Digital Commons Network^™

Usd Magazine Fall 2026, Publications Department

University of San Diego Magazine (1992- )

Beauty, Goodness and Truth; Dear Toreros; Around Alcalá Park; Counsel for the Community; Faith in Action; Toreros Together for Global Impact; Climate Collaborative; At the Edge of It All; Global Perspectives; Torero Athletics; Toreros Making Waves; In Memoriam; Leading With Love

The Role Of Risk-Based Audit Quality In Enhancing Financial Control Efficiency And Mitigating Financial Corruption In Iraqi Banks In Light Of The Ippf 2024 Framework, Nadia Talib Salman

Muthanna Journal of Administrative and Economics Sciences

The study investigates the relationship between risk-based audit quality, financial oversight efficiency and financial corruption reduction in banks listed on the Iraq Stock Exchange in the years 2008–2025 by applying International Professional Practices Framework (IPPF) 2024. A composite risk-based audit quality index was created which included auditor independence, auditor professional qualification, frequency of audit committee meetings, and auditor presence of a formal risk committee. Thirty three hundred annual reports were processed using AI-supported structured extraction with the Gemini API, resulting in an unbalanced final panel of 303 bank-year observations from 20 banks. Random Effects and some robustness and diagnostic tests …

Random Forest Algorithm Vs. Linear Regression For Financial Performance Determinants: Evidence From Iraqi Mixed-Sector Firms, Odaiy Obaid Zeidan, Mustafa Habib Mahdi

Journal of Economics and Administrative Sciences

This paper examines the predictive factors of financial performance among Iraqi mixed-sector firms, comparing the predictive power of traditional Ordinary Least Squares (OLS) regression with the Random Forest (RF) machine learning algorithm. Utilizing annual data from 11 firms over the period 2015–2020, the study measures financial performance using Return on Assets (ROA), Return on Equity (ROE), and Return on Sales (ROS). OLS regression identified the directional relationships and p-values, whereas Random Forest evaluated predictive accuracy and modeled nonlinear relationships. The OLS results indicate that firm size, liquidity, productivity, and cash-to-asset ratio have significant positive effects on financial performance, while the …

Deepfinsec: Advancing Online Financial Fraud Detection Via Structural Similarity-Based Graph Embedding, Diksha Sharma, Robin Prakash Mathur, Manmohan Sharma

Baghdad Science Journal

The frequent use of digital technology has significantly increased the use of online transactions. Most financial activities are conducted electronically. Financial fraud involves deceiving individuals or businesses for monetary gain. The proposed graph node embedding technique, FA-Struc2vec, is applied to the financial transaction network to extract graph node features based on their structural similarity and generate node embeddings. The node embeddings serve as input to DeepFinSec, which classifies transactions into desired classes. DeepFinSec is a proposed DL-based model that is a hybrid of CNN and LSTM. CNN processes transaction data and extracts local and high-level abstract features while LSTM captures …

A Study On Online Romance Fraud And National Security: A Ghanaian Perspective, Osmond B. Kabah

Doctoral Dissertations and Projects

One of the most prevalent cybercrimes is online romance fraud, where the perpetrators prey on individuals looking for love on various platforms. The crime, widespread on dating apps and social media, is typified by scammers starting a relationship and then defrauding the gullible victims of their money. Ghana is one of the countries whose citizens have been victims of these swindlers, and the national security apparatus is increasingly receiving reports of online financial victimization. This qualitative study aims to explore the issue in the country from a national security perspective. This study investigates what motivates the perpetrators of this crime …

Programmed To Please, Optimized For Obsession, Ela Ender

Journal of Business & Technology Law

No abstract provided.

Playing Pretend: Why Indigenous Identity Fraud Demands A Crime, Olivia Meier

UBC Law Review

No abstract provided.

Assessing The Financial Impact Of Spoofing Attacks In Michigan’S Finance Industry, Shatarsha Smith

Walden Dissertations and Doctoral Studies

Spoofing attacks have become an increasingly significant cybersecurity threat, exposing organizations and customers to financial losses by impersonating trusted identities in digital environments. Cybersecurity leaders need a better understanding of how information security maturity and the selection of cybersecurity strategy relate to spoofing loss outcomes. Grounded in game theory, the purpose of this quantitative cross-sectional correlational study was to examine the relationships among information security maturity, cybersecurity strategy selection, and spoofing loss outcomes in the financial industry. Data were collected from approximately 90 IT professionals employed in a Michigan-based government office in the public finance sector with at least 5 …

Denaturalization's Missing Limit, Cassandra Burke Robertson, Irina D. Manta

NULR Online

No abstract provided.

