Digital Commons Network^™

Private Coverage, Public Risk: The Role Of Cyber Insurance In National Security Governance, Deborah L. Johnson

Faculty Publications

This Article explores the growing yet underexamined role of the commercial cyber insurance market in shaping the United States' approach to cybersecurity readiness. It argues that, as cyber threats against U.S. critical infrastructure and essential services increase, commercial cyber insurance has quietly come to function as a de facto governance tool in national cybersecurity. In hospitals, water and energy systems, and other public-facing sectors, policy language, underwriting questionnaires, and post-breach claims practices help define what counts as "reasonable" security, influence how organizations plan for and respond to incidents, and affect the pace and scope of recovery after an attack. Yet …

Financial Literacy And Fraud Vulnerability In Digital Finance: Evidence From The 2024 Nfcs, Mercedes Palacios Diaz

Posters - 2026

Digital finance has expanded rapidly through fintech platforms and cryptocurrency markets, increasing access to financial services while also exposing individuals to higher levels of financial fraud. Most research treats fraud vulnerability as a single outcome, without distinguishing between financial loss and the ability to recognize scams. This study examines how financial literacy shapes fraud vulnerability through its effects on fraud victimization and scam awareness, highlighting its dual role in reducing financial losses and improving scam recognition. Fraud vulnerability is therefore analyzed across two distinct dimensions: realized financial loss and scam awareness.

Financial Fraud In Mississippi Public Agencies: Case Studies Of Control Failures And Implications On Civic Engagement, Gracie Sullivan

Honors Theses

This study analyzes the relationship between financial fraud in Mississippi public institutions and its potential impact on civic engagement. While the existing literature explores the mechanics of fraud through frameworks such as the Fraud Triangle and the role of internal controls in preventing misconduct, limited research examines how government corruption influences civic disengagement, particularly in Mississippi. Using a quantitative, comparative research design, this study examines the relationship between major public financial fraud cases and voter turnout in case-linked and comparison counties. These cases include the Mississippi TANF welfare fraud, the Department of Corrections bribery case, and the misuse of funds …

Leveraging Accounting Analytics To Enhance Payroll Accuracy And Fraud Detection In U.S. Public Sector Institutions: A Case Study Approach, Regina Debrah

Beacom School of Business Student Publications

This study explores how accounting analytics can be leveraged to enhance payroll accuracy and improve fraud detection in U.S. public-sector institutions, addressing persistent irregularities amid rising demands for fiscal transparency. The research employs a qualitative design with secondary sources including academic literature, reports, and case studies. The literature identifies successful analytics implementation, such as Treasury OPI’s machine learning for data integration for unemployment claims. These precedents demonstrate direct transferability to payroll’s high volume and rules-based structure. Findings show that analytics significantly reduce improper payments through real-time screening, data integration, and risk prioritization when embedded in workflows. The findings also show …

Ai In Government Accounting & Fraud Detection, Amy Hernandez Paz

Doctoral Student Association Conference

The United States is seeing an alarming rise in financial fraud and suspicious activity trends within its government entities (Bruce et al., 2024). According to the Accountability Office (2024) the federal government estimates the annual financial loss has increased from $233 billion to $521 billion, based on data from fiscal years 2018 through 2022. Advancements in technology are not strictly limited to government financial solutions. The Federal Bureau of Investigation (2024) found a rise in generative Artificial Intelligence (AI) exploitation among criminals to facilitate financial schemes such as fraud and extortion. The literature review aims to investigate the gap in …

Can Ai Fix Anti-Money Laundering? The Case For Federated Intelligence In Financial Crime Prevention, Matthias Connelly

Student Journal of Information Privacy Law

The global anti-money laundering (AML) regime is failing. Trillions of dollars are laundered each year, yet governments detect only a fraction of that activity, even as financial institutions spend hundreds of billions on compliance. In the United States, AML regulation has evolved from a retrospective, prosecution-oriented framework into an expansive, preventative regime that measures inputs rather than enforcement outcomes. Although artificial intelligence has demonstrated potential to improve detection, firm-siloed AI systems introduce substantial financial, technological, and systemic risks, including prohibitive development costs, data- privacy constraints, and market concentration among third-party service providers. This Article argues that federated learning offers a …

Adolph Michelin V. Warden Moshannon Valley Correctional Center

2026 Decisions

USDC for the Western District of Pennsylvania

Combatting Authorized Push Payment Fraud: Which Regulatory Approach Should The United States Adopt?, Garrett S. Grewal

North Carolina Banking Institute

No abstract provided.

