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Customs risk management has been widely recognized as a powerful tool to balance between trade facilitation and revenue maximization. However, most customs administrations worldwide, particularly in developing countries, are suffering from a lack of experience and knowledge to assess their risk management systems for revenue protection (RP). Customs risk management has a very limited legacy in the literature. Academic research is quite scarce and very limited, although its relevance to customs administrations. This paper aims to identify the key risk profiles and indicators that contribute to the protection of customs revenue and investigate the role of these risk profiles and indicators on customs RP using the case of Jordan Customs.

This study adopts a panel data approach by using the case of Jordan Customs. Data were collected from the risk targeting and selectivity system at Jordan Customs for the year 2019, a total of 600 observations.

The findings show that all risk targeting criteria except random selectivity (RS) and HS code have a significant positive association with RP. The findings also revealed that RS is an effective tool to prevent traders with fraud and offenses history from a prediction of targeting patterns and to assess the traders’ compliance and make sure their declarations are free from fraud or offenses. Moreover, the findings of this study indicate that customs administrations should adopt alternative programs such as authorized economic operator and post clearance audit as an effective means to measure and improve compliance.

The main contribution of this study lies in proposing a model to assist customs administrations in assessing the performance of risk management systems to protect revenue. This model provides a comprehensive conceptualization and explanations necessary for numerous aspects of risk management projects and it assists to predict the outcomes based on formulated indicators.

This study provides guidelines for risk analysts on how to identify and assess the key risk profiles and indicators that effect on maximizing the detection of revenue leakage and to obtain interpretable and predictable results. In addition, the findings of this study will assist customs administrations in supporting revenue collection, minimizing uncertainty, allocating resources more effectively to target high-risk consignments, while simplifying the procedures for the safe consignments.

This paper is of significant value because it is one of the preliminary studies that empirically identify the risk indicators/profiles that contribute to the protection of revenue and investigate the predictive power of these risk indicators/profiles as a key predictor to protect customs revenue.

Electronic government (e-government) is perceived as an effective tool to enhance accountability in public organizations. However, e-government implementation to enhance accountability is still unclear and involves many complex processes because of the multiple accountabilities disorder. The e-government elements that contribute to mitigate the disorders and dysfunctions of accountability relationships are still underdeveloped in the current literature. This paper aims to provide understanding on how e-government enhances public organizations’ accountability by highlighting the key elements of e-government that mitigate the disorders and dysfunctions of accountability relationships.

This study adopts a qualitative case study approach by using the case of Jordan Customs. Data were collected using a triangulation approach that involved semi-structured interviews, document reviews and observation.

The findings revealed that the e-government elements that mitigate the disorders and dysfunctions of accountability relationships are classified into three contexts, namely, technological, environmental and organizational aspects. The technological elements include systems integration, single window and electronic connectivity. The environmental elements embrace public participation and partnership council. The organizational elements comprise having well-defined strategic plan and risk management approach.

This study contributes and gives further insight into how to address the confusion, fuzziness and dysfunctions in accountability relationships existing in the literature by providing several success elements that mitigate the problematic of disorder between accountability relationships in public organizations. The paper highlights the need to investigate further elements, particularly, in the organizational context, to assist public organizations in improving their performance to enhance accountability in their operations.

This study provides guidelines for future e-government implementation strategy in public organizations, particularly, in the context of developing countries, as most of the recent studies of e-government in developing countries indicated that they are suffering from difficulty of managing e-government implementation to promote accountability successfully and are struggling with a lack of resources and experiences to handle this new trend of technology.

This study is of a significant value, as it is one of the preliminary studies that empirically extend the accountability dimensions suggested by Koppell (2005) with the key success elements of e-government that enhance accountability proposed by Heeks (1998b) and other current literature. This paper enriches the body of literature by providing some new key success elements of e-government that enhance accountability in public organizations. It also contributes to the expansion of the boundaries of knowledge by adding further interpretation on how these elements reduce the existing confusions and dysfunctions in accountability relationships.

Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience.

The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots.

The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions.

The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.