How Do Dual Tasks Affect Id Verification? A Mobile Eye-Tracking Analysis, Nathan Wieters

Masters Theses (Archived)

ID screening is a two-part process — the card and identity must be authenticated. Psychological research has typically focused on identity verification without considering card verification. To fill this gap, the present study had participants complete an ID verification task while wearing eye-tracking glasses to record gaze behavior during face-matching decisions. After 2 practice trials, a research assistant presented 48 physical ID cards with embedded facial images, one at a time, beside a computer monitor that presented a facial videos and calendar date. Participants were asked to decide if the ID card was valid on a 6-option scale from Definitely …

Artificial Intelligence (Ai) In Forensic Psychology: An Umbrella Review Of Potentials And Pitfalls, Ysabel Thereze Ang Guevarra, Nur Eva Alisha Binte Mohamed Hisham, Andree Hartanto

Research Collection School of Social Sciences

Artificial intelligence (AI) is becoming increasingly embedded within forensic psychological practice, shaping how criminal risk, legal responsibility and public safety are assessed. AI tools are now used in recidivism prediction, behavioural analysis, deception detection and investigative support, high-stakes domains where errors can have profound consequences. Despite this rapid adoption, the existing literature remains fragmented, with most reviews confined to narrow subdomains and offering limited integrated synthesis of AI′s broader role in forensic psychology. Thus, this umbrella review addresses this gap by synthesising findings from 43 reviews obtained from five major databases, namely EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus and Web …

Surveyception: An Exploration Of Deceptive Survey Forms, Muhammad Danish

Computer Science ETDs

Survey platforms such as Google Forms and Microsoft Forms are widely used for feedback, data collection, and engagement, but scammers increasingly exploit them to distribute phishing and deceptive attacks. This thesis presents a large-scale study of survey-form abuse across ten major providers. We collected 140,000 forms from three sources: public posts on X, search-engine results, and web pages from the top 10 million DomCop-ranked domains. Using automated filtering and manual qualitative review, we identified 2,645 forms requesting sensitive information and classified 566 as scams. These forms used techniques including phishing, private-secret theft, account and personal-data harvesting, financial deception, and psychological …

Selected California State University Cybersecurity Readiness Policies And Related Guidance For Staff: A Qualitative Document Analysis, Andrew Hung-Nguyen

Dissertations

This study examines the cybersecurity and data privacy policy guiding the staff in the following four California State University campuses: Long Beach, San Bernardino, Chico, and Northridge. The purpose is to evaluate how institutional policies communicate expectations for staff and support cybersecurity readiness. Using a qualitative document analysis approach, the study analyzes publicly available policy documents as primary data sources. The analysis is guided by three frameworks, as follows: Weidman’s framework (2018), which assesses the presence of key policy elements; Karakoç’s framework (2022), which evaluates the depth of policy content; and the Flesch Reading Ease and Flesch-Kincaid Grade Level formulas …

The Role Of Online Disinhibition On Social Media Users' Privacy Concerns And Behaviors, Lisa Thompson, Sinyong Choi

Faculty Articles

As social media platforms become central to digital interactions, concerns about both privacy and the disclosure of personal information have evolved. Online disinhibition—the psychological detachment users experience in digital spaces that distance their online behaviors from offline consequences—may shape perceptions of privacy risks, particularly on social media platforms like TikTok. This study examines the relationships between online disinhibition and users’ privacy concerns and protective behaviors while accounting for individuals’ sense of gratification, media awareness, identity, and experience. By exploring dimensions such as anonymity and invisibility, we investigate how these factors are linked to self-disclosure and diminished privacy caution. Findings from …

Usability Of Bitcoin As Currency In Türkiye: Fourier Shin Approach, İsmail Cem Özkurt, Deniz Özyakışır, Yunus Kutval

The Indonesian Capital Market Review

This paper seeks to assess the feasibility of utilizing Bitcoin as a currency within Türkiye. To achieve this, the research analyzes long-term cointegration relationships between Bitcoin and both the US Dollar and Euro, employing monthly data from November 2017 to February 2025 and utilizing the Fourier Shin cointegration test. The results of the cointegration tests, bolstered by Fourier series analysis, reveal significant long-term cointegration relationships between Bitcoin and both the USD and Euro. The DOLS analysis indicates that a 1% rise in Bitcoin leads to a 14% decrease in the USD price and a 17% increase in the Euro. These …

Method For Trust-Aware And Language Model–Driven Email Threat Detection And Mitigation, Niranjan M M

Defensive Publications Series

Email remains the primary communication channel for enterprises and continues to be the most exploited attack vector for cyber threats. Modern email attacks increasingly rely on impersonation, social engineering, and contextual manipulation rather than traditional malware or malicious links, allowing them to evade existing detection mechanisms. At the same time, email security systems are adopting large language models (LLMs) to improve intent detection and contextual analysis, introducing new risks where email content itself can manipulate or degrade automated reasoning.

