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This paper aims to inform the readers about the existing financial intelligence tools that are being used by financial intelligence units. It tries to demonstrate, with the help of a literature review, what the limitations of these tools are and how these limitations hinder the potential of the financial intelligence tools for early detection of terrorist financing activities.

The literature review method was adopted to discuss the financial intelligence tools, their limitations and the implications of the limitations for early detection of terrorist financing activities.

It was found that although the financial intelligence tools were introduced with a view to detect terrorist financing activities early, there are some inherent limitations of the tools relating to technical design features and operational procedures that hinder early detection of terrorist financing activities.

The existing financial intelligence tools need to be repaired by removing the inherent limitations of the tools.

The financial intelligence units should take into cognizance the importance of early detection of terrorist financing activities for preventing terrorist attacks and need to redesign the existing tools in such a way that make these tools effective for early detection of terrorist financing activities.

Peace will be established in society by preventing terrorist attacks through early detection of terrorist financing activities.

The originality of the paper lies in identifying the limitations of the existing financial intelligence tools for the early detection of terrorist financing activities.

This paper aims to inform the readers about the preferred type of financial intelligence for early detection of terrorist financing activities.

Literature review methodology was adopted to find the existing approaches of financial intelligence and logical reasoning was applied to sort out what type of financial intelligence is more preferable for early detection of terrorist financing activities.

It was found that proactive financial intelligence executed through financial intelligence tools is the most preferred type of financial intelligence for early detection of terrorist financing activities.

The research will pave the way for further research on how to design financial intelligence tools for the early detection of terrorist financing activities.

The financial intelligence units will use the preferred type of financial intelligence for the early detection of terrorist financing activities.

It will help to establish peace in the society by thwarting terrorist conspiracies because early detection of terrorist financing through financial intelligence tools will stop the flow of funds to and from terrorists.

The originality of the paper lies in distinguishing proactive financial intelligence from reactive financial intelligence.

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.

This study aims to examine how important the countering terrorism financing is in the fight against terrorism and to what extent does financial intelligence contribute into this field.

To this end, to collect data, semi-structured interview method was conducted for 15 experts, including academicians, judges, security and intelligence officers who have specialised and been practising in the field of terrorism. Then, the data were analysed with the descriptive and systematic method.

The findings highlight that countering terrorism financing is indispensable in the scope of the combating terrorism; however, there are still much to be done in practice to achieve more success in this field. The results also suggest that the process requires more flexible and proactive approach with the help of an autonomous financial intelligence unit to be more efficient. Moreover, there must be better cooperation and coordination at both national and international levels. Furthermore, training more professionals from multidisciplinary backgrounds and raising awareness among the public and private sectors are found to be other key factors for effective combating of the system.

The research has been conducted on participants who mostly have been in fight against terrorism over 20 years who are aware of the early methods, as well as the recent ones both in theory and in practice. Their view is significant to understand the situation in combating financing of terrorism.

The purpose of this paper is to analyze the effectiveness of the Financial Action Task Force (FATF’s) recommendations in Latin America in the fight against Money Laundering (ML) through the Immediate Outcomes 4 (Preventive Measures) and 6 (Financial Intelligence) and the relationship between anti-money laundering (AML) effectiveness and anti-corruption measures.

Through quantitative and quality methods measure the performance of the Immediate Outcomes’ levels in compliance with the established FATF’ standards; The objective is to analyze progress in the prevention and identification of ML.

The authors discuss the relationship between AML effectiveness and anti-corruption measures starting with the analysis of the best-ranked country in these indicators (IO4 and IO6).

Mutual evaluations require a long process, which is why the authors do not have all the updated data for all the Latin American countries; only the updated Immediate Outcomes data up to September 16, 2021, are taken. This paper is part of the Research Project “The impacts of corruption in the Western Hemisphere and regional responses.”

Through a multidisciplinary analysis, the Immediate Results evaluation model is prioritized to measure the effectiveness of the methods applied in Latin America through the IO4 and IO6. The model that presents the best effectiveness is selected and the successes that this country is applying over its peers are analyzed. From this analysis, a quantitative and qualitative analysis can be appreciated.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

The purpose of this paper is to share research data from the Financial Intelligence sector on trade-based money laundering (TBML), as a way to better inform banking risk assessment and the submission of suspicious activity reports (SARs).

The research data formed part of a bigger project on TBML banking risk assessment for improving the detection of TBML activity. This paper analysed the data from an online survey carried out among the financial intelligence staff from financial intelligence units (FIUs) and some external financial intelligence agencies. The aim was to determine which areas of banking SARs needed to be improved or enhanced to support FIU investigations.

The research found that FIUs do use the data supplied to them, in particular the SARs. The research also found that more data would be appreciated from banks especially in relation to beneficial ownership information and politically exposed persons data. The findings highlighted that contact between banks and FIUs was limited and restricted to a couple of key individuals, whereas the increased requirement for intelligence and more data would suggest that this relationship needs to be expanded and strengthened.

The main limitation was the restricted scope of the survey (only focussed on TBML) and was broad in depth, and perhaps a local FIU survey would be useful to look at specific country recommendations. Similar research also needs to be conducted on other forms of ML activity. The research identified the need for more information on beneficial ownership information; however, other work needs to be done on how exactly banks can access this data.

The main outcome from the research was the need for SARs to contain more detailed information on beneficial ownership and politically exposed persons data. This needs to be incorporated into a specific risk assessment tool for TBML that considers not only the client but also relevant business partners and silent partners/shell companies used by the client. This research is part of a bigger research project that has developed a risk matrix tool for TBML and can be linked into this work.

The paper used original data collected by the researcher from 49 FIU and financial intelligence staff across the globe. The timely presentation of the results and the nature of the sample means that this is relevant and useful data to be presented to the banking sector.

This paper aims to examine the probable effect of the General Data Protection Regulation of the European Union on the transfer of financial intelligence to a third country without an adequacy decision.

This is an analytical study of the financial intelligence exchange mechanisms between the Bangladesh Financial Intelligence Unit (BFIU) and its foreign counterparts. The research analyses the key challenges this national agency faces in using the Egmont Group membership to import financial intelligence from jurisdictions with a superior data protection regime.

Membership in the Egmont Group of Financial Intelligence Units does not guarantee unrestricted international intelligence exchange. Existing data protection regulations in Bangladesh are inadequate. This may forbid the transfer of the financial intelligence linked to European Union (EU) data subjects to Bangladesh.

This paper does not cover a thorough discussion on any specific alternative tools for data transfer from the EU to a third country except for “appropriate safeguards” options.

The results of this study will help understand the existing legal and institutional limitations that may prevent intelligence exchange between the BFIU and its EU counterparts.

The study helps ascertain the legislative reform necessary in Bangladesh, a third country, to facilitate the transfer of financial intelligence from the EU.

The main purpose of this research is to empirically test the impacts of emotional intelligence score and emotional intelligence processing on the performance of investor’s portfolio.

A mail questionnaire survey was conducted on a sample of 983 international investors. The total number of usable responses received was 197 giving a response rate of 20.4 per cent. From 197 investors, 46 accepted to complete the experiment study during two trading hours for each investor from January first until February 19, 2015.

The results reveal a positive impact of emotional intelligence on portfolio performance. Additional analysis shows that the emotional intelligence process has a significant impact on the portfolio performance. The higher impact is revealed when the investors understand the markets tendency, manage their own emotions, take their financial decisions and finally control their personnel emotions during market fluctuations. The lower impact is detected when investors take reactive decisions after perceiving the markets tendency. This research also reveals that the investors have high capacity to manage and control their emotions during market fluctuations especially who are characterized by high emotional intelligence level.

The first limit of this research is the exploration of limited number of investors and financial operations during limited period. Therefore, the results could not be generalized, and further studies should include larger samples during larger period. The second limitation concerns the used variables to measure the portfolio performance and the emotional intelligence level. For future studies, it will be preferred to use other quantitative and qualitative variables lead to measure the different analytical dimensions of portfolio performance and emotional intelligence.

The results hold implications for investors that seek to enhance efficiently and effectively the portfolio performance. It also prompts investors to focus on effort that can improve the management and the control of personnel emotions.

This paper presents one of the first empirical studies that attempt to explore how emotional intelligence and, particularly, emotional process serve to sustain the performance of portfolio during market fluctuations.

