: When Artificial Intelligence Turns Arbitration Upside Down, Imre Stephen Szalai

Pepperdine Dispute Resolution Law Journal

Arbitration agreements are everywhere in the United States. These agreements already block access to courts in a troubling manner, and pursuant to these agreements, parties must resolve their disputes before a private, human arbitrator with broad, virtually unreviewable powers. However, with the growth of AI, companies could easily redraft their contracts to require arbitration before non-human bots or AI arbitrators instead of a human arbitrator. Based on the history, values, policy, and text of the Federal Arbitration Act (FAA), this Article concludes that the FAA would govern and support the use of an AI arbitrator. As a result, a pre-dispute …

Detecting Android Malware Based On Static Analysis Using Classification And Modified Clustering Techniques, Abdullah Allawi Al-Sraratee, Ahmed Habeeb Al-Azawei

Journal of Intelligent Informatics, Networking, and Cybersecurity

Because Android malware harms internet security, prior research proposes several different approaches to detect it accurately. However, such proposed models depend on numerous number of features to attain high accuracy. This could lead to high computation cost and potential overfitting. Furthermore, manual data labeling is labor-intensive, requiring significant human effort and skills. This research aims to: 1) extend previous literature on Android malware detection, 2) improve the accuracy of Android malware detection based on a low number of features, and 3) modify a clustering technique to group data into two different clusters to address the issue of unlabeled data. To …

Real-Time Fraud Detection Using Big Data, Abdulla Matar Alketbi

Theses

In today’s digital world, fraud detection has become an important part of financial security. This study explores and compares the performance of different machine learning models in identifying fraudulent transactions using the IEEE-CIS Fraud Detection dataset. Techniques such as Random Forest, Gradient Boosting, Deep Neural Networks, and Logistic Regression were evaluated. The dataset was pre-processed using SMOTE to balance the classes and improve model sensitivity to fraud cases. Each performance of the model was assessed using accuracy, precision, recall, and F1-score. The Random Forest model achieved the highest overall performance with an F1-score of 99.23

Usa V. Martins Inalegwu

2025 Decisions

USDC for the District of New Jersey

The Proactive Approach Of Artificial Intelligence In Monitoring And Analyzing Illegal Money Laundering Operations In The Virtual World, Ammar Yasser El-Bably

Journal of Police and Legal Sciences

The research dealt with the proactive method of artificial intelligence in monitoring and analyzing non-suspicious operations of money laundering in the virtual world, especially the Internet and dark web, where it dealt with the risks created for those networks and their exploitation for the benefit of terrorist and criminal organizations, money laundering, the purchase of weapons and drugs, human trafficking and financial fraud, where Internet users around the world reached 5 billion users, and the volume of money laundering crimes became 2.8 Trillion dollars, according to United Nations and International Monetary Fund statistics 2023 AI applications where algorithms, machine learning, …

Halo: Heterophily-Aware Label-Free Ordering For Unsupervised Graph Fraud Detection

Defensive Publications Series

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a …

Differential Privacy Enabled Deep Skin Image Classification Model Development, Prasun Kumar Mandal

Master’s Dissertations

Abstract In the era of big data, the explosive growth in data volume has significantly accelerated the development of deep learning. Deep learning is the most promising area of AI, yielding significant advancements in medical image classification. However, healthcare data contains important sensitive information and so privacy and security are crucial to preventing unauthorized access. Note that there are several data protection rules from multiple regulations to penalize any kind of data security violation, for example, the data protection principles (Article 5.1-2) and the data protection by design and by default (Article 25) of the General Data Protection Regulation from …

Reevaluating Fourth Amendment Protections In The Digital Age, Tiffany Benjamin

UC Law Constitutional Quarterly

No abstract provided.