The proposal introduces a secure, trust-aware, and LLM-safe framework for automated email threat detection and remediation. The proposed system integrates sender …

Impartial Intelligence? Evidence Of Country-Label Sensitivity In Ai Financial Analysis, Fabio Motoki, Jedson Pinto

School of Accountancy Faculty Publications

This study examines whether large language models exhibit systematic country-contingent differential treatment in financial fraud detection. Analyzing 30,000 synthetic transactions with identical statistical properties across three country attributions (United States, Great Britain, and China), we find LLMs assign significantly higher fraud probabilities to Chinese-attributed transactions (36.2%) compared to Western countries (≈30–31%), resulting in accuracy disparities of 67% versus 74%. The gap remains stable across five independent experimental replications and persists when using Chinese language prompts, ruling out linguistic effects. Bias mitigation strategies, such as requiring explanations or explicit country neutrality instructions, reduce but fail to eliminate these disparities. Testing across …

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present …

Static Analysis-Based Android Malware Detection (2021–2026): A Systematic Survey And Taxonomy, Ali Hussein Mohammed Ali, Musaab Riyadh Abdulrazzaq

Al-Esraa University College Journal for Engineering Sciences

Android malware is not only increasing in size and sophistication but is also a challenge to be detected on a large scale. Static analysis is popular due to its ability to detect malware without running applications or tracing run-time events. However, features, datasets, labeling methods, and assessment methodology differ, which makes studying performance difficult. This is a systematic study of 78 peer reviewed articles written between 2021 and 2026 regarding the Android malware detection by static analysis. In each of the studies, the current survey cover the following feature source, feature representation and engineering, learning paradigms, datasets and labeling methods, …

A Systematic Literature Review Of Fraud Research Prevalence, Mohammed Khojah, Nawaf Alzahrani, Saed Eidow, Jawad Alamri, Ibrahim Albassam, Muath Alghamdi, Aseel Atawi, Osama Bayunus, Osama Alhodaly, Osama Rabie

Journal of King Abdulaziz University: Computing and Information Technology Sciences

Background: Fraud is a pervasive worldwide problem that is evolving rapidly along with the technological advances and causing significant financial losses in a variety of industries. Traditional detection techniques often fall short in the face of more complex and digitalized fraud schemes. The pressing need for more intelligent detection systems is what motivated this review, which attempts to systematically assess the body of research on fraud detection and pinpoint dominant fraud sectors, types, and methods. Methods: This study employed a Systematic Literature Review (SLR) approach to comprehensively assess the current landscape of fraud detection research across multiple domains. …

Exploring Critical Limitations And The Sustainability Of Small- And Medium-Sized Enterprises, Uluocha Diamond Okojie

Walden Dissertations and Doctoral Studies

No abstract provided.

Neutrosemigroups Generated By Upside-Down Logic, Aykut Emniyet, Memet S¸Ahin

Neutrosophic Sets and Systems

No abstract provided.

Building Trustworthy Information Systems: A Unified Framework For Comparative Risk Detection, Parisa Momeni

USF Tampa Graduate Theses and Dissertations

Risk detection in large scale information systems increasingly depends on heterogeneous data generatedby both centralized and distributed ecosystems. While centralized systems provide curated and validated reports, distributed environments produce large-scale and real-time observational evidence. Existing computational approaches analyze these ecosystems in isolation, limiting systematic comparison of risk repre-sentations across heterogeneous sources.

This dissertation presents a unified computational framework for comparative risk detection across centralized and distributed information systems. The framework provides a domain independent methodology for transforming heterogeneous risk reporting data into comparable multidimensional representations. To enable interpretable comparison of heterogeneous risk distributions, this work introduces the Geometric Overlap Score …

Agentic Ai Approach For Online Financial Fraud Detection, Shadi Saleh, Kelechi Osuji, Wolfram Hardt

Al-Farahidi Expert Systems Journal

As digital payment systems facilitate billions of transactions every day, there is a high probability of fraudulent activities in these systems. Traditional fraud detection systems, including rule-based systems and machine learning-based systems, have three major limitations: lack of explainability in terms of regulatory requirements, lack of contextual reasoning in terms of rare behavioral patterns, and lack of interaction with human experts in fraud analysis. This paper proposes a novel agentic framework in fraud detection systems by integrating Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) in context-aware reasoning based on historical transaction evidence. The framework is designed as a system …

Extending Unibreak: Semantic Retrieval And Harmful-Intent Direction Suppression For Token-Level Llm Jailbreaking, Sanket Saha

