A wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.

The construction industry is known for poor performance, low productivity, high waste generation, and for lagging in the adoption of new technology. A high rate of material wastage in construction projects has economic and environmental implications for concerned stakeholders. Construction waste quantification is an essential requirement for formulating waste management strategies. Technological advancements like building information modeling (BIM) and artificial intelligence (AI) provide effective solutions to the construction industry to deal with these prevalent issues. This literature review-based study observes the scarcity of research on the application of BIM in construction waste quantification. The limited number of studies found in the literature confirm the ability of BIM-aided waste quantification models to forecast waste generation. Moreover, the application of these models can also assist in reducing waste to a considerable extent. This study recommends that further studies should be conducted on technology-assisted waste quantification in building and infrastructure projects to evaluate their effectiveness for subsequent implementation in the industry.

Technologically advanced countries are accruing benefits from the adoption of Building Information Modelling (BIM) in the Architecture, Engineering, and Construction (AEC) industry after decades of struggling for productivity enhancement using innovation and automation. Most of the developing countries have not been able to embrace technology in the AEC Sector and, consequently, are unable to ameliorate prevalent performance-related issues in construction projects. This review article identifies hurdles to BIM implementation in selected developing countries via an examination of the latest studies. The most significant challenges observed in this study are Lack of Training for Professionals, Lack of Awareness, Huge Capital Cost, Resistance to Change, and Complexity of BIM Software. This study is an update on previous studies conducted with the aim of assisting the implementation of BIM in developing countries.

Highway construction projects are known for their propensity to consume enormous quantities of materials and their susceptibility to generate considerable waste. Adequate research has been conducted on identifying the causes of waste produced in building projects, however, there are limited studies available on identifying causative factors for highway projects. This study aims to identify and evaluate the causes and factors of waste generated on highway projects using a literature review and questionnaire survey technique. Causes leading to waste in highway projects were identified from the literature as well as from highway construction experts. Subsequently, quantitative data were collected from 127 highway construction professionals using a Likert Scale questionnaire survey, which was ranked using the Relative Importance Index (RII) and further analyzed by using a very robust Factor Analysis (FA) technique. RII results highlight the most significant causes of waste in highway construction, while FA suggests the main factors contributing to the waste in highway projects. The top five most significant causes of waste revealed by this study were: (1) mistakes of surveyors, (2) faulty drawings, (3) incompetence of quantity surveyors, (4) faulty/substandard work, and (5) poor workers’ skills, whereas the seven waste factor groups evaluated by the study were: (1) design, (2) storage, (3) survey, (4) workers, (5) waste management, (6) site management, and (7) external. This study further suggested waste management and mitigation strategies for highway construction projects corresponding to each factor group. This is a novel study on waste generation in highway projects in Pakistan and will assist academia and industry practitioners in understanding and controlling construction waste generation in highway projects during various stages of project execution.

The construction industry is struggling to resolve the issue of the enormous quantity of waste produced during construction processes, which impacts the performance and sustainability of projects. Causes of waste generation have been studied by researchers to formulate waste minimization strategies for these projects. The research on waste in highway infrastructure projects and waste causes specific to the roles and competencies of project team members is inadequate. This quantitative study addresses this gap by evaluating the influence of project managers (PMs) in minimizing CW through a structured questionnaire survey administered to 300 professionals, yielding 129 valid responses (43% response rate). The results indicate that 8.5% of construction materials are wasted in highway projects. Among four key project stakeholders (PM, quantity surveyor, designer, and client), PMs were rated as having the most significant impact on waste minimization (mean Likert score: 4.5/5). Using the Relative Importance Index (RII), the study identified the top five waste causes linked to PM competencies: faulty work requiring the work to be carried out again (RII = 0.742), wrong construction methods (0.734), lack of awareness (0.731), poor supervision (0.721), and poor material planning (0.706). A waste minimization framework is proposed, linking each of these causes to specific PM competencies and actionable strategies. These findings provide empirical support for targeting PM training and resource planning to reduce material waste in highway construction projects.