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This review aims to provide an understanding of the strengths and weaknesses of forensic accounting education and profession.

This paper reviews published forensic accounting studies to explore forensic accounting strengths and weaknesses.

The strengths of forensic accounting are its benefits to students and accounting professionals, the significant need and increasing demand, the new career channels and the reduction of fraud. The weakness factors are the lack of regulation, the lack of control over the profession entry, the lack of agreement on how to teach forensic accounting, the lack of specialized research journals, the misconception of its intrinsic aim, the lack of highly qualified practitioners and educators and the lack of public recognition and occupation reputation.

It is hoped that this structured investigation of the factors relevant to the current and future status of forensic accounting education and profession will provide a sufficient overview of the critical issues and concerns that are important to be known for understanding and advancing the vital application of forensic accounting on the Socio-Economic Development. It is anticipated that this paper has an impact on future policy that ultimately contributes to improving business and limit fraud incidents, thus, it can contribute to business and socio-economic development.

The literature on forensic accounting is extensive and varied. However, there is a lack of comprehensive understanding of the strengths and weaknesses of forensic accounting. This study provided policymakers with a comprehensive understanding of forensic accounting.

This study aims to examine the scope and contribution of Forensic Clinical Psychology (FCP) advice from the National Crime Agency (NCA) to criminal investigations in the UK to address the gap in current knowledge and research.

The 36 FCP reports reviewed were written between 2017 and 2021. They were analysed using Toulmin’s (1958) application of pertinent arguments to the evaluation process. The potential utility of the reports was analysed in terms of the advice provided.

Most of the reports involved murder and equivocal death. The reports focused primarily on understanding the offender’s psychopathology, actions, motivation and risk to self and others using a practitioner model of case study methodology. Out of the 539 claims, grounds were provided for 99% of the claims, 91% had designated modality, 62% of the claims were potentially verifiable and 57% of the claims were supported by a warrant and/or backing. Most of the reports provided either moderate or high insight into the offence/offender (92%) and potential for new leads (64%).

The advice provided relied heavily on extensive forensic clinical and investigative experience of offenders, guided by theory and research and was often performed under considerable time pressure. Flexibility, impartiality, rigour and resilience are essential prerequisites for this type of work.

To the best of the authors’ knowledge, this study is the first to systematically evaluate forensic clinical psychology reports from the NCA. It shows the pragmatic, dynamic and varied nature of FCP contributions to investigations and its potential utility.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

The present study aims to examine the scope and contribution of behavioural investigative advice (BIA) reports from the National Crime Agency (NCA).

The 77 BIA reports reviewed were written between 2016 and 2021. They were evaluated using Toulmin’s (1958) strategy for structuring pertinent arguments, current compliance with professional standards, the grounds and backing provided for the claims made and the potential utility of the recommendations provided.

Consistent with previous research, most of the reports involved murder and sexual offences. The BIA reports met professional standards with extremely high frequency. The 77 reports contained a total of 1,308 claims of which 99% were based on stated grounds. A warrant and/or backing was provided for 73% of the claims. Most of the claims in the BIA reports involved a behavioural evaluation of the crime scene and offender characteristics. The potential utility of the reports was judged to be 95% for informative behavioural crime scene analysis and 40% for potential new lines of enquiry.

The reports should serve as a model for the work of behavioural investigative advisers internationally.

To the best of the authors’ knowledge, this is the first study to systematically evaluate BIA reports commissioned by the NCA; it adds to previous similar studies by evaluating the largest number of BIA reports ever reviewed, and uniquely provides judgement of overall utility.

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

There is widespread concern about the fact that small- and medium-sized enterprises (SMEs) seem to be particularly vulnerable to cyberattacks. This is perhaps because smaller businesses lack sufficient situational awareness to make informed decisions in this space, or because they lack the resources to implement security controls and precautions.

In this paper, Endsley’s theory of situation awareness was extended to propose a model of SMEs’ cyber situational awareness, and the extent to which this awareness triggers the implementation of cyber security measures. Empirical data were collected through an online survey of 361 UK-based SMEs; subsequently, the authors used partial least squares modeling to validate the model.

The results show that heightened situational awareness, as well as resource availability, significantly affects SMEs’ implementation of cyber precautions and controls.

While resource limitations are undoubtedly a problem for SMEs, their lack of cyber situational awareness seems to be the area requiring most attention.

The findings of this study are reported and recommendations were made that can help to improve situational awareness, which will have the effect of encouraging the implementation of cyber security measures.

This is the first study to apply the situational awareness theory to understand why SMEs do not implement cyber security best practice measures.

Governments worldwide have been trying to direct more students towards STEM specialty areas, but the number of students majoring in STEM and seeking a STEM career path is not proportional to their efforts. Additionally, the COVID-19 pandemic has brought to the surface the unrealistic expectations and false assumptions held by the general public about STEM fields. These have mainly been shaped by the “T” in the acronym, substituting technology for science. This brief urges for a policy shift in STEM and science education to reveal the true nature of science, emphasizing that science is not about providing definite answers nor granting miraculous solutions but about creating and disseminating knowledge based on the most accurate outcome informed by empirical evidence. Such a policy change will have a dual effect. First, it will ensure students authentically appreciate the nature of science, serve as agents of scientific thinking and reasoning within their communities, and become more oriented towards STEM majors. Secondly, this policy shift will prompt more realistic expectations and valid assumptions about science for the general public, which will help reduce public confusion and uncertainty, specifically at times of pandemics or emergencies.

Benford's Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford's Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford's Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford's Law. This paper implements a transformation procedure which allows for assessment data to be compared to Benford's Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.

The purpose of this paper is to explore the perceptions of supply chain managers regarding the elements that make up cyber supply chain risk management (CSCRM) and the related level of alignment, to understand how organizations can deploy a CSCRM strategy that goes beyond the technical, internal functioning of single companies and moves beyond the dyad, to create a better alignment that can ultimately lead to improved cyber supply chain resilience.

An exploratory survey in the fast-moving consumer goods (FMCG) industry involving over 100 organizations in Italy was conducted. Results were analysed through one-way analysis of variance, to appraise the differences in the perceptions of the various actors of the FMCG supply chain (Manufacturers, Logistics Service Providers, Retailers).

While a certain degree of alignment of the perceptions across the FMCG supply chain exists, the study found that Logistics Service Providers can play a crucial role as orchestrators of the CSCRM process towards a more “supply chain-oriented” response to cyber threats and risk events. The research also highlights the necessity to see people as key elements for improving cyber resilience in the supply chain.

Through a vertical analysis of a supply chain, the study extends the existing theory on CSCRM, which contains isolated case studies. It also contributes to extending the current theory with the proposal of the paradigm of Logistics Service Providers as orchestrators of the CSCRM process. The study combines different classifications of CSCRM initiatives and embraces theories external to the supply chain literