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This study aims to evaluate the suspicious transaction reporting (STR) as a financial intelligence tool to identify the potential strengths and limitations of STR and to come up with the criteria, which will make this tool an effective one in early detection of terrorist financing activities.

Considering the research aim, this research uses the funnelling method for identifying effectiveness criteria. Funnelling is a method of literature review that helps find pertinent literature by refining the search through filtering the available research (Ridley, 2008). Using this method, the researcher first applied the criteria of actionable intelligence to filter the financial intelligence tools to select the most promising and important tool (suspicious transaction reporting) for early detection of terrorist financing activities. The funnelling method was also applied to derive the effectiveness criteria from the operational features, and corresponding limitations, of the suspicious transaction reporting system. The funnelling method was also used to identify those operational features and limitations of suspicious transaction reporting that have the most direct relevance to the early detection problem of suspicious transaction reporting.

There are some operational features of STR that give rise to certain limitations that undermine its effectiveness in terms of early detection of terrorist financing activities. The limitations of STR necessitate a search for criteria that will make STR effective in early detection of terrorist financing activities. Based on the operational features and their corresponding limitations, effectiveness criteria for STR have been derived in this study. It is shown how these effectiveness criteria can remove the limitations of STR.

The list of operational features and the corresponding limitations based on which the effectiveness criteria have been derived may not be exhaustive. There may have other operational features, and corresponding limitations that also make STR largely ineffective in the early detection of terrorist financing activities, and for which more effectiveness criteria should also be derived.

The limitations and the effectiveness criteria will pave the way for redesigning STR in such a way that will make it highly useful for detecting financing activities relating to imminent terrorist attacks.

The society will experience fewer terrorist attacks that will make the society peaceful, happy and vibrant.

In this study, the effectiveness criteria of STR for early detection of terrorist financing activities have been derived in an innovative way by deducing them from the operational features of STR and the corresponding limitations.

The purpose of this study is to explore and use artificial intelligence (AI) techniques for identifying the relevant attributes necessary to file a suspicious activity report (SAR) using historical customer transactions. This method is known as predictive modeling, a statistical approach which uses machine learning algorithm to predict outcomes by using historical data. The models are applied to a modified data set designed to mimic transactions of retail banking within the USA.

Machine learning classifiers, as a subset of AI, are trained using transactions that meet or exceed the minimum threshold amount that could generate an alert and report a SAR to the government authorities. The predictive models are developed to use customer transactional data to predict the probability that a transaction is reportable.

The performance of the machine learning classifiers is determined in terms of accuracy, misclassification, true positive rate, false positive rate and false negative rate. The decision tree model provided insight in terms of the attributes relevant for SAR filing based on the rule-based criteria of the algorithm.

This research is part of emerging studies in the field of compliance where AI/machine learning technology is used for transaction monitoring to identify relevant attributes for suspicious activity reporting. The research methodology may be replicated by other researchers, Bank Secrecy Act/anti-money laundering (BSA/AML) officers and model validation analysts for BSA/AML compliance models.

The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).

The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.

Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.

Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.

MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.

The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.

Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.

Purpose: Analysis and systematisation of global trends in the transformation of DSSs from the standpoint of solving their global and local problems and determining the central axioms of setting up and supporting business processes in DSSs.

Need of the Study: Decision Support Systems (DSSs) are the basis of doing business in an enterprise by automating business processes, keeping accounting and reducing various risks associated with complexity, labour-intensiveness, slow execution time and, therefore, potential loss of profit. In recent decades, the rapid development of DSSs has led to the emergence of complex enterprise information system architectures. At the same time, many local business processes are not implemented or are partially implemented. In Ukraine, such techniques include VAT accounting.

Methodology: The study is based on the literature analysis, Internet resources and practical experience obtained during the SAP ERP system implementation projects. Particular attention is paid to developing information systems architecture to solve the problems enterprises face during their growth. Thanks to the analysis of the example of the realisation of the Internet sales process and the induction method, the axioms of automation of business processes in accounting systems were formed.

Findings: Regardless of the qualitative and quantitative transformation, modern DSSs still cannot solve all the enterprise’s problems, mainly due to the use of paper documents and the diversity of national legislation. By the example of the SAP ERP system, the optimal implementation of the business process of VAT liabilities was proposed by Ukrainian legislation for sales below cost price.

Practical Implications: Compliance with the established axioms of automation of business processes will reduce the cost of resources for their implementation, maintenance and correction of potential errors and, therefore, will provide an opportunity to process more transactions. Implementing the proposed algorithm for calculating VAT liabilities in SAP ERP for sales below the cost price will simplify the existing process and enable the fulfilment of other requirements within the framework of current legislation.

