Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

Functional encryption based approaches for practical privacy-preserving machine learning

Machine learning (ML) is increasingly being used in a wide variety of application domains. However, deploying ML solutions poses a significant challenge because of increasing privacy concerns, and requirements imposed by privacy-related regulations. To tackle serious privacy concerns in ML-based applications, significant recent research efforts have focused on developing privacy-preserving ML (PPML) approaches by integrating into ML pipeline existing anonymization mechanisms or emerging privacy protection approaches such as differential privacy, secure computation, and other architectural frameworks. While promising, existing secure computation based approaches, however, have significant computational efficiency issues and hence, are not practical. In this dissertation, we address several challenges related to PPML and propose practical secure computation based approaches to solve them. We consider both two-tier cloud-based and three-tier hybrid cloud-edge based PPML architectures and address both emerging deep learning models and federated learning approaches. The proposed approaches enable us to outsource data or update a locally trained model in a privacy-preserving manner by employing computation over encrypted datasets or local models. Our proposed secure computation solutions are based on functional encryption (FE) techniques. Evaluation of the proposed approaches shows that they are efficient and more practical than existing approaches, and provide strong privacy guarantees. We also address issues related to the trustworthiness of various entities within the proposed PPML infrastructures. This includes a third-party authority (TPA) which plays a critical role in the proposed FE-based PPML solutions, and cloud service providers. To ensure that such entities can be trusted, we propose a transparency and accountability framework using blockchain. We show that the proposed transparency framework is effective and guarantees security properties. Experimental evaluation shows that the proposed framework is efficient

Research in multi-cultural relationship building

This study aims to explore the ‘missing gap' between the values of an Accounting firm and the preference shown by Maaori on how they would like to be approached when wanting to build a trusted relationship within a business sense. This study makes use of qualitative approaches in which data is collected primarily through interviews and analysed to produce results and recommendations. The study found that Maaori would like to be approached in a way that makes sense to them and also identifies with their cultural proceedings. It also provides insight into how important trust is when establishing a relationship with Maaori. The study recommends that further studies conducted should interview a wider variety of focus groups to add different elements to this research and that FIRM A's small business department's offerings do not align with what Maaori want so need to be rethought to adapt to Maaor expectations

Expanding the education role to narrow the audit expectation gap: exploring the expectation gap’s existence among accounting students

Society perceives and expects more from auditor’s than what auditors can actually achieve. Society has been found to misperceive the role of an auditor, in particular, in regards to fraud, internal controls and going concern issues. Society’s differing perceptions and expectations of the auditing profession is known as the audit expectation gap. This study aims to explore the audit expectation gap’s existence among Wintec accounting students and the effect that education has on the audit expectation gap. This research intends to answer two research questions. First, does an audit expectation gap exist among Wintec accounting students? Second, what is the effect of education on the audit expectation gap? To answer these research questions, 20 Wintec accounting students have been interviewed. Ten students who have received an audit education, and ten students who have not received audit education. Qualitative data was collected from these interviews and analysed using a mixed methods approach. This study found that an audit education exists among Wintec accounting students. This study also found that education reduced the audit expectation gap, in particular, the deficient performance gap and the communication gap. However, while education did reduce the audit expectations gap, it was not eliminated. This study recommends that Wintec provides a more basic auditing education during introductory accounting papers, educating students on the role of an auditor. This study also recommends that education focuses on the practical use of an audit report, to increase student’s understandings of the information in the audit report, and how this information is communicated. Thus, the researcher believes that these recommendations will help to further reduce the audit expectation

Analysis and optimization of distribution logistics for Just Water Company

This report details the various factors that affect the operational efficiency of distribution logistics. The research aimed at studying the existing components involved in the distribution logistics of Just Water. Distribution logistics begins at the end of the production line where the finished product is emerged until it reaches the customers. The literature review explains the key components of distribution logistics in an organisation. This research analyses the existing components of the distribution logistics of Just Water and discusses possible improvements that can be adapted to increase the overall efficiency of the distribution logistics operation. The background of the research is that Just Water faces difficulty with delivering its products on time during peak seasons. The research tries to unveil the reason for this delay and finds that the demands for extra water-out deliveries are interfering with the normal runs of the trucks, therein delaying their regular schedule. One another cause was found to be the shortage of supplies due to slow or less return logistics. The research suggests a change in the existing drop shipping distribution model and recommends the adaptation of intermediary or multi-stage distribution networks, possibly the ‘Last Mile Delivery’ configuration in order to reduce delivery lead-time, reduce transportation costs and improve customer satisfaction

Interoperability framework of virtual factory and business innovation

Interoperability framework of virtual factory and business innovationTask T51 Design a common schema and schema evolution framework for supporting interoperabilityTask T52 Design interoperability framework for supporting datainformation transformation service composition and business process cooperation among partnersA draft version is envisioned for month 44 which will be updated to reflect incremental changes driven by the other working packages for month 72 deliverable 7.

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

Total Quality Management and Six Sigma

In order to survive in a modern and competitive environment, organizations need to carefully organize their activities regarding quality management. TQM and six sigma are the approaches that have been successful in solving intricate quality problems in products and services. This volume can help those who are interested in the quality management field to understand core ideas along with contemporary efforts done in the field and authored as case studies in this volume. This volume may be useful to students, academics and practitioners across diversified disciplines