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This study is focused to support an ongoing development project of the case company's current state and the challenges of the order-to-cash (O2C) process. The O2C process is the most visible process to the customer, and therefore, its punctual and fluent order management is vital. It is observed that the high degree of manual work in the O2C process causes mistakes, delays and rework in the process. The purpose of this article is therefore to analyze the case company's current state of the O2C process as well as to identify the areas of development in this process by deploying the means of Lean Six Sigma tools such as value stream mapping (VSM).

The study was conducted as a mix of quantitative and qualitative analysis. Based on both the quantitative and qualitative data, a workshop on VSM was organized to analyze the current state of the O2C process of a case company, engaged in the energy and environment sector in Finland.

The results found that excessive manual work was highly connected to inadequate or incorrect data in pricing and invoicing activities, which resulted in canceled invoices. Canceled invoices are visible to the customer and have a negative impact on the customer experience. This study found that by improving the performance of the O2C process activities and improving communication among the internal and external stakeholders, the whole O2C process can perform more effectively and provide better customer value.

The O2C process is the most visible process to the customer and therefore its punctual and fluent order management is vital. To ensure that the O2C process is operating as desired, suitable process performance metrics need to be aligned and followed. The results gathered from the case company's data, questionnaire interviews, and the VSM workshop are all highlighted in this study. The main practical and managerial implications were to understand the real-time O2C process performance, which is necessary to ensure strong performance and enhance continuous improvement of the O2C process that leads to operational excellence and commercial competitiveness of the studied case company.

The accurate prediction of incoming cash flows enables more effective cash management and allows firms to shape firms' planning based on forward-looking information. Although most firms are aware of the benefits of these forecasts, many still have difficulties identifying and implementing an appropriate prediction model. With the rise of machine learning algorithms, numerous new forecasting techniques have emerged. These new forecasting techniques are theoretically applicable for predicting customer payment behavior but have not yet been adequately investigated. This study aims to close this research gap by examining which machine learning algorithm is the most appropriate for predicting customer payment dates.

By using various machine learning algorithms, the authors evaluate whether customer payment behavior patterns can be identified and predicted. The study is based on real-world transaction data from a DAX-40 firm with over 1,000,000 invoices in the dataset, with the data covering the period 2017–2019.

The authors' results show that neural networks in particular are suitable for predicting customers' payment dates. Furthermore, the authors demonstrate that contextual and logical prediction models can provide more accurate forecasts than conventional baseline models, such as linear and multivariate regression.

Future cash flow forecasting studies should incorporate naïve prediction models, as the authors demonstrate that these models can compete with conventional baseline models used in existing machine learning research. However, the authors expect that with more in-depth information about the customer (creditworthiness, accounting structure) the results can be even further improved.

The knowledge of customers' future payment dates enables firms to change their perspective and move from reactive to proactive cash management. This shift leads to a more targeted dunning process.

To the best of the authors' knowledge, no study has yet been conducted that interprets the prediction of incoming payments as a daily rolling forecast by comparing naïve forecasts with forecasts based on machine learning and deep learning models.

Money laundering (ML) is a major criminal offence stemming from unethical practices by personnel on the ground at Chattogram Port, an important import and export facility in Bangladesh. Because money can be more easily laundered through imports, it is necessary to investigate the dubious process in this sector. This study aims to identify the items most regularly used for easy ML and the factors contributing to their vulnerability.

This research uses a qualitative approach and analyses information from primary sources. Data is obtained from customs officials, port authority personnel, importers and customs brokers through semi-structured questionnaires. Although there are many techniques for ML, this study only found three most overwhelming: under-invoicing, over-invoicing and misdeclaration. A few case studies have been used based on newspaper reports and the internet to triangulate the qualitative data.

Four import items – food products, garments, capital machinery and chemicals – have a higher risk of ML. This study also revealed that money launderers prefer under-invoicing food and garment items. Misdeclaration is more commonly associated with capital machinery and chemical items. Over-invoicing, on the other hand, is only prevalent in government purchases. The port authorities need to pay particular attention to these issues.

As ML is an ongoing activity that changes over time, the findings of this research are circumscribed by the data collected at a single point in time. Additionally, this research did not consider alternative laundering methods.

The research results can provide a basis for creating effective anti-money laundering (AML) strategies to assist with sustainable economic growth.

Developing effective AML measures can help combat corruption and establish good governance in the country and support human well-being.

This paper presents original research findings based on technical analysis. The Chattogram Port Authority and the National Board of Revenue have accepted and used the main findings in a collaborative action plan to tackle ML. The Bangladesh Bank, the country’s central bank, has also incorporated the necessary guidelines and regulations into the Money Laundering Prevention Act, 2012.

The health crisis linked to COVID-19 has made digitization a significant issue for companies regardless of their form or geographical location. Our research focuses on the financial technology (Fintech) revolution in the context of international development. Its theoretical framework lies in both the fields of Fintech and factoring. As innovative start-ups that combine finance with new technologies, Fintechs have been able to disrupt the banking world in a few years by challenging its traditional practices. We use the case method to analyze two Fintech in-depth, and our results highlight the upheaval of Fintechs in the factoring sector. The supply of the analyzed Fintech is a limited working capital requirement. Furthermore, Fintechs may threaten classical banks due to the innovation of their offers and business models. However, the Fintech revolution focused on corporate finance is still in its infancy.

Brazil uses the dollar as a vehicle currency to invoice its exports. This fact produces a tendency toward equalizing the prices of products in dollars in the international market and reducing the ability of firms to practice pricing-to-market (PTM). This study aims to evaluate the hypothesis by estimating error correction models in panel data, obtaining estimates of PTM for 25 manufacturing products exported by Brazil between 2010 and 2020.

This study uses the correlated common effect estimator proposed by Pesaran (2006) and Chudik and Pesaran (2015b) to estimate the PTM coefficients.

Results of this study indicate that exporters practice local-currency pricing stability for dollar prices. This study obtains that Brazilian exporters tend to stabilize their dollar price for exports, reducing heterogeneity between destination markets. The results are in agreement with the hypothesis of the prevalence of the coalescing effect of Goldberg and Tille (2008) and lower sensitivity of the markup adjustment to the specific market, as pointed out by Corsetti et al. (2018). The pricing of Brazilian exports in dollars reflects a profit maximization strategy that considers an international price system based on global demand for products.

In addition to analyzing the dollar role in the pricing of Brazilian exports through the triangular decomposition, this study also shows the importance of examining the cross-section dependence of errors, considering the heterogeneous cointegration in export pricing models and producing PTM estimates for short-term and long-term.

The purpose of this study is to identify money laundering typologies and techniques in Ethiopia.

This is a descriptive study that relies on primary data generated from interviewees drawn from the Ethiopian Financial Intelligence Center, Ethiopian Customs Commission, selected commercial banks and law enforcement agencies, as well as secondary data from government reports, media press, statutes and other online and offline sources.

According to this study, criminals in Ethiopia used several laundering techniques, the most common of which are money laundering using financial institutions, trade-based money laundering, cash-based money laundering, money laundering through illegal hawala, shell companies, or anonymous beneficiaries. Criminals have recently been suspected of using financial technologies and virtual currencies to launder the proceeds of their illicit activities. The laundering strategies are extremely intertwined and their distinction remains highly blurred. Moreover, the typologies are operated transnationally, despite being highly tailored to Ethiopia’s political economy.

This is one of the very few papers to date that provides the typologies and techniques of money laundering, specifically in the context of cash-intensive economies.

Increasingly, emerging information technologies such as shared software and continuous accounting are offering alternative ways to perform accounting tasks in a supposedly more efficient fashion. Yet, few studies have investigated how they affect the provision of accounting services, especially in the context of small accounting firms, which provide legal and tax services to entrepreneurs and businesses. Drawing on the service perspective, the paper critically examines how technological innovation challenges and reconfigures the co-production of accounting services in these firms.

The paper answers calls issued in prior studies to conduct empirical research on emerging information technologies for accountants. It focuses on the specific context of small accounting firms and draws on interviews with small accounting firms' managers (n = 20).

The study emphasizes five significant challenges that accounting firm managers face when using information technologies to support the provision of their services (ensuring reliability, factoring in their heterogeneous client base, repricing, training clients to use new technologies and promoting advisory services). Information technologies are shown to have a structuring role in the co-production of accounting services, as they lead to reconfigurations of the relationships between accountants and their clients. A range of four configurations is developed to highlight accountants' strategies to maintain collaborative relationships with their clients while integrating new technologies into their work practices.

By conceptualizing accounting services as a co-production process, the paper offers new insights into the implications of emerging information technologies for small accounting firms.

Money laundering (ML) has become a significant challenge all over the world today. Trade-based money laundering (TBML) is a type of ML that poses a hazard to any country. In recent years, developed and developing countries have pursued liberal policies for international financial markets. The Financial Action Task Force (FATF) defines TBML as the process of concealing criminal earnings and shifting value through trade transactions in an attempt to justify their illicit origins. As international financial markets have improved ML controls, criminals have turned to the trade sector as a new venue, raising trade risks. The purpose of this study is to highlight the danger posed by TBML and the initiatives that should be taken to prevent such.

A review of publicly available reports, case studies, secondary data and literature on TBML from a variety of Sri Lankan and international contexts comprised the methodology. However, due to the dearth of literature on TBML details/information in the Sri Lankan context, international case studies have been analyzed. More critically, there are no precise estimates of TBML or defined protocols for collecting and maintaining TBML data. As a result, the FATF potential TMBL typologies were analyzed, and typical TBML procedures were examined to identify viable treats for Sri Lanka.

The study found that TBML has a significant effect on the economy and, as a result, social conflicts. Sri Lanka has the potential for TBML, and ML through financial institutions was identified as a major risk. Literature, on the other hand, shows that a large quantity of money has been laundered using TBML in Sri Lanka. The geographical location entices criminals to wash their illicit gains, and so the country has potential danger from South Asian countries. However, because of the sociopolitical climate in Sri Lanka, criminals are constantly looking for ways to profit illegally. Relaxing rules to promote foreign investment may encourage launderers to use their illicit proceeds. The government needs to take great care when dealing with this particularly delicate issue.

Due to the complexity of financial crimes, this study had a number of limitations, as do many others. The data used for this study was sourced from publicly available information and the TBML has been clearly defined or understood due to the fact that the complexity of the methods used by criminals. As a result, the number of local instances reported on TMBL is quite small, hence this study relied on international case studies.

This research on TBML in Sri Lanka is original. It is anticipated that the findings and contribution of the study would help the stakeholders develop TBML prevention measures.

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 study aims to analyze how startups organize their purchasing activities to improve operative excellence and become attractive customers.

The authors use a two-phase exploratory approach with semistructured interviews and a World Café. In total, 20 startup purchasers and suppliers participated. It is an international study with participants from eight countries (Belgium, Brazil, France, Germany, Hungary, The Netherlands, the UK and the USA).

The authors find that startups organize the purchasing function in five ways: partial outsourcing, transactional-oriented, strategic only, outsourced purchasing and full department. Each type has advantages and disadvantages regarding operative excellence. The authors identify type-specific antecedents to operative excellence: forecasting, payment habits, ordering process, contact accessibility and quick decision-making.

The value of this paper is that it offers entrepreneurs a framework to organize startup purchasing activities, including outsourcing options. Furthermore, it provides theoretical contributions that expand the topic of purchasing and supply organization and operative excellence to the startup context.

The value of this paper is that, to the best of the authors’ knowledge, it is the first to explore purchasing organization and operative excellence in startups.