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4 Papers

Advanced Supervised Machine Learning Algorithms in Credit Card Fraud Detection

Topics: Money, Credit and Banking; Financial Modeling; Financial Analysis; Financial Statistics

Authors: Simin Yu 1 , Victor Chang 1 , Gia Huỳnh 2 , Vitor Jesus 3 and Jiabin Luo 1

Affiliations: 1 Department of Business Analytics and Information Systems, Aston Business School, Aston University, Birmingham, U.K. , 2 Becamex Business School, Eastern International University, Binh Duong, Vietnam , 3 School of Computer Sci and Digital Tech, College of Engineering and Physical Sci, Aston University, Birmingham, U.K.

Digital Object Identifier: 10.5220/0013485400003956

Proceedings of FEMIB 2025, ISBN: 978-989-758-748-1

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The rapid growth of online transactions has increased convenience but also risks like money laundering, threatening financial systems. Financial institutions use machine learning to detect suspicious activities, but imbalanced datasets challenge algorithm performance. This study uses resampling techniques (SMOTE, ADASYN, Random Undersampling, NearMiss) and ensemble algorithms (XGBoost, CatBoost, Random Forest) on a simulated money laundering dataset provided by IBM (2023) to address this. Our findings reveal that each resampling technique offers unique advantages and trade-offs. CatBoost consistently outperforms XGBoost and Random Forest across sampling techniques, achieving the best balance between precision and recall while maintaining strong ROC curve scores. This strong performance could reduce the number of transactions banks must examine, as investigations would only focus on the predicted laundering cases.

Generative AI Risk Management in Digital Economy

Topics: Future Business Models, Trends and Development; Technologies for Finance, Economics, Management And/Or IT Business; Blockchain, Fintech and Mobile Payments; ICT for Business, Education, Healthcare, Finance and Other Sectors; Informatics and Digital Transformation; Impact of AI on Businesses

Authors: Victor Chang , Leigh Draper and Simin Yu

Affiliation: Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, U.K.

Digital Object Identifier: 10.5220/0012729800003717

Proceedings of FEMIB 2024, ISBN: 978-989-758-695-8

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In Healthcare Procurement, This Study Delves into the Integration of Generative AI, Focusing on Its Application within HealthTrust Europe’s marketing and communication frameworks. By analyzing the interplay between innovative AI-driven content personalization and the associated ethical, security, and operational risks, the research offers a nuanced perspective on leveraging technology for enhanced efficiency and engagement. The study employs qualitative research methods to assess risks and propose mitigation strategies, advocating for best practices in AI governance and risk management. It emphasizes the importance of maintaining network security, data integrity, and ethical standards in deploying AI solutions.

Exploring Risks in the Metaverse in an Immersive Digital Economy

Topics: Information Systems; Innovation; Business for IT-driven Services; Future Business Models, Trends and Development; Technologies for Finance, Economics, Management And/Or IT Business; Informatics and Digital Transformation; HCI and Computers Human Behavior; HCI and/or Technological Forecasting & Social Change

Authors: Victor Chang 1 , Sandra Strittmatter 1 , Vitor Jesus 1 , Lewis Golightly 2 , Pin Ni 3 and Karl Hall 2

Affiliations: 1 Aston University, Birmingham, U.K. , 2 Teesside University, Middlesbrough, U.K. , 3 University College London, London, U.K.

Digital Object Identifier: 10.5220/0011990900003494

Proceedings of FEMIB 2023, ISBN: 978-989-758-646-0

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The Metaverse is the next paradigm of computing and a combination of the words next and the universe. Immersive technology such as Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), and Extended Reality (XR) have been emerging at a fast pace in recent years. The Metaverse aims to solve the limited social dynamic of using the internet for communications. Since the Covid-19 pandemic, the internet has been used for education, work, and gaming in a more immersive method than ever before. However, the problem the Metaverse aims to solve is the social limitations of using technology such as Microsoft teams. In this paper, we address the opportunities of the Metaverse, such as online gaming and an enhanced 3D virtual community life, alongside the potential of cyber risk and security issues, such as data privacy and information security. We complement our paper with a risk assessment of the Metaverse, particularly focusing on risks associated with cybersecurity.

Forecasting of COVID-19 Pandemic Using ARIMA and Fb-Prophet Models: UK Case Study

Topics: Bioinformatics; Health Systems Complexity; Risk Analysis and Management; Time Series Prediction; Pandemic and COVID-19 Research

Authors: Victor Chang 1 , Oghara Akpomedaye 2 , Vitor Jesus 1 , Qianwen Xu 1 , Karl Hall 2 and Meghana Ganatra 2

Affiliations: 1 Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, U.K. , 2 Information Systems and AI Research Group, School of Computing and Digital Technologies, Teesside University, Middlesbrough, U.K.

Digital Object Identifier: 10.5220/0011990300003485

Proceedings of COMPLEXIS 2023, ISBN: 978-989-758-644-6

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This study aims to provide insights into predicting future cases of COVID-19 infection and rates of virus transmission in the UK by critically analyzing and visualizing historical COVID-19 data, so that healthcare providers can prepare ahead of time. In order to achieve this goal, the study invested in the existing studies and selected ARIMA and Fb-Prophet time series models as the methods to predict confirmed and death cases in the following year. In a comparison of both models using values of their evaluation metrics, root-mean-square error, mean absolute error and mean absolute percentage error show that ARIMA performs better than Fb-Prophet. The study also discusses the reasons for the dramatic spike in mortality and the large drop in deaths shown in the results, contributing to the literature on health analytics and COVID-19 by validating the results of related studies.

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