Master’s Dissertations

Token-level adversarial perturbations remain one of the most efficient known attacks against the safety alignment of instruction-tuned large language models (LLMs). Among recent works, the UniBreak framework (You et al., 2026) stands out for unifying gradient-based optimization with an evolutionary perturbation repository. However, its repository relies solely on accumulated success frequency without utilizing query content, and its fitness function implicitly assumes that suppressing refusal tokens is sufficient to elicit harmful responses. In this dissertation, we extend UniBreak along both axes and re-evaluates the framework under stricter generalization and judgment protocols. Specifically, we introduce a semantic perturbation repository that replaces frequency-only …

Techniques For Preventing Anchoring Bias In Ai Investigative Analysis Through Staged Data Redaction And Hypothesis-Gated Revelation, Avneet Singh Chhabra

Defensive Publications Series

Presented herein is a system for preventing anchoring bias in large language model (LLM) investigative analysis. Bias in LLMs is not typically addressed by instruction-level approaches; rather, it is most effectively mitigated by physically separating the biasing data from an LLM's input during the hypothesis formation phase. The proposed system includes five core components: a Data Redaction Engine, a Staged Analysis Protocol, a Structural Gate, a Corrections Bridge, and a Procedural Attestation Record. The Data Redaction Engine operates to separate source data into a redacted evidence set and an attribution set, validated by a multi-layer validation pass. The Staged Analysis …

Evaluating Legal Frameworks For The Protection Of Consumers Of Digital Financial Services: Global Trends And Their Significance For Vietnam, Ha Son Nguyen, Lanh Dinh Cao, Nguyen Dinh Phan

International Journal on Consumer Law and Practice

The development of digital financial services (DFS) in the context of global digital transformation poses fundamental challenges for the legal framework for consumer protection, particularly in developing countries such as Vietnam. This article argues that the sectoral approach currently adopted in Vietnam, with rules dispersed across various specialized statutes, has created a structurally significant legal gap, a lack of consistency in protection standards, and limitations on effective coordination among regulatory authorities. It thereby undermines the law’s capacity to protect consumers in the highly cross-sectoral environment of digital finance.

Through an analytical and comparative approach that draws on the experiences of …

Adaptive Outlier Detection Over Data Stream, Rui Zhu, Mingyuan Jiang, Xiaochun Yang, Baihua Zheng, Bin Wang, Tao Qiu

Research Collection School Of Computing and Information Systems

Continuous distance-based outlier detection in streaming data poses significant challenges and has a wide range of practical applications. Traditional threshold-based methods perform well under stable streaming conditions, where fixed parameters remain effective. However, they often struggle with dynamic data distributions and high stream speeds, leading to suboptimal performance, limited control over the number of returned outliers, and failure to meet real-time detection requirements. To address these issues, this paper introduces a novel Recall and Proportion-Aware Outlier Detection (RPA-OD) query. In RPA-OD, ρ defines a distance relaxation that enables real-time outlier detection. Specifically, objects with fewer than k neighbors within the …

A Review On Credit Card Electronic Fraud Detection Methodologies, Titilayo Mary Sayikanmi, Ibrahim Adepoju Adeyanju, Bolaji Abigail Omodunbi

Mansoura Engineering Journal

Credit card fraud remains a critical and escalating challenge within the global financial ecosystem, driving substantial annual losses and necessitating the continuous evolution of detection methodologies. This paper presents a systematic literature review, conducted via the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, which comprehensively analyzes the state-of-the-art in electronic credit card fraud detection. Through a rigorous examination of 49 high-quality studies, this review maps the methodological evolution from traditional rulebased systems and statistical models to advanced artificial intelligence techniques, including machine learning, deep learning, and graph-based approaches. The analysis reveals that while individual methods possess distinct advantages …

Online Shopping Non-Adoption In South Africa: A Distrust-Centric Framework Integrating Social Exchange, Trust Transfer, And Social Presence Theories, Cleopatra Moipone Matli, Jeevarathnam P. Govender

Journal of Marketing and Consumer Behaviour in Emerging Markets

Online shopping non-adoption remains critically underexplored, especially in emerging markets. Addressing this gap, the authors develop and test a distrust-centric framework that applies the inhibitory logic of social exchange, trust transfer, and social presence theories to explain pre-emptive avoidance. Focusing on South Africa, where only 18.16% of the population engage in e-commerce, data was collected from 414 consumers who have never shopped online. Structural equation modelling reveals that perceived lack of reciprocity directly increases the intention of non-adoption. Moreover, distrust in online payment systems transfers to broader channel scepticism, which in turn drives avoidance. Perceived lack of social presence further …