Promoting Public Integrity and Combating Financial Crime: Challenges on the Pathway to Sustainable Development

Перший розділ монографії розкриває сучасні тенденції поширення незаконних фінансових потоків, види протиправних діянь у контексті досягнення цілей сталого розвитку та вплив цифрових технологій на поширення фінансових злочинів. У другій частині монографії визначено та формалізовано імпульси для активізації фінансових злочинів, спричинених цифровізацією економіки, та оцінено вплив розвитку ринку криптовалют на розвиток фінансового шахрайства. Третій розділ монографії присвячено боротьбі з корупцією та сприянню належному врядуванню в кліматичних діях. Вперше розроблено методологію оцінки ймовірнісного впливу корупції в кліматичному фінансуванні на досягнення нульових викидів.The first section of the monograph reveals current trends in the spread of illicit financial flows, types of illegal acts in the context of achieving sustainable development goals and the impact of digital technologies on the spread of financial crimes. The second part of the monograph identifies and formalises the impulses for the intensification of financial crimes caused by the digitalisation of the economy and assesses the impact of the development of the cryptocurrency market on the development of financial fraud. The third section of the monograph is devoted to fighting corruption and promoting good governance in climate action. For the first time, a methodology has been developed to assess the probabilistic impact of corruption in climate finance on achieving zero emissions

Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges

Federated learning plays an important role in the process of smart cities. With the development of big data and artificial intelligence, there is a problem of data privacy protection in this process. Federated learning is capable of solving this problem. This paper starts with the current developments of federated learning and its applications in various fields. We conduct a comprehensive investigation. This paper summarize the latest research on the application of federated learning in various fields of smart cities. In-depth understanding of the current development of federated learning from the Internet of Things, transportation, communications, finance, medical and other fields. Before that, we introduce the background, definition and key technologies of federated learning. Further more, we review the key technologies and the latest results. Finally, we discuss the future applications and research directions of federated learning in smart cities

Money laundering and financial crimes in Dubai: a critical study of strategies and future direction of control

Preventing money laundering is a major international problem. Several attempts, from the national to the international level, have been made to address and prevent money laundering. These are often frustrated by the dynamic nature of the crime itself. However, regardless of its reach and dynamism in illegal or legal transactions, which are often intertwined, individual nations need to address the issue of money laundering to signify to an international audience and legitimate commercial interests their intent to tackle money laundering and thus illustrate that public and private state run organisations in the financial and law enforcement sectors are honest and professional, and that their country is a ‘place to do business’. This thesis, therefore, presents an evaluation of the strategies and future directions of money laundering in Dubai, as it is a ‘new’, dynamic place in which to conduct business and the financial centre of the Middle East. It examines the various ways in which legislation and law enforcement in Dubai are struggling with and tackling the issues and problems of money laundering in the face of organised crime and terrorism. In this thesis, the concepts of money laundering and financial crimes in Dubai, with a special focus on strategies as well as future direction of control, are explored in some depth. This work has established that Dubai has a substantial anti-money laundering framework; however, it suffers from some weaknesses. These weaknesses are caused by the poor relationship between anti-money laundering units, the Anti-Organised Crime Department of the Dubai police, the financial sector and the Central Bank of Dubai. This situation is particularly evident when it comes to sharing information on those suspected of money laundering in Dubai. The ‘lack of a relationship’ is illustrated by primary research, as is the fact that other nations have (i.e. the UK) developed a more intelligence-led approach and partnerships in their quest to prevent money laundering where possible in their jurisdiction. This thesis highlights the progress that is needed in Dubai and the UAE to prevent money laundering, and as such is an original contribution to knowledge in an under-researched field in the Middle East

The Multifaceted Relationship Between AI and Economics: Impacts, Challenges, and Insights

Artificial intelligence (AI) has the potential to enhance decision-making by offering precise and timely information to businesses and policymakers. This study delves into the intricate relationship between AI and economics, with a specific focus on three key domains: Supply Chain Optimization, Financial Fraud Detection, and Automation's Impact on the Workforce. By shedding light on both the advantages and challenges of AI integration in economics, this research aims to contribute to the ongoing discussion. The research objectives encompass exploring AI's influence on the multifaceted relationship with economics, offering valuable insights for policymakers, industry stakeholders, and researchers

Artificial Intelligence in Banking Industry: A Review on Fraud Detection, Credit Management, and Document Processing

AI is likely to alter the banking industry during the next several years. It is progressively being utilized by banks for analyzing and executing credit applications and examining vast volumes of data. This helps to avoid fraud and enables resource-heavy, repetitive procedures and client operations to be automated without any sacrifice in quality. This study reviews how the three most promising AI applications can make the banking sector robust and efficient. Specifically, we review AI fraud detection and prevention, AI credit management, and intelligent document processing. Since the majority of transactions have become digital, there is a great need for enhanced fraud detection algorithms and fraud prevention systems in banking. We argued that the conventional strategy for identifying bank fraud may be inadequate to combat complex fraudulent activity. Instead, artificial intelligence algorithms might be very useful.  Credit management is time-consuming and expensive in terms of resources. Furthermore, because of the number of phases involved, these processes need a significant amount of work involving many laborious tasks. Banks can assess new clients for credit services, calculate loan amounts and pricing, and decrease the risk of fraud by using strong AA/ML models to assess these large and varied data sets in real-time. Documents perform critical functions in the financial system and have a substantial influence on day-to-day operations. Currently, a large percentage of this data is preserved in email messages, online forms, PDFs, scanned images, and other digital formats. Using such a massive dataset is a difficult undertaking for any bank. We discuss how the artificial intelligence techniques that automatically pull critical data from all documents received by the bank, regardless of format, and feed it to the bank's existing portals/systems while maintaining consistency