Federated Learning Using Fully Homomorphic Encryption, Sk Golam Kuddus

Master’s Dissertations

Traditional machine learning approaches require centralizing data for training, which raises significant privacy concerns when dealing with sensitive information. Federated learning (FL) addresses this by keeping data local and enabling multiple users to collaboratively train a shared machine learning model. In spite of this, FL remains vulnerable to inference attacks, as sensitive information can still be extracted from the model’s learned parameters. While traditional privacy-enhancing techniques such as di!erential privacy introduce noise to model updates to obscure individual data points, they often present a fundamental trade-o! between privacy and utility. Furthermore, these approaches still carry risks of data leakage if …

The Settlement Privacy-Transparency Matrix: Moving Beyond The Dichotomous Mindset In The Settlement Debate, Alyson Carrel, Peter K. Chan

Journal of Dispute Resolution

The philosopher Arthur Schopenhauer once wrote, “The first forty years of life give us the text; the next thirty supply the commentary on it.” Forty years after Owen Fiss’s seminal article “Against Settlement,” it is time to reimagine how we approach the settlement debate beyond traditional dichotomies. Just as Schopenhauer recognized that time and experience allow us to better understand our past, forty years of grappling with the settlement debate have given us the perspective to see its limitations, and now with emerging technologies, new possibilities. The ‘text’ of the debate—its dichotomous framing and distributive solutions—has shaped four decades of …

How Investment Activity And Corporate Governance Affect The Disclosure Of Esg, Melisa Anggraini, Darsono Darsono, Danes Quirira Octavio

Jurnal Akuntansi dan Keuangan Indonesia

Background: This research investigates the impact of corporate governance elements and corporate investment activity on ESG disclosure in companies from Asia Pacific Emerging Markets. Methods: The study analyzes a sample of 150 companies from Asia Pacific Emerging Market countries over the period 2015–2022, yielding a total of 1200 observations. The Generalized Method of Moments-Difference (GMM-DIFF) is employed to test the hypothesis. Four independent variables related to corporate governance and one variable representing investment activity are used. Findings: The results show that investment in property, plant, and equipment assets, as well as the presence of audit committees, positively affect ESG disclosure …

Cyber Insurance For Public Housing: Confronting Market Barriers And Forging Policy Solutions, Deborah L. Johnson, William B. Simpson

Student Journal of Information Privacy Law

In 2023, the Los Angeles Public Housing Authority was hit by the LockBit ransomware gang, which claimed to have exfiltrated 15 terabytes of data. In another incident, hackers impersonated a vendor and diverted nearly $1 million in housing funds from a second California agency. Public Housing Authorities (PHAs), which handle large amounts of sensitive data, are increasingly being targeted by cyberattacks. These attacks often exploit the weak cyber defenses and broad risk profiles of these relatively unsophisticated entities. With limited resources and few avenues for recovery, PHAs are left vulnerable to, and by, cyberattacks, threatening the vital services they provide. …

The Case For A Federal Data Privacy Law From A National Security Perspective - What The U.S. Can Learn From Overseas, Theodore H. Massey Iii

Student Journal of Information Privacy Law

The collection of personal data in the private sector has grown exponentially over the years, leading to an exponential growth in the theft and the purchase of personal data by criminals and foreign adversaries. While the United States has implemented EO 14117 and the Protecting Americans’ Data from Foreign Adversaries Act of 2024 to protect against the inherent national security risks associated with data privacy, the United States must create an omnibus federal privacy law if it wishes to mitigate the national security risk. This paper introduces the reader to the increase in personal data collected by private organizations and, …

Strain In The System: Examining The Relationship Between Adverse Childhood Experiences And Pathways Into Cybercrime, Hannah R. Murphy

USF Tampa Graduate Theses and Dissertations

This study examines the relationship between adverse childhood experiences (ACEs) and cybercriminal behavior among 238 active hackers with verified website defacements identified through the Zone-H archive. Drawing upon General Strain Theory, this research investigates whether childhood trauma influences both the developmental timing and motivational aspects of hacking activities. Using ordinary least squares and ordered logistic regression analyses with country-clustered standard errors, the study tested two hypotheses: (1) that higher ACE scores would be associated with earlier onset of hacking behavior, and (2) that higher ACE scores would predict increased likelihood of engaging in revenge-motivated hacking. Results revealed a complex relationship …

