Digital Commons Network^™

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 …

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. …

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 …

Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar

Theses

The paper explores how statistical analysis and machine learning can be used to identify the fraud patterns in the police reports. The study aims at establishing the most important predictive factors and indicators distinguishing fraudulent and valid cases with the use of structured data of police databases. The work was done in the background of the increase in financial fraud instances and the rising necessity of the introduction of automated detection systems in police departments. Police reports of the pastwere mined down to data and analyzed on SPSS 1, to carry out statistical operations. The sample was structured data which …

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, …

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 …

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

Balancergnn: Balancer Graph Neural Network For Imbalanced Datasets, Mallika Boyapati

Dissertations

Addressing imbalanced datasets is challenging due to machine learning models' inclination to learn the majority class. Graph construction plays a major role in determining how Graph Neural Networks (GNNs) perform on imbalanced datasets. In this research, we introduce the BalancerGNN framework to tackle highly imbalanced datasets, demonstrating its effectiveness in fraud detection as one of the case studies. This framework is designed to work for any binary node classification dataset with significant class imbalances. This research addresses the following questions: i) How effective are feature engineering techniques in the case of imbalanced datasets? ii) How do graph representation learning and …

Smart Credit Card Fraud Detection Using Machine Learning, Aisha Bin Sulaiman, Rowdha Masood Falaknaz

Theses

This thesis addresses the increasing issue of fraud resulting from technological advancements affecting both customers and fraudsters, while law enforcement agencies face challenges of inadequate case prioritization; data overload and slow investigative analysis. The central objective of this thesis revolves around strategies to counter these challenges, emphasizing the urgency of rapid response to major alert cases that can lead to widespread impacts. The authors' experience in financial fraud detection and credit card crime investigation within the police department has inspired this research, guiding the approach toward practical solutions for everyday operational challenges. The significance of this thesis lies in its …

Data Science In Finance: Challenges And Opportunities, Xianrong Zheng, Elizabeth Gildea, Sheng Chai, Tongxiao Zhang, Shuxi Wang

Information Technology & Decision Sciences Faculty Publications

Data science has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Data science in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying data science to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is …

Credit Card Fraud Detection Using Machine Learning, Meera Alemad

Theses

The purpose of this project is to detect the fraudulent transactions made by credit cards by the use of machine learning techniques, to stop fraudsters from the unauthorized usage of customers’ accounts. The increase of credit card fraud is growing rapidly worldwide, which is the reason actions should be taken to stop fraudsters. Putting a limit for those actions would have a positive impact on the customers as their money would be recovered and retrieved back into their accounts and they won’t be charged for items or services that were not purchased by them which is the main goal of …

Integrating Machine Learning Algorithms With Quantum Annealing Solvers For Online Fraud Detection, Haibo Wang, Wendy Wang, Yi Liu, Bahram Alidaee

Faculty and Student Publications

Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems detect duplicitous activities after they have already occurred, not at or near real time. Since spurious transactions are far fewer than the normal ones, the highly imbalanced data makes fraud detection very challenging and calls for ways to address it beyond the traditional machine learning approach. This study has proposed a detection framework, and implemented it using quantum machine learning (QML) approach by applying Support Vector Machine (SVM) enhanced with quantum annealing solvers. To evaluate its detection performance, we have further implemented twelve machine learning …

Leveraging Maching Learning In Financial Fraud Forensics In The Age Of Cybersecurity, Md. Ariful Haque, Sachin Shetty

VMASC Publications

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source …

Fraud Detection Using Data Analytics, Ayesha Karmustaji

Theses

At present, the biggest concern of every organization is to detect and control financial fraud. Tax frauds cause the loss of billions of dollars every year. As a result, data mining techniques are used to combat the growing problem of tax fraud. Tax evasions cause a reduction in revenue collection. It also has a bleak impact on government policies and budget. The goal of this study is to describe the use of data analytics tools to process and analyze tax data related to value-added tax evasions. This study is a conceptual perspective that provides a theoretical and methodological basis for …

Can Generative Adversarial Networks Help Us Fight Financial Fraud?, Sean Mciver

Dissertations

Transactional fraud datasets exhibit extreme class imbalance. Learners cannot make accurate generalizations without sufficient data. Researchers can account for imbalance at the data level, algorithmic level or both. This paper focuses on techniques at the data level. We evaluate the evidence of the optimal technique and potential enhancements. Global fraud losses totalled more than 80 % of the UK’s GDP in 2019. The improvement of preprocessing is inherently valuable in fighting these losses. Synthetic minority oversampling technique (SMOTE) and extensions of SMOTE are currently the most common preprocessing strategies. SMOTE oversamples the minority classes by randomly generating a point between …

Detecting Credit Card Fraud: An Analysis Of Fraud Detection Techniques, William Lovo

Senior Honors Projects, 2020-current

Advancements in the modern age have brought many conveniences, one of those being credit cards. Providing an individual the ability to hold their entire purchasing power in the form of pocket-sized plastic cards have made credit cards the preferred method to complete financial transactions. However, these systems are not infallible and may provide criminals and other bad actors the opportunity to abuse them. Financial institutions and their customers lose billions of dollars every year to credit card fraud. To combat this issue, fraud detection systems are deployed to discover fraudulent activity after they have occurred. Such systems rely on advanced …

The Auditor's Responsibilities For Fraud Detection And Disclosure: Do The Auditing Standards Provide A Safe Harbor?, James L. Costello

Maine Law Review

Eighty-seven percent of managers recently surveyed were willing to commit financial statement fraud. More than half were willing to overstate assets, forty-eight percent were willing to understate loss reserves and thirty-eight percent would "pad" a government contract. These disturbing results are underscored by the financial miseries still brewing in the savings and loan industry, as well as by other corporate and banking financial debacles of the past decade, including Lincoln Savings & Loan, Wedtech, and the Delorean sports car venture scandal. Amidst these financial ruins we find the chronic element of management fraud. Unfortunately for investors and depositors a troublesome …

Fraud Detections For Online Businesses: A Perspective From Blockchain Technology, Yuanfeng Cai, Dan Zhu

Publications and Research

Background: The reputation system has been designed as an effective mechanism to reduce risks associated with online shopping for customers. However, it is vulnerable to rating fraud. Some raters may inject unfairly high or low ratings to the system so as to promote their own products or demote their competitors.

Method: This study explores the rating fraud by differentiating the subjective fraud from objective fraud. Then it discusses the effectiveness of blockchain technology in objective fraud and its limitation in subjective fraud, especially the rating fraud. Lastly, it systematically analyzes the robustness of blockchain-based reputation systems in each type of …

Automating Vendor Fraud Detection In Enterprise Systems, Kishore Singh, Peter Best, Joseph Mula

Journal of Digital Forensics, Security and Law

Fraud is a multi-billion dollar industry that continues to grow annually. Many organizations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper, we adopt a DesignScience methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated by developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of …

Continuous Fraud Detection In Enterprise Systems Through Audit Trail Analysis, Peter J. Best, Pall Rikhardsson, Mark Toleman

Journal of Digital Forensics, Security and Law

Enterprise systems, real time recording and real time reporting pose new and significant challenges to the accounting and auditing professions. This includes developing methods and tools for continuous assurance and fraud detection. In this paper we propose a methodology for continuous fraud detection that exploits security audit logs, changes in master records and accounting audit trails in enterprise systems. The steps in this process are: (1) threat monitoringsurveillance of security audit logs for ‘red flags’, (2) automated extraction and analysis of data from audit trails, and (3) using forensic investigation techniques to determine whether a fraud has actually occurred. We …