Digital Commons Network^™

Bridging The Expectations Gaap In Financial Reporting, Neal F. Newman

Faculty Scholarship

The true nature of auditing publicly traded companies’ financial statements has been somewhat of an enigma to the general public. Due to this mystery, an “expectations gap” has formed between the public expectations of auditors’ roles and what the auditor actually does in their analysis of financial statements. Auditors are an extremely important piece of the financial reporting puzzle because they determine whether or not a company’s financial statements are a fair and accurate reflection of the company’s financial position, a determination that can be a major influence on how the financial strength of a company is perceived. Newman’s Article …

Foreign Director Exit In The Midst Of Deteriorating Bilateral Political Relations, Xiaocong Tian, Yuehua Xu, Daphne W. Yiu

Research Collection Lee Kong Chian School Of Business

Amid global geopolitical realism pushing for a race of hegemonic rivalry nowadays, an outflow of global human talents is a pressing organizational concern. Our study draws attention to the underexamined phenomenon of the exit of foreign directors from their host countries during geopolitical tensions. Theorizing from a sensemaking logic, we posit that the deterioration of bilateral political relations serves as an unexpected event that activates foreign directors’ schemas for dual identity conflict, propelling them to react behaviorally to such identity threats by exiting the board in the host country. In addition, we further posit that the sensemaking process is contingent …

User Privacy In The Digital Playground: An In-Depth Investigation Of Facebook Instant Games, Sideeq Bello

LSU Master's Theses

Amid growing concerns over data privacy in web and mobile applications, this study aims to assess the privacy mechanisms in instant games on Facebook, a platform with approximately 3.03 billion monthly active users and a substantial repository of personal data. Instant Games have become increasingly popular due to their ease of access and social integration features. Investigating these games can provide insights into privacy mechanisms and practices, thereby informing the development of more fair, compliant, and user privacy-centric gaming experiences. Thus, this study proposes an integrated analytical framework that leverages a combination of descriptive, memory, and network analysis techniques to …

Emotional Disinformation And Engagement Bait: Ai-Generated Images On Facebook, Hyrije Mehmeti

UBT International Conference

Emotional disinformation and engagement bait: AI-generated images on Facebook PhD. Student Hyrije Mehmeti1 Abstract Images generated with Artificial Intelligence (AI) are emerging as a powerful tool for disinformation, particularly on Facebook, where content spreads rapidly and reaches wide audiences. This paper examines how such images are used as engagement bait in Albanianlanguage Facebook posts to spread emotional disinformation. The study draws on fact-checkerverified cases where fabricated visuals were paired with captions designed to provoke user interaction and amplify reach. Using a qualitative case study approach, the analysis was guided by three criteria: the post included an AI-generated image, the text …

Web3-Based Identity And Kyc Innovations For Next-Generation Fintech, Usama Arshad, Abdallah Tubaishat, Sajid Anwar, Zahid Halim, Abedallah Abualkishik, Abrar Ullah

All Works

The growing reliance on digital financial services necessitates a secure, efficient, and privacy-centric approach to identity verification and Know Your Customer (KYC) compliance. Traditional identity management systems rely on centralized databases, making them susceptible to data breaches, inefficiencies, and regulatory constraints. Over 10 billion identity records have been exposed in centralized KYC breaches, leading to a 60% increase in financial fraud cases. The rise of Decentralized Finance (DeFi) has further complicated KYC compliance, requiring innovative solutions that balance privacy and regulatory requirements. This paper proposes a Web3-powered decentralized identity framework that leverages blockchain technology, self-sovereign identity (SSI), verifiable credentials (VCs), …

Examining Iot-Enhanced For Current Developments In Face Image Authentication (Fia) Methods And Their Drawbacks, Marwa Jamal Hadi, Emaan Ouudha Oraby

Al-Esraa University College Journal for Engineering Sciences

The quick development of IoT and facial image manipulation (FIM) algorithms, as well as the growth of their user-friendly applications, highlight the pressing need for manipulation detection methods. These techniques need to demonstrate how face photos have been altered and validate their legitimacy. The scientific community has recently taken notice of the phrase “DeepFakes” and methods for detecting them. Take note of the latest methods for identifying watermark-based face image modification as well. The important thing to remember is that every one of these methods has its own set of drawbacks. This study provides a brief introduction to face image …

