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This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.

This research aims to explore the potential of big data technology for sustainable management and investigate its impact on tourism. Its goal is to obtain meaningful results related to sustainable tourism to understand better how big data technology plays a role in decision-making by looking at it through the lens of various studies.

A systematic review, which is a qualitative method, was used in this study. The analysis was conducted using secondary data from the Web of Science Core Collections databases.

Big data technology has many economic benefits for businesses, but it also has managerial benefits such as forecasting, decision-making and tracking human and machine behaviour. Furthermore, big data technology offers sustainability benefits such as resource efficiency, preventive quality systems, carbon reduction and environmentally friendly production.

Big data's capabilities enable businesses to make more informed business decisions, improve overall business performance and contribute to achieving various SDGs. Big data, which aids in developing smart and sustainable tourism in the tourism sector, assists tourism managers in making economically, socially and environmentally sound decisions.

This chapter conceptualises a link between Industrial Revolution 4.0 (IR 4.0), big data, data science and sustainable tourism.

The author adopts a grounded theory and conceptual approach to endeavour in this exploratory research.

The outcome shows a significant rise of big data in the tourism sector under three major dimensions, i.e. business, governance and research. And, some exemplary evidence of institutions promoting the use of big data and data science for sustainable tourism has been discussed.

The conceptualised interlinkage of concepts like IR 4.0, big data, data science and sustainable development provides a valuable knowledge resource to policy-makers, researchers, businesses and students.

This chapter proposes a model for discovering suspicious financial operations such as money laundering. To achieve this, the authors reviewed research papers on money laundering and financial institutions’ cases and problems, especially those related to financial transfers. They also collected primary data through face-to-face semi-structured interviews with financial companies’ owners and experts in financial transfers to identify hypotheses that help discover suspicious transfers. The chapter discusses the six big data analysis cycle phases from problem discovery to model deployment to identify suspicious transfers. The chapter uses hypothetical data and models to discuss the results and focuses on exchange companies willing to analyze financial operations. The chapter proposes tools that exchange companies can use to monitor and prevent suspicious transfers including data visualization and machine learning algorithms.

With the ever-growing population in the urban areas, the concept of smart cities started to be more present in the literature. Smart cities are seen as a solution that will respond to the needs of providing a sustainable place for living, and at the same time improving residents’ lives. To achieve this, various information and communication technologies (ICTs) are exploited, making the digitalization in the modern world of an immense importance. Advanced digital technologies enable the transformation of existing and the creation of new business models, the development of new products and services, increase the efficiency and competitiveness of the economy, and contribute to wider socio-economic development. Digitization of society and the economy through innovative and intensive use of ICTs has great potential for growth and is the basis for further development and competitiveness. This all generates an enormous amounts of data sets from which useful information are generated and used again the decision support systems. This chapter presents two examples from Slovenia where big data is used for improving residents’ lives, as part of the strategies for smart cities.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).