The Positivity Effect In Contracts, Megan Kathryn Stricker

College of Science and Health Theses and Dissertations

The positivity effect is a noted preference for positive information over negative information in older adults’ cognition. This effect has been observed in multiple domains of cognition, including both attention and memory (Carstensen & Mikels, 2005). Evidence for the positivity effect has shown up consistently in replications across type of memory (Reed et al., 2014), and this phenomenon can be considered within the context of other trends of cognitive aging. Most if not all research in this area has been constrained to laboratory settings, and there has been limited research as to the context sensitivity of this observed effect. What …

Introduction: Forever Criminalized?: How Collateral Consequences Advance Disparities In Criminal Justice, Jenny Roberts

Hofstra Law Review

No abstract provided.

The Role Of Smart Actuarial Calculations In Improving The Quality Of Financial Reports Of The Iraqi Insurance Company, Zena Abdulstar Allayla, Waheed Mahmood Al-Ibrahimi

Journal of Economics and Administrative Sciences

The research aims to explore the role of actuarial calculations supported by artificial intelligence technologies (smart actuarial calculations) in enhancing the accuracy and quality of financial reporting. Companies face challenges related to the accuracy, transparency, and speed of preparing financial reports, necessitating innovative solutions based on smart technologies. The research analyzed the literature and employed an exploratory methodology to study the impact of smart actuarial calculations on the integrity of financial reports. The association between the application of artificial intelligence and professional standards and their impact on the performance of insurance companies and financial reporting practices was also examined. The …

Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu

Research Collection School Of Computing and Information Systems

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …

Unlocking The Power Of Socio-Knowledge Association For Enterprise Risk Identification In Stock Market, Zhenghao Liu, Keng Siau, Shaochen Yang, Feicheng Ma

Research Collection School Of Computing and Information Systems

Potential risk signals reflected in supply chain and equity connections between enterprises and social connections between investors are becoming crucial to identifying enterprise risks in addition to basic financial indicators. Traditional risk management systems face challenges in adapting to these complexities, highlighting the need for a proactive paradigm shift in risk management. Leveraging graph models such as social networks and knowledge graphs offers a promising approach to identifying and managing potential associated risks effectively. To bridge existing research gaps, a novel risk identification framework driven by social-knowledge graphs has been proposed, integrating graph deep learning and reinforcement learning techniques guided …

Ai Governance And Algorithmic Auditing In Financial Institutions: Lessons From Singapore, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

This paper examines the role of algorithmic auditing as a mechanism for responsible AI development and deployment in the financial sector, with a particular focus on Singapore’s regulatory and institutional initiatives. Against the backdrop of fragmented global artificial intelligence (AI) governance frameworks, the study analyses how Singapore has developed operational tools — such as the Veritas Toolkit, AI Verify, Project Moonshot and Project Mindforge — that go beyond abstract ethical principles to provide measurable, use-case-specific standards for auditing AI systems. These initiatives contribute to standardising audit practices, enhancing transparency and bridging trust gaps between financial institutions, regulators and stakeholders. The …

Combating Corruption In Heavily Indebted African Nations: The Role Of A Transparent Public Procurement System, Lauren G. Harkins

Washington Semester Program

This paper explores the implementation of transparency-focused public procurement reforms adopted during the HIPC Initiative and compares longitudinal corruption trends in 18 African countries after the reform initiative. Using IMF documentation and WGI corruption data, the study compares countries that implemented procurement and financial transparency measures with those that did not. The results show no consistent link between these reforms and improved corruption scores, highlighting the influence of political instability, sector-specific conditions, and limited implementation capacity. The study underscores the importance of realistic, incremental reforms and adequate institutional support, especially as countries adopt electronic procurement systems.

The Impact Of Artificial Intelligence On Esg: A Conceptual Framework For Practitioners And Policymakers, Mario Marlito R. Domingo

Journal of Management for Global Sustainability

The growing integration of artificial intelligence (AI) into corporate ecosystems has reshaped the Environmental, Social, and Governance (ESG) landscape. While AI offers powerful tools for sustainability optimization, social equity advancements, and governance automation, it also introduces significant challenges such as high energy consumption, bias in algorithmic decision-making, and regulatory uncertainties. Existing research often isolates these aspects rather than examining AI’s role within an interconnected ESG framework. This paper presents a conceptual framework—the AI-ESG Integration Model—for systematically analyzing AI’s dual impact on ESG initiatives. The model categorizes AI’s influence into three pillars: 1) AI’s role in environmental sustainability (E), addressing energy-intensive …

South Carolina And The Blue Ridge Rail Road, 1852 To 1873: Dreams Of Linking The South And West, Steve E. Gibson

Doctoral Dissertations and Projects

While the 1850s were productive and railroading did innovate with the initial adoption of steel, the embrace of the telegraph, the use of coal as a fuel, and the opening of generally long railroads, particularly in South Carolina, the railroads themselves generally remained conservative (almost timid) in their business models and strategic thinking. This was especially true in the South. Northern railroads recognized the need to connect major cities, connect seaports with crop productive hinterlands, and generally understood that railroad mobility, mixed economic development, and prosperity were intertwined and mutually reinforcing with the prosperity of both the railroad and the …

Reconceptualization Of Corporate Governance For Fintech Firms, Isa Alade

Loyola of Los Angeles International and Comparative Law Review

The scandals involving some major fintech firms/platforms such as FTX, Wirecard, Celsius, TerraLuna and Lending Club raise questions about the efficacy of the current corporate governance architecture for fintech firms. Comparing some of the factors that contributed to these recent scandals in the fintech industry against the weaknesses in the corporate governance arrangements for traditional financial firms in the period leading to the global financial crisis (GFC), this paper argues that the unique characteristics of fintech firms amplify some of the weaknesses in the corporate governance arrangements in the financial services industry that contributed to the GFC. The ubiquitous nature …

Exploring Contemporary Issues Through Accounting Case Studies, Emaline Myers

Honors Theses

This thesis was completed as part of the course “Accounting 420- Independent Study” under the guidance of Dr. Victoria Dickinson and includes a collection of five case studies exploring the role of accounting, ethics, and policy in today’s world. Each case investigates how financial information, government decisions, and business strategy intersect, and what that means for professionals entering the accounting field. The topics covered in this thesis range from international financial fraud to environmental disclosures, military spending, public education, and small business development. Several key takeaways emerged from the completion of these cases. Case Study I highlights issues in global …

Exploring Financial Implications Through Case Studies Of Various Economic, Financial, And Social Factors, Rachel Spencer

Honors Theses

This thesis explores many topics surrounding the financial, economic, and social implications of key issues identified in various case studies. Topics explored include financial fraud presented in The China Hustle, a reflection on the United States’ military industrial complex and their role in the Ukrainian war, the increasing prevalence of Environmental, Social, and Governance (ESG) programs, the ideology of Dr. Ben Carson and Dr. Thomas Sowell, and a start-up business plan for a brewery.

Each chapter in this thesis analyzes one of these complex topics. This thesis is a critical reflection of how economic implications, government strategies, and social beliefs …

Full Issue

Tenor of Our Times

No abstract provided.

Democracy And Poverty: The Nigerian Experience, Tonyesima Furro

Journal of African Policy Studies

This study examines the plight of the poor within the new democratic rule in Nigeria. Since the inception of democracy in 1999, there has been a sustained prosperity in terms of oil windfall that swells government's revenues on the order of over $30 billion annually. For the first time in over 20 years, the GDP growth rate is 7.5 percent. The nation has accumulated an impressive foreign exchange reserve of $60 billions. This reserve is the highest ever in sub-Saharan Africa. However, this financial outlay has not resulted in improved standard of living of the broad majority of the people. …

Halo: Heterophily-Aware Label-Free Ordering For Unsupervised Graph Fraud Detection

Defensive Publications Series

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a …