Emotionally Unstable: Addressing Emotional Distress As A Concrete Injury In Data Breach Cases Post-Transunion, Anna P. Cox

Fordham Law Review

Data breaches and data breach litigation are exponentially on the rise. Plaintiffs whose information is stolen in a data breach often claim emotional distress for fear of future harm the data breach may cause. However, plaintiffs who bring suit in federal court must show that they have suffered an injury in fact for purposes of Article III standing before a federal court will exercise jurisdiction. The U.S. Supreme Court’s decision in TransUnion LLC v. Ramirez requires that plaintiffs who seek money damages under a theory of risk of future harm show that they have suffered a present concrete injury to …

Assessing Market Efficiency In Corporate And Securities Litigation, Charles Korsmo, Minor Myers

Indiana Law Journal

In recent decades, courts have increasingly looked to trading prices as evidence—often conclusive evidence—in high-stakes corporate law disputes over a company’s fair value. This development has been especially dramatic, and consequential, in Delaware. Where a stock trades in an efficient market, the logic goes, the prevailing trading price can be used to resolve any disputed issue of valuation. But this expedient comes with an unavoidable question: When is a market “sufficiently efficient” for a court to rely on it as a measure of value?

 Federal courts have long experience evaluating the relative efficiency of trading markets in the context of …

Future Crime: A Theoretical Foundation For Designing Effective Cybercrime Laws In The Age Of Ai And Ransomware, Thi Ha Do, Niloufer Selvadurai

North Carolina Journal of Law & Technology

No abstract provided.

A Comprehensive Review Of Financial Knowledge Graphs, Jeyaraman Brindha Priyadarshini, Bing Tian Dai, Yuan Fang

Research Collection School Of Computing and Information Systems

Knowledge Graphs (KGs) are increasingly used in finance to manage complex, interconnected data and support advanced analytics. This survey provides an overview of how KGs are applied across various financial areas, such as fraud detection, credit risk assessment, anti-money laundering, and regulatory compliance. We examine key techniques for building and using KGs in finance, including graph construction, embedding methods, and machine learning models. The survey also discusses challenges specific to finance, like handling private data, ensuring interpretability, and managing real-time data. Additionally, we explore the emerging combination of KGs with large language models and generative AI, which offers new possibilities …

Assessing The Effectiveness And Implementation Of Artificial Intelligence In Detecting And Preventing International Financial Crimes: A Case Study Of The United Kingdom, Robert J. Dalton

OneUSF Undergraduate Research Conference

Financial crime has been an issue that has plagued the world for as long as the concept of trade has existed. With the rise of the digital age and globalization, financial crime has continuously and significantly evolved, with increasingly complex and intricate methods of financial crime and fraud being committed each year. Government organizations and financial institutions are having to constantly overcome new barriers and challenges to monitor financial crimes due to their evolving nature. Artificial intelligence, a new technology that is being constantly researched and explored, has the potential to innovate current and new technologies within the financial intelligence …

A Federated Approach To Scalable And Trustworthy Financial Fraud Detection, Yaser Alhasawi, Aljwhrh Abdalaziz Almtrf, Muhammad Asad

School of Engineering, Computing and Mathematics

Financial fraud remains a critical challenge for digital banking, requiring detection solutions that ensure both scalability and data privacy. Traditional centralized approaches face limitations due to security risks and system bottlenecks. This paper proposes FedFraud, a novel federated learning (FL) framework that detects fraudulent transactions without sharing raw data. FedFraud introduces two key innovations: (i) a trust-aware client aggregation mechanism that assigns weights based on update reliability and (ii) an asynchronous communication protocol enabling clients to contribute updates independently. Evaluated on the Credit Card Fraud Detection dataset under a nonidentically distributed (non-IID) data setting where client data distributions differ significantly, …

Gun Violence Declines Came Through Strategic Planning And Community Focus (Op-Ed), Alvin Bragg

Other Publications

No abstract provided.

Older Adults And Financial Exploitation, Aaron Li, Natalie Galucia, Cal J. Halvorsen

Harvey A. Friedman Center for Aging

The National Adult Protective Services Association defines elder financial exploitation as misuse, mishandling, or exploitation of an older adult’s property, possession, or assets. These actions often occur without consent and are typically carried out through false pretense, undue influence, coercion, or manipulation.

Ponzilens+: Visualizing Bytecode Actions For Smart Ponzi Scheme Identification, Xiaolin Wen, Tai D. Nguyen, Shaolun Ruan, Qiaomu Shen, Jun Sun, Feida Zhu, Yong Wang

Research Collection School Of Computing and Information Systems

With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponzi schemes, a reliable and transparent identification approach adaptive to various smart Ponzi schemes is still missing. To fill the research gap, we first extract semantic-meaningful actions to represent the execution behaviors specified in smart contract bytecodes, which are derived from a literature review and in-depth interviews with domain experts. We then propose PonziLens+, a novel visual analytic approach that provides an intuitive …

Identifying Potentially Illicit Money Laundering And Terrorism Financing Transactions Through Machine Learning Techniques, Shiuh Tong Lim, Rou Qing Khoo, Khai Wah Khaw, Xinying Chew

International Journal of Management, Finance and Accounting

Financial institutions worldwide face significant challenges in detecting and preventing illicit financial activities, such as money laundering and terrorism financing. Traditional rule-based methods often generate high false positive rates, increasing manual verification efforts and higher operational costs. This research explores machine learning techniques to enhance the detection of suspicious transactions. Several algorithms, including K-Nearest Neighbors, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine, and Naïve Bayes, are applied and evaluated using a dataset from a financial institution. After a comprehensive performance assessment, the Random Forest model is the most effective, exhibiting the highest accuracy of 0.9333 in identifying suspicious …

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Generating Financial Risk Tiers For Geo-Located Entities Using Monetary Claims From User-Generated Content And Transactional Signals, Mithun Kumar S R

Defensive Publications Series

Moderation systems for local business platforms may have difficulty identifying certain types of financial fraud, such as overcharging or fake deposit scams, because of challenges in validating monetary claims found in user-generated content (UGC). A disclosed method can address this potential limitation by synthesizing two different data streams. A system can use technologies, for example natural language processing and computer vision, to extract unstructured financial claims and pricing information from UGC, which may include content such as text reviews and photos of menus or receipts. The system can then correlate these claims with structured, verified transactional signals from integrated payment …

Design And Development Of Machine Learning And Deep Learning Based Algorithms For Cyberattacks Detection, Abiramasundari S

Theses and Dissertations

E-mail is the fastest mode of communication. It is amongst the most commonly used modes of communication and can be used for both legal and illegal purposes. Many elements that could be useful in detecting email fraud are constantly being investigated. Phishing assaults in which thieves use Internet to trick consumers into visiting fake websites are very common and are causing significant harm to victims. Several methods of filtering phishing emails have been devised but a solution to the problem still evades. One of the attacks addressed in this research is email phishing. Machine learning is a popular and efficient …

Investigations Of Secure Memory For Vlsi Based Crypto System, Vijay Sai R Mr

Theses and Dissertations

Semiconductor technology is growing very rapidly in their architectural developments, involving the usage of processor and memory. Presence of memory, in general is a vital commodity in various devices which are almost embedded into human activity, from robust work stations to handy mobile phones. Security of data stored in memory is very important, and hence, observation must be made that these valuable data should not be thwarted by malicious means. Security in cache memory is a major issue in memory related applications such as smart cards and bio-metric implementations.

Cache, is a small and limited memory located between central processing …

Enhancing Fraud Detection: A Comprehensive Analysis Of Financial Transactions Using Data Visualization, Statistical Models, And Machine Learning Techniques, Juliana Patrone

Master’s Theses and Projects

No abstract provided.

Revisiting Android Permission Evolution Through Temporal Analysis: A Data-Driven Study Of Behaviors And Implications, Ali Al Kinoon

Graduate Thesis and Dissertation post-2024

Malicious applications continue to pose significant privacy and security risks within the Android ecosystem, often exploiting user permissions and obscuring data collection practices behind opaque privacy policies. To address these challenges, this dissertation presents a comprehensive framework for enhancing Android app security through dataset construction, permission behavior analysis, and privacy policy classification. The framework systematically investigates key issues such as dataset fidelity, permission misuse, model performance, and the alignment between declared and actual data practices. Through a combination of empirical analysis and machine learning techniques, this work advances the development of more transparent and secure mobile applications. The first part …

Gnsi Decision Brief: Building Trust In Digital Response: The Role Of Chatbots In Cybercrime Prevention Open-Source Tools For Safer Digital Reporting And Public Trust, George Burruss, Loni Hagen, Ly Dinh, Lingyao Li

GNSI Decision Briefs

Cybercrime poses a growing global threat, inflicting financial harm on individuals, businesses, and governments. In 2024 alone, the FBI's Internet Crime Complain Center (IC3) received over 800,000 complains, with reported losses exceeding $16.6 billion, a 33 percent increase from the prior year. The damage extends beyond financial loss: cybercrime undermines trust in digital systems and can cause psychological harm. Its anonymous and complex nature makes detection and prosecurtion difficult, creating low-risk, high-reward conditions for offenders.

The Role Of Data Analytics In Detecting Unemployment Insurance Fraud: A Case Study Of State Governments In The United States, Tina Louise Carkhuff

Doctoral Dissertations and Projects

The unprecedented surge in unemployment insurance claims during the COVID-19 pandemic exposed state labor agency systems in the United States to significant fraud risks, resulting in billions of dollars in improper payments. This study investigated the role of data analytics in detecting and mitigating unemployment insurance fraud, with a focus on state government responses. Using a single-case study approach, I examined how advanced data analytics, including machine learning, predictive modeling, and strategies to identify fraudulent claims, can reduce fraud within unemployment insurance systems. The study also included an investigation of systemic vulnerabilities and the impact of policy improvements on fraud …

Turning The Tables With Tech: Scambaiting Tools And Their Applications, Doetri Ghosh, Tatiana Renae Ringenberg

Discovery Undergraduate Interdisciplinary Research Internship

Online financial scams have become increasingly complex, utilizing fake social media accounts, remote access tools, deepfake recordings, and other advanced scamming tactics. In response, a growing community of scambaiters have matched this technological sophistication, in their efforts to disrupt the scam ecosystem. Various tools are used throughout scambaiting sessions, where proper use of technology is essential for success. Limited research exists on the changing needs and tools involved in scambaiting. This study bridges this gap by conducting an exploratory qualitative analyses of 250 posts from the Reddit group r/Scambait. We explore various aspects of technology use, specifically looking into recommendations …

Examining The Active Ingredients In An Anti-Stigma Intervention For Individuals With A Criminal History, Kaylee Cook

Electronic Theses and Dissertations Archive

Obtaining employment is key to successful reintegration for people who have been incarcerated. However, employers are often hesitant to hire formerly incarcerated persons due to general negative attitudes toward them and concern that they will not exhibit positive work behaviors. Interventions to reduce general types of stigma exist, with primary strategies involving education and/or contact. Most research examining the effectiveness of stigma-reducing strategies largely focus on reducing identity-based stigma (e.g., race, mental illness), with limited research on reducing choice-based stigma, such as those with criminal histories. However, regardless of targeted stigma, there is no consensus regarding which intervention format (i.e., …

Anomalygfm: Graph Foundation Model For Zero/Few-Shot Anomaly Detection, Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. …

Affinitytune: A Prompt-Tuning Framework For Few-Shot Anomaly Detection On Graphs, Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces …

Enhancing Urban Governance For Inclusive Growth And Sustainable Development, Asti Amelia Novita

Journal of Environmental Science and Sustainable Development

In light of global challenges such as climate change, inequality, and governance shortcomings, cities have a vital part to play in achieving sustainable development. Numerous local governance systems are deficient in transparency, public participation, and social fairness mechanisms essential for implementing sustainability. This study investigates how strengthening transparency, public engagement, and social fairness can enhance Malang City’s capacity to achieve the Sustainable Development Goals (SDGs). This study employed a mixed-methods approach, using the Sustainable Value Mapping and Analysis (SVMA) framework to assess insight from 250 stakeholders, including local officials, civil society groups, and community representatives. Findings reveal modest but statistically …

The Collection Problem: How The Circuit Split On Pleading Standards In Securities Fraud Claims Undermines Federal Regulatory Goals, Lucas Immer

St. John's Law Review

(Excerpt)

The Great Depression is generally recognized as the greatest economic calamity in United States history. One of the Great Depression’s many causes was reckless financial speculation driven in part by financial fraud. In response to the crisis, Congress passed the 1934 Securities Exchange Act (“the Exchange Act”), which courts have long held creates a private right of action for plaintiffs who experience an economic loss due to reliance on a material misstatement surrounding the purchase or sale of a security. A prima facie claim for securities fraud under the Exchange Act requires a showing of scienter, defined as “a …