The purpose of this study was to demonstrate a cloud business intelligence model for industrial SMEs. An initial model was developed to accomplish this, followed by validation and finalization of the cloud business intelligence model. Additionally, this research employs a mixed-techniques approach, including both qualitative and quantitative methods. This paper aims to achieve the following objectives: (1) Recognize the Cloud business intelligence concepts. (2) Identify the role of cloud BI in SMEs. (3) Identify the factors that affect the design and presenting a Cloud business intelligence model based on critical factors affecting SMEs during pandemic COVID-19. (4) Discuss the importance of Cloud BI in pandemic COVID-19 for SMEs. (5) Provide managerial implications for using Cloud BI effectively in Iran’s SMEs.

In the current study, an initial model was first proposed, and the cloud business intelligence model was then validated and finalized. Moreover, this study uses a mixed-methods design in which both qualitative and quantitative methods are used. The fuzzy Delphi Method has been applied for parameter validation purposes, and eventually, the Cloud business intelligence model has been presented through exploiting the interpretive structural modeling. The partial least squares method was also applied to validate the model. Data were also analyzed using the MAXQDA and Smart PLS software package.

In this research, from the elimination of synonym and frequently repeated factors and classification of final factors, six main factors, 24 subfactors and 24 identifiers were discovered from the texts of the relevant papers and interviews conducted with 19 experts in the area of BI and Cloud computing. The main factors of our research include drivers, enablers, competencies, critical success factors, SME characteristics and adoption. The subfactors of included competitors pressure, decision-making time, data access, data analysis and calculations, budget, clear view, clear missions, BI tools, data infrastructure, information merging, business key sector, data owner, business process, data resource, data quality, IT skill, organizational preparedness, innovation orientation, SME characteristics, SME activity, SME structure, BI maturity, standardization, agility, balances between BI systems and business strategies. Then, the quantitative part continued with the fuzzy Delphi technique in which two factors, decision-making time and agility, were deleted in the first round, and the second round was conducted for the rest of the factors. In that step, 24 factors were assessed based on the opinions of 19 experts. In the second round, none of the factors were removed, and thus the Delphi analysis was concluded. Next, data analysis was carried out by building the structural self-interaction matrix to present the model. According to the results, adoptability is a first-level or dependent variable. Regarding the results of interpretive structural modeling (ISM), the variable of critical success factors is a second-level variable. Enablers, competencies and SME characteristics are the third-level and most effective variables of the model. Accordingly, the initial model of Cloud BI for SMEs is presented as follows: The results of ISM revealed the impact of SME characteristics on BI critical success factors and adoptability. Since this category was not an underlying category of BI; thus, it played the role of a moderating variable for the impact of critical success factors on adoptability in the final model.

Since this study is limited to about 100 SMEs in the north of Iran, results should be applied cautiously to SMEs in other countries. Generalizing the study's results to other industries and geographic regions should be done with care since management perceptions, and financial condition of a business vary significantly. Additionally, the topic of business intelligence in SMEs constrained the sample from the start since not all SMEs use business intelligence systems, and others are unaware of their advantages. BI tools enable the effective management of companies of all sizes by providing analytic data and critical performance indicators. In general, SMEs used fewer business intelligence technologies than big companies. According to studies, SMEs understand the value of simplifying their information resources to make critical business choices. Additionally, they are aware of the market's abundance of business intelligence products. However, many SMEs lack the technical knowledge necessary to choose the optimal tool combination. In light of the frequently significant investment required to implement BI approaches, a viable alternative for SMEs may be to adopt cloud computing solutions that enable organizations to strengthen their systems and information technologies on a pay-per-use basis while also providing access to cutting-edge BI technologies at a reasonable price.

Before the implementation of Cloud BI in SMEs, condition of driver, competency and critical success factor of SMEs should also be considered. These will help to define the significant resources and skills that form the strategic edge and lead to the success of Cloud BI projects.

Most of the previous studies have been focused on factors such as critical success factors in cloud business intelligence and cloud computing in small and medium-sized enterprises, cloud business intelligence adoption models, the services used in cloud business intelligence, the factors involved in acceptance of cloud business intelligence, the challenges and advantages of cloud business intelligence, and drivers and barriers to cloud business intelligence. None of the studied resources proposed any comprehensive model for designing and implementing cloud business intelligence in small and medium-sized enterprises; they only investigated some of the aspects of this issue.