Financial fraud has expanded rapidly with the growth of the digital economy, evolving from conventional transactional misconduct to more complex and data-intensive forms. Traditional rule-based detection methods are increasingly inadequate for addres...
With the growth of e-commerce platforms, the number of fraud reviews has rapidly increased, making fraud detection for platform data critically important. To detect fraud reviews in platform data, recent approaches have leveraged graph neural network...
Financial fraud represents a critical global challenge with substantial economic and social consequences. This comprehensive review synthesizes the current knowledge on machine learning approaches for financial fraud detection, examining their effect...
Fraud detection in large-scale graphs presents significant challenges, especially in heterophilic graphs where linked nodes often belong to dissimilar classes or exhibit contrasting attributes. These asymmetric interactions, combined with class imbal...
1 January 2020
Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day. Shill bidding is the predominant form of auction fraud, and it is also the most difficult to detect because it so close...
Debit and credit card payments have become the preferred method of payment for consumers, replacing paper checks and cash. However, this shift has also led to an increase in concerns regarding identity theft and payment security. To address these cha...
In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial en...
Specialty literature and solutions in the market have been focusing in the last decade on collecting and aggregating significant amounts of data about transactions (and user behavior) and on refining the algorithms used to identify fraud. At the same...
As the focus of capital market supervision, financial report fraud has shown a development trend of enormous numbers, complex transactions, and hidden means in recent years. To improve audit efficiency and reduce the dependence on non-financial data,...
Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and t...
Detecting tax fraud is a top objective for practically all tax agencies in order to maximize revenues and maintain a high level of compliance. Data mining, machine learning, and other approaches such as traditional random auditing have been used in m...
With the growing advantages and conveniences provided by digital transactions, the financial sectors also face a loss of billions of dollars each year. While the use of credit cards has made life easier and convenient, it has also become a significan...
Credit card transactions may contain some categorical attributes with large domains, involving up to hundreds of possible values, also known as high-cardinality attributes. The inclusion of such attributes makes analysis harder, due to results with p...
Energy fraud detection bears significantly on urban ecology. Reduced losses and power consumption would affect carbon dioxide emissions and reduce thermal pollution. Fraud detection also provides another layer of urban socio-economic correlation heat...
Online shopping, already on a steady rise, was propelled even further with the advent of the COVID-19 pandemic. Of course, credit cards are a dominant way of doing business online. The credit card fraud detection problem has become relevant more than...
Credit card fraud detection is a critical research area due to the significant financial losses and security risks associated with fraudulent activities. This study presents FraudX AI, an ensemble-based framework addressing the challenges in fraud de...
Fraud detection within transaction data is crucial for maintaining financial security, especially in the era of big data. This paper introduces a novel fraud detection method that utilizes quantum computing to implement community detection in transac...
Bank transaction fraud detection has emerged as an important area of research in the economic sector, driven by the developing sophistication of fraudulent activities and the considerable economic losses they entail. This paper reviews numerous metho...
Rising financial fraud impacts industries, economies, and consumers, creating a need for advanced technological solutions. Compliance frameworks help detect and prevent illicit activities like money laundering, market manipulation, etc. However, with...
Effective financial fraud detection requires systems that can interpret complex transaction semantics while dynamically adapting to asymmetric operational costs. We propose a hybrid framework in which a large language model (LLM) serves as an encoder...
The surge in credit fraud incidents poses a critical threat to financial systems, driving the need for robust and adaptive fraud detection solutions. While various predictive models have been developed, existing approaches often struggle with two per...
Despite the recognized importance of ethical idealism in enhancing fraud detection in the audit context, there remains limited understanding of the mediating role of conscientiousness in the relationship between auditors’ ethical idealism and f...
The increasing cost of energy and the prevalence of electricity theft pose significant financial and operational challenges for energy providers. Traditional fraud detection methods often fail to identify sophisticated unauthorized consumption, parti...
Currently, fraud detection is employed in numerous domains, including banking, finance, insurance, government organizations, law enforcement, and so on. The amount of fraud attempts has recently grown significantly, making fraud detection critical wh...
Credit card fraud remains a major cause of financial loss around the world. Traditional fraud detection methods that rely on supervised learning often struggle because fraudulent transactions are rare compared to legitimate ones, leading to imbalance...
In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, block...
The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To...
Wangiri fraud is a pervasive telecommunications scam that exploits missed calls to lure victims into dialing premium-rate numbers, resulting in significant financial losses for operators and consumers. This paper presents a comprehensive machine lear...
Online advertising is a marketing approach that uses numerous online channels to target potential customers for businesses, brands, and organizations. One of the most serious threats in today’s marketing industry is the widespread attack known...
Credit card fraud detection remains a critical challenge for financial institutions, particularly due to extreme class imbalance and the continuously evolving nature of fraudulent behavior. This study investigates two complementary approaches: anomal...
In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble...
Although the most widely studied datasets in fraud-detection systems belong to the banking sector, the aviation industry is susceptible to fraud activities that seriously harm airline companies. Therefore, big airline companies have started to purcha...
Fraud in financial services—especially account opening fraud—poses major operational and reputational risks. Static rules struggle to adapt to evolving tactics, missing novel patterns and generating excessive false positives. Machine lear...
The study utilized Multiple Linear Regression along with advanced classification algorithms such as Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, to detect financial statement fraud. M...
Detecting fraudulent users in social networks could reduce online fraud and telecommunication fraud cases, which is essential to protect the lives and properties of internet users and maintain social harmony and stability. We study how to detect frau...
Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhanc...
The rapid growth in e-commerce has resulted in an increasing number of people shopping online. These shoppers depend on credit cards as a payment method or use mobile wallets to pay for their purchases. Thus, credit cards have become the main payment...
In recent years, with the rapid development of technology and the economy, online transaction fraud has become more and more frequent. In the face of massive records of online transaction data, manual detection methods are long outdated, and machine...
10 February 2025
The relationships within a supply chain are crucial for analyzing business transactions and can reveal significant patterns in disclosed financial data. These relationships also aid in the assessment and detection of financial fraud. Recent studies e...
Mobile payments and e-commerce, along with credit cards, have become deeply embedded in people’s daily business and economic activities. However, the increase in transaction volume has also led to a rise in fraudulent activities. To address cha...
The increasing sophistication of fraud tactics necessitates advanced detection methods to protect financial assets and maintain system integrity. Various approaches based on artificial intelligence have been proposed to identify fraudulent activities...
The increasing sophistication of fraud has rendered rule-based fraud detection obsolete, exposing banks to greater financial risk, reputational damage, and regulatory penalties. Financial stability, customer trust, and compliance are increasingly thr...
Financial technology, or Fintech, represents an emerging industry on the global market. With online transactions on the rise, the use of IT for automation of financial services is of increasing importance. Fintech enables institutions to deliver serv...
In recent years, financial statement fraud schemes have evolved to become markedly more sophisticated and concealed, thereby posing severe threats to both social stability and economic health. Traditional detection methods, which rely primarily on fr...
In fraud detection, centralized approaches often face challenges related to data protection, security, and potential data breaches. Such methods require sensitive healthcare and insurance data to be pooled in one location, which increases vulnerabili...
This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The...
Detecting financial fraud is a critical aspect of modern intelligent financial systems. Despite the advances brought by deep learning in predictive accuracy, challenges persist—particularly in capturing complex, high-dimensional nonlinear featu...
Credit card fraud remains a significant concern for financial institutions due to its low prevalence, evolving tactics, and the operational demand for timely, accurate detection. Machine learning (ML) has emerged as a core approach, capable of proces...
The proliferation of internet services in various industries, especially the financial sector, has increased financial fraud. Fraud detection and prevention are critical to protecting both individuals and organizations from significant financial loss...
Transaction Fraud, a type of financial operational risk, remains a major threat to financial sectors and continuously imposes devastating financial impacts. This study comprehensively reviews 41 cutting-edge publications on financial transaction frau...
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