This study aims to explore the application of machine learning (ML) in enhancing cybersecurity measures, focusing specifically on intrusion detection, malware detection, fraud detection, and anomaly detection systems. A systematic review of 743 scholarly papers was conducted, from which 115 were selected for in-depth analysis. The review process involved evaluating advancements in ML techniques within the context of cybersecurity. The findings reveal that ML-driven systems significantly enhance the automation of security processes, improve the recognition of novel threats, and reduce human error in cyber threat management. However, challenges such as adversarial attacks and the need for high-quality model training pose significant barriers to the broader adoption of ML in cybersecurity. The study discusses the implications of these findings, emphasizing the necessity for developing robust and adaptive ML models that can withstand adversarial threats while improving integration across various cybersecurity applications. The insights gained from this research provide a comprehensive overview of the potential benefits and challenges of implementing ML in cybersecurity, highlighting the need for continuous innovation in threat detection mechanisms. This review acknowledges limitations such as the predominance of literature from Western contexts, potentially overlooking insights from other regions. Furthermore, the complexity of implementing ML systems in dynamic cyber environments remains a critical challenge. Future research should concentrate on refining ML algorithms to enhance resilience against adversarial threats, exploring the integration of emerging technologies for improved cybersecurity, and addressing gaps in the existing literature regarding the life cycle of ML models in real-world applications.

In today's digital age, the swift advancement of information and communication technology (ICT) has brought immense benefits to humanity, but it has also opened the door to various technological crimes, particularly cybercrime, which is becoming an increasingly significant issue in Bangladesh. This paper aspires to critically examine the nature and scope of cybercrime in Bangladesh, exploring its impact on individuals, businesses, and the economy as a whole. This study investigates the state of cybercrime in Bangladesh, its sector-wide impacts, and the effectiveness of existing legal government law in combating these crimes. a survey alongside semi-structured interviews and a variety of sources, including Scopus, Web of Science, DOAJ, Scimago, and Google Scholar databases. The study also utilized PRISMA based methodology to find out the research gaps in the context to increase deeper insights and understanding into technology-related crimes. Cybercrime (hacking, phishing and credit Card Fraud, financial fraud etc) greatly affected economic growth, emerging trends, different sectors, banking, finance and legal frameworks which are emerging economies that have the mediation process of expanding factors in Bangladesh. Additionally, financially motivated attackers, politically motivated attackers and espionage motivated attackers are the main reasons for cybercrime. As Bangladesh rapidly adopts digital technologies, the study identifies the emerging factors, patterns, underlying logic, the role of law enforcement agencies and cybersecurity measures related to cybercrime. The implication will be presented in the increasingly alarming consequences of cybercrime and suggests strengthening existing global cyber laws to more effectively fight these crimes.

This study explores financial crime and money laundering trends through a bibliometric analysis using Scopus and Web of Science databases. The primary objective is to assess research themes, scholarly contributions, and emerging topics in financial crime and money laundering, particularly those involving cryptocurrency. The methodology involves a comprehensive data collection process, with 1280 articles screened using inclusion and exclusion criteria, narrowing to 190 articles for in-depth analysis. Tools like VOSviewer, MS Excel, and Biblioshiny R package were employed for bibliometric coupling, keyword co-occurrence, and thematic clustering. Findings reveal the central role of cryptocurrency in financial crimes, with specific attention to techniques like fraud detection, cybercrime, and anti-money laundering. It highlights the rapid increase in publications since 2017, attributed to cryptocurrency adoption, regulatory developments, and the prevalence of financial crime. The USA, UK, and Australia emerged as major contributors, with the USA displaying the highest collaborative research efforts. Practical implications are significant for policymakers, offering insights to enhance digital transfer security and inform measures against money laundering risks. The study's social implications underline the need for robust cybersecurity and forensic measures to combat online financial crimes effectively. Limitations include its reliance on English-language documents and focus on specific publication databases, and generalizability. Future research directions suggest further investigation into cryptocurrency’s psychological impacts, consumer trust, and geographical distribution of fraud activities. This study's originality lies in its bibliometric approach, combining quantitative and qualitative insights, filling research gaps on emerging digital financial crime trends, especially related to cryptocurrency and global collaborative research.

This study explores the integration of emerging technologies into financial systems and their impact on financial crimes. The research aims to identify and categorize financial crimes, cyber breaches, and the role of digital evidence in financial technologies using the PRISMA technique. A systematic review was conducted, analyzing literature from multiple databases such as PubMed, Scopus, Web of Science, and Google Scholar, focusing on studies published between June 2021 and June 2023. Key terms, including "technology-based financial crime," "cybercrime," "financial fraud," and others, were incorporated to refine the search. After rigorous screening, 175 studies (150 papers and 25 reports) were selected for analysis. The study highlights the growing concern over the use of emerging technologies in facilitating financial crimes such as money laundering, tax evasion, and cyber fraud. It also examines the evolving nature of financial crimes due to technological advancements and the challenges of investigating and prosecuting such crimes. Further research is recommended to investigate the socio-economic factors influencing financial crime and to develop effective global collaboration mechanisms for addressing transnational financial offenses. This study contributes to a deeper understanding of how business entities and individuals handle financial misconduct in the digital era.

The study aspires to examine the relationship between financial technology (FinTech) and financial inclusion (FI) on sustainable performance, focusing on global insights. FinTech has transformed the financial landscape by promoting FI, especially in marginalized groups. Through a systematic literature review (SLR), this study analyzes bibliographic data from Scopus, Web of Science (WOS), and Google Scholar. A keyword-based search yielded 1944 articles, from which 371 were selected after a rigorous screening process. Findings highlight that FinTech and sustainable finance positively affect corporate financial performance, especially in the services, while FI uniquely contributes to sustained corporate growth. Our findings suggest a future direction for how FI derives sustainable performance by overcoming financial obstacles. Evidence shows that FinTech and inclusive finance practices can reduce costs, improve capital access, uplift brand value, and support environmental compliance. The paper recommends policies for effective risk-minimized FinTech use and encourages further quantitative and qualitative research in this field.

This study investigates the mathematical structure of Hilbert spaces, defined as complete inner product spaces, and their significance in both theoretical and applied contexts. We begin by exploring their foundational properties, including inner products, orthogonality, and completeness, which extend Euclidean geometric concepts to infinite-dimensional settings. Key mathematical tools, including the Cauchy–Schwarz inequality, triangle inequality, polarization identity, and Apollonius identity, are analyzed to highlight the analytical framework of Hilbert spaces and their relationship to normed spaces and Banach spaces. Then we examine practical applications in quantum mechanics, signal processing, and machine learning, where the inner product structure enables techniques like kernel methods, Support Vector Machines, and Principal Component Analysis. We provide MATLAB-based visualizations are provided, illustrating concepts such as projections and orthonormal expansions in computational contexts. This work integrates rigorous mathematical analysis with practical demonstrations, offering valuable insights for students and researchers in mathematics and data science.