This paper seeks to investigate the factors influencing auditors' behavioural intentions towards blockchain technology (BT) adoption in Iraqi government banks. It also highlights the relationships between these factors to determine if the proposed model can provide a more comprehensive means of comprehending how auditors in government banks have adopted BT.

The study uses the unified theory of acceptance and use of technology and expands it by incorporating five external constructs: “system trust”, “cost”, “transparency”, “security” and “auditor's skill.” This study employed a quantitative and exploratory methodology through the gathering and examination of data from 300 auditors. For the evaluation of the measurement and structural models, the authors adopted the partial least squares structural equation modelling approach with SmartPLS v4.

The findings demonstrate that “auditor's skill and four government features of BT adoption” are major factors in government bank auditors' adoption of BT. Additionally, the findings indicate that social influence is a potent indicator of one's intention to adopt BT in the banking industry.

One limit of this study is the selection of governmental perspective. This study is limited to auditors' opinions, who work at the government banks. Further studies may consider other perspectives in order to provide an in-depth analysis of blockchain.

This paper offers valuable insights into the factors influencing the adoption of blockchain technology in Iraqi governmental banks. It provides empirical evidence supporting auditing units and internal auditors in enhancing their job performance through the adoption of such technology.

This study contributes to the existing literature on technology adoption within the audit profession, specifically examining the use of blockchain technology. By exploring the features of technology adoption within government institutions in the auditing field, it introduces a new perspective, emphasizing the importance of auditor skills.

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.

As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.

The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.

This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.

This paper summarizes the future research development of the consensus mechanisms.

Blockchain technology has led the evolution of double entry accounting system to triple entry accounting system. Triple entry accounting is an innovative, promising and potential accounting method when implemented properly would be a game changer for dissemination of accounting information. It is an efficient way to address fundamental concerns of accounting information. This chapter discusses the triple entry accounting system, how it is different from double entry accounting and what are the concerns in implementing triple entry accounting. Triple entry accounting holds the potential to fundamentally evolve accounting practices, can enhance the effective utilisation and sustainable management of resources, and can contribute in development of financial markets.

This paper aims to examine the conditions under which small and medium enterprises (SMEs) invest in security services when they migrate their e-commerce applications to the cloud environment. Using a risk management perspective, the paper assesses the impact of security service pricing, security incident prevalence and virulence to estimate SME security spending at the market level and draw out implications for SMEs and security service providers.

Security risks are inherently characterized by uncertainty. This study uses a Monte Carlo approach to understand the role of uncertainty in the decision to adopt security services. A model relating key security constructs is assembled based on key constructs from the domain. By manipulating security service costs and security incident types, the model estimates the market-level adoption of services, security incidents and damages incurred, along with measures of their relative dispersion.

Three key findings emerge from this study. First, adoption of services and protection is higher when tiered security services are provided, indicating that SMEs prefer to choose their security services rather than accept uniformly priced products. Second, SMEs are considered price-sensitive, resulting in a maximum level of spending in the market. Third, results indicate that security incidents and damages can be much higher than the mean in some cases, and this should serve as a cautionary note to SMEs.

Security spending has been modeled at the firm level. Adopting a market-level perspective represents a novel contribution. Additionally, the Monte Carlo approach provides managers with tangible measures of uncertainty, affording additional information and insight when making security service adoption decisions.

Purpose: The banking sector took the initiative to improve it by releasing a new blockchain application. This innovative approach connects customers from various geographic locations and also gives them a sense of banks’ global presence. Competition is one of the most important market factors because consumer tastes, interests and demands constantly change, making it difficult to meet these problems.

Methodology: Blockchain develops a Blue Ocean Approach in this competitive climate by enticing numerous market segments and giving the financial industry a fresh perspective that benefits the potential consumer. This chapter illustrates how the Blue Ocean Approach can be unlocked by a disruptive technology called blockchain, which generates value innovation and renders the competition obsolete.

Findings: This paradigm shifts the emphasis away from the present competition and generates value and demand for the product. The researcher advises that the Blue Ocean Strategy in retail banking, which uses blockchain technology, works very well since it eliminates cut-throat competition and favours costs, operations, and meeting financial targets on time.

Practical Implications: The study focuses on the bank’s real-world application of the Blue Ocean Strategy and the discovery of sustainable marketing strategies that will aid in their pursuit of innovation. It also highlights the elements introduced in the banking industry to support innovation and the development of long-lasting markets.