Fraudsters Beware: Unleashing the Power of Metaverse Technology to Uncover Financial Fraud

Xu, Cheng; Liang, Xueji; Sun, Yanqi; He, Xudong

doi:10.1080/10447318.2023.2238367

Cited by 7 publications

(3 citation statements)

References 97 publications

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“…Beyond the typical individual rule assessment identi ed in the literature, the study [41] suggests applying concepts from coalitional game theory and the Shapley Value to evaluate a rule of collaborative performance, the study identi es the most important features of fraud detection. According to the study [9], game theory offers a framework for investigating the systems that provide nancial fraud detection in the metaverse setting, the study shows that using a metaverse environment can improve nancial analysts' ability to identify fraud, offering useful information for future collaboration tactics in corporate nance. The study does not examine other possible outcomes or measures of e cacy; instead, it concentrates on the accuracy of fraud detection as the dependent variable.…”

Section: Game Theory and Machine Learning In Fraud Detectionmentioning

confidence: 99%

“…Improving fraud detection will enable insights into fraud changing strategies to a wider spectrum of fraud risk across the banking and nance eld. The existing literature utilizes Ordinary Least Square (OLS) to address nancial fraud, but it is time-consuming and inadequate [7][8][9]. The literature suggests that OLS cannot handle complex and fraud-changing patterns, in response, machine learning techniques were employed [10][11][12] to solve the problem of nancial fraud, however, the studies achieved a high False Positive Rate (FPR).…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

USMAN,

Abdullahi,

Ran

et al. 2024

Preprint

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New bank account fraud is a significant problem causing financial losses in banking and finance. Existing statistical and machine-learning methods were used to detect fraud thereby preventing financial losses. However, most studies do not consider the dynamic behavior of fraudsters and often produce a high False Positive Rate (FPR). This study proposes the detection of new bank account fraud in the context of simultaneous game theory (SGT) with Neural Networks, the SGT involves two players, a fraudster, and bank officials attacking each other through Bayesian probability in a zero-sum. The influence of outliers within the SGT was tackled by adding a context feature for effective simulation of the dynamic behavior of fraudsters. The Neural Networks layer uses the simulated features for fraud context learning. The study is validated using Bank Account Fraud (BAF) Dataset on different machine-learning models. The Radial Basis Function Networks achieved FPR of 0.0% and 8.3% for fraud and non-fraud classes, respectively, while achieving True Positive Rate (TPR) of 91.7% and 100.0% for fraud and non-fraud classes, respectively. An improved Radial Basis Function Networks detect fraud by revealing fraudulent patterns and dynamic behaviors in higher dimensional data. The findings will enhance fraud detection and reduce customer attrition.

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Section: Game Theory and Machine Learning In Fraud Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

USMAN,

Abdullahi,

Ran

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Second, we introduce a gender perspective into human-computer interaction (HCI) (Xu et al, 2023), an aspect often omitted by HCI literature, revealing that males are more responsive towards transparency, while females are more responsive towards immutability.…”

mentioning

confidence: 99%

Deciphering trust mechanisms in blockchain platforms: A multifaceted experimental exploration

Xu,

Sun

2024

Manage Decis Econ

Self Cite

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Blockchain technology, celebrated for its transparency and immutability, has emerged as a transformative force across various industries. However, the nuanced mechanisms by which these attributes cultivate user trust remain enigmatic. This study embarks on an empirical odyssey to elucidate the multifaceted relationship between transparency, immutability, and user trust within blockchain platforms, incorporating a gender perspective to enrich the exploration. Through a pre‐registered experimental procedure involving diverse participants, we manipulate the visibility of transaction history and the immutability of records but also the complexity. Our results unveil that both transparency and immutability significantly amplify user trust, with distinct effects observed based on gender. Additionally, we uncover the underlying mediating variables that affect these relationships, providing a textured understanding of the dynamics at play. The findings extend the theoretical frontier in blockchain‐related trust research and furnish valuable insights for practitioners in the design and regulation of blockchain platforms, thus contributing to a more profound comprehension of this burgeoning technology.

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Social identity in trusting artificial intelligence agents: Evidence from lab and online experiments

Sun,

Xu,

2024

Manage Decis Econ

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This paper explores human trust in artificial intelligence (AI), focusing on the effects of social categorization (ingroup vs. outgroup) and AI human‐likeness through two pre‐registered studies involving 160 participants each. The first study, a lab experiment in China, and the second, an online experiment representative of the United States, both utilized a trust game to assess trust across four conditions: ingroup‐humanoid AI, ingroup‐non‐humanoid AI, outgroup‐humanoid AI, and outgroup‐non‐humanoid AI. Results indicated higher trust for ingroup and humanoid AIs, with statistical significance. Mixed‐design ANOVA was used to analyze the data, revealing significant main effects and interactions. The second study also identified an emotional connection as a mediator in trust, suggesting significant design implications for AI in trust‐critical sectors like healthcare and autonomous transportation.

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Fraudsters Beware: Unleashing the Power of Metaverse Technology to Uncover Financial Fraud

Cited by 7 publications

References 97 publications

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

Modeling the Dynamic Behaviors of Bank Account Fraudsters Using Combined Simultaneous Game Theory with Neural Networks

Deciphering trust mechanisms in blockchain platforms: A multifaceted experimental exploration

Social identity in trusting artificial intelligence agents: Evidence from lab and online experiments

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