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …

Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin

Research Collection School Of Computing and Information Systems

This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published …

Graph Convolution Neural Network And Deep Q-Network Optimization-Based Intrusion Detection With Explainability Analysis, Kelvin Mwiga, Mussa Dida, Leandros Maglaras, Ahmad Mohsin, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to capture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a …

Strategies For Reducing Fraud In Corporate Organizations, Tarshall Horton Mccauley

Walden Dissertations and Doctoral Studies

Financial statement fraud has been a persistent problem that poses risks to corporations globally. Business leaders are concerned about financial statement fraud due to its potential to weaken financial stability, erode stakeholder confidence, and create conditions that may lead to significant financial distress or organizational collapse. Grounded in the fraud triangle theory, the purpose of this qualitative pragmatic inquiry research was to explore internal control strategies corporate leaders use to reduce financial statement fraud risk. The participants were 11 financial leaders in North Carolina, the District of Columbia, and Virginia who practiced effective internal controls to minimize financial fraud. Data …

National Ai Forensic Grid: Federated Multi-Modal Deepfake Detection And Cryptographic Evidence Infrastructure For Law Enforcement, Pranav Bhatnagar Mr

Defensive Publications Series

This disclosure presents a federated forensic infrastructure designed for detection of malicious synthetic media and preservation of evidentiary integrity across law enforcement jurisdictions. The system integrates multi-modal deepfake detection, cross-node artifact intelligence exchange, and cryptographic chain-of-custody preservation. The disclosed architecture enables scalable national coordination, standardized forensic scoring, and tamper-resistant evidence management suitable for judicial proceedings.

Ai Role-Play Interview Simulation In A Graduate-Level Research Methods Course, Julie Siddique

Teaching Repository of AI-Infused Learning

To enhance qualitative research skills and deepen student engagement, I introduced an AI-driven role-play strategy in a graduate-level research methods course. Students were asked to practice interview skills using an AI bot that responded as a simulated persona, allowing students to practice semi-structured interviewing and navigate ethical considerations in a simulated context.

Quantum Chebyshev Transform-Based Graph Neural Networks For Financial Fraud Detection, Minrui Xu, Bingyan Guan, Bethel Hui Ting Loke, Paul R. Griffin

Research Collection School Of Computing and Information Systems

Financial fraud detection is a critical challenge requiring accurate identification of anomalous patterns in complex transaction networks. Graph Neural Networks (GNNs) have emerged as powerful tools for fraud detection by capturing relational structures among entities. Meanwhile, quantum computing offers new possibilities to enhance machine learning through high-dimensional Hilbert spaces and parallelism. In this paper, we propose a hybrid classical-quantum model called QCTGNN (Quantum Chebyshev Transform-based Graph Neural Network) for financial fraud detection. The QCTGNN integrates a classical graph neural network component based on Simplified Graph Convolutions (SGConv) with a quantum component that performs a Chebyshev polynomial-based transform via variational quantum …

Abuse Of Contract: A Proposal For A New Cause Of Action, Miriam A. Cherry

William & Mary Law Review

With the growth of online commerce and the platform economy, many companies are including provisions in their online terms and conditions that extend far beyond what reasonable consumers would expect. Some terms and conditions purport to bind customers to separate contracts in future transactions that have little to do with the first contract. Other boilerplate purports to cover family members of the customer who created an account. Some retailers have argued that people shopping in their brick-and-mortar stores are subject to terms and conditions because those shoppers had at some point previously created an online account. For example, Disney argued …

Money Over Everything: Reimagining Health Care Enforcement, Jacob T. Elberg

Missouri Law Review

Annual press releases from the Department of Justice trumpet billions of dollars in annual recoveries and optimism that its health care enforcement regime deters even more fraud than it addresses. Yet DOJ’s consistent recoveries can be seen as a sign that fraud continues unabated. While some scholars have questioned the deterrent value of the government’s model of enforcement through civil settlements, little attention has been given to what may be the most important aspect of DOJ’s effort—its focus on money both as the sole measure of harm and the sole means of deterrence.

Leveraging Blockchain Technology In Mining Supply Chain Management: Vietnam Coal Mining Case Study, Thu Hang Nguyen, Nguyen Trung Tuan, Hong Anh Le

Journal of Sustainable Mining

The global economy heavily relies on the mining industry for essential resources such as coal, oil, gas, and metal ores. However, the intricate nature of mining operations poses significant challenges in supply chain management (SCM). This research investigates how blockchain technology can address these challenges within mining supply chain management (MSCM). Through a systematic review of existing research and projects, a conceptual blockchain model is proposed to improve mining supply chains’ transparency, traceability, efficiency, and sustainability, specifically focusing on coal supply chain management in Vietnam. The model integrates distributed ledgers, smart contracts, IoT devices, identity management, and consensus mechanisms to …

Applying Total Quality Management And Auditor Size Theories To Audit Quality: Evidence From Tanzanian Auditing Firms, Freddy Chaula, Henry Zeno Chalu, Paul Ambege

Business Management Review

Amid mixed global findings on the determinants of audit quality, particularly in emerging markets, the role of engagement team competence and audit firm size remains underexplored. This study investigates the relationship between engagement team competence, audit firm size, and audit quality in Tanzanian auditing firms. The hypotheses were tested using PLS-SEM based on data obtained from a cross-sectional survey of 147 Tanzanian audit partners. Audit firm size (β = 0.309, p < 0.01) and engagement team competence (β = 0.567, p < 0.01) were found to have significant positive effects on audit quality. These results suggest that audit quality is significantly enhanced by audit firm size and engagement team competence, thus supporting total quality management and auditor size theories. These findings highlight the need for regulators to strengthen auditor competence and to support the growth of small auditing firms, thereby enhancing their capacity, resources, and the overall audit quality. For auditing firms, the findings underscore the need to develop engagement team competence through initiatives such as professional training, mentoring, and knowledge-sharing programs. Additionally, firms should expand their capacity to strengthen audit quality. While focused on the Tanzanian context, the findings offer broader insights for improving audit quality in developing economies.

Credit Card Fraud Detection Using Metaheuristic Techniques, Narges Sabry Anwer Mohammed, Mohammed Sabry Saraya, Amr M. Thabet, Labib M. Labib

Mansoura Engineering Journal

Credit Card Fraud Detection (CCFD) has become a critical challenge to financial security due to increasingly sophisticated fraudulent activities. This study investigates the effectiveness of Meta-Heuristic (MHT) optimization techniques in improving fraud detection (FD) through feature selection (FS) and model optimization. To address class imbalance, Random Under-Sampling (RUS) was applied. The selected feature subsets were evaluated using three machine learning (ML) classifiers—Decision Tree (DT), KNearest Neighbours (KNN), and XGBoost (Xgb-Tree)—across four benchmark datasets: European, Statlog (Australian Credit Approval), PaySim, and Credit Card Transactions Fraud Detection (CCTFD). Eleven binary MHT algorithms were implemented and compared. The comparative analysis shows that the …

Generative Identity Theft: Criminalizing Deepfakes Using The Right Of Publicity, Dustin Marlan

Akron Law Review

No abstract provided.

A Centralized Defense Against Hospital Cyberattacks, Jack Woehler

UIC Law Review

No abstract provided.

The Ai Advocate: Tracking The Impact Of Artificial Intelligence On Trial Advocacy, John G. Browning

Loyola University Chicago Law Journal

In his annual State of the Judiciary address on December 31, 2023, Chief Justice John G. Roberts chose to focus on the use of generative AI. He cautioned that while AI "has great potential to dramatically increase access to key information for lawyers," its use "requires caution and humility" because of the risk of "dehumanizing the law." Chief Justice Roberts' timely warning, made at the height of concerns nationally about lawyers relying on fabricated case citations caused by the "hallucinations" of generative AI, could have just as easily been targeted not only to the legal profession generally, but to trial …

Assessing The Efficacy Of Homeland Security Task Forces (Hstf) Per Executive Order 14159, Robert J. Hehl, Stella Difronzo, Maribel Ramirez, Treyson Carino, Samuel Zwiener

CJC 450 - Criminal Justice and Criminology Capstone

This Capstone Research Assessment (CRA) will assess the efficacy of Homeland Security Task Force (HSTF) operations in Rhode Island as mandated under Presidential Executive Order 14159 (January 20, 2025), Section 6(b), Protecting the American People Against Invasion. Through review of national data, interviews of HSTF Rhode Island partner agency members, government and peer reviewed journals, and open-source reporting, this assessment will examine the efficacy of the HSTF mission including interagency coordination, resource allocation, and enforcement effectiveness. Furthermore, this CRA will assess the extent to which HSTF units have achieved intended objectives and identify operational and environmental limitations impacting overall success. …

Tackling The Societal And Regulatory Challenges Of Emerging Technologies: A Case Study Of Deepfake, Jingyao Li

2026

Governing emerging technologies such as Artificial Intelligence (AI) poses enduring challenges for policymakers, industries, and societies. Early-stage governance is often hindered by limited understanding of technological implications, rapid innovation cycles, and resistance from powerful industry actors who favor minimal oversight. Yet, timely and effective governance is essential, as new technologies are most malleable in their formative stages. This dissertation examines how emerging technologies can be governed effectively by using deepfakes technology as a focal case. This dissertation comprises three interrelated studies.

The first paper reviews the literature on deepfakes and emerging technology governance, identifying the distinct characteristics of deepfake technology …

Prosocial Ceos And Accounting Manipulation, Mei Feng, Weili Ge, Zhejia Ling, Wei Ting Loh

Research Collection School Of Accountancy

This paper examines the association between chief executive officers’ (CEOs’) prosocial tendency and their firms’ likelihood of accounting manipulation. We measure CEOs’ prosocial tendency based on their involvement with charitable organizations. We find that prosocial CEOs are less likely to engage in accounting manipulation, as proxied by material non-reliance restatements and SEC or DOJ enforcement actions. The effect is more pronounced when CEOs are involved with charities that directly aim to improve the welfare of others and when they face stronger incentives to misreport. These results continue to hold in analyses of changes in CEOs’ prosocial tendency around turnover events. …

Closing The Door On Housing Discrimination: Why Rhode Island Must Enact A Fair Chance In Housing Act, Jessica Galego

Roger Williams University Law Review

No abstract provided.

Abuse Of Contract: A Proposal For A New Cause Of Action, Miriam A. Cherry

Faculty Publications

With the growth of online commerce and the platform economy, many companies are including provisions in their online terms and conditions that extend far beyond what reasonable consumers would expect. Some terms and conditions purport to bind customers to separate contracts in future transactions that have little to do with the first contract. Other boilerplate purports to cover family members of the customer who created an account. Some retailers have argued that people shopping in their brick-and-mortar stores are subject to terms and conditions because those shoppers had at some point previously created an online account. For example, Disney argued …

Adaptive Real-Time Fraud Detection Using Online Learning And Explicit Concept-Drift Detection, Purva Govind Tugaonkar

Master's Projects

Real-time credit card fraud detection faces challenges such as extreme class imbalance, delayed feedback, and concept drift in transaction streams. This project implements and evaluates an adaptive streaming fraud detection framework based on three methodologies: (1) online learning with incremental updates, (2) explicit conceptdrift detection using statistical monitoring, and (3) separate models for immediate and delayed supervision, combined with cost-sensitive learning and anomaly detection. The system processes the credit card fraud dataset in a batched streaming fashion, uses multiple online learners and ensembles. Experiments show that online, driftaware models maintain high recall on frauds while controlling false positives under imbalanced …

Fitting A Square Peg In A Round Hole – The Regulatory Landscape Of Cryptocurrency, Jaxon Hill

Minnesota Journal of Law, Science & Technology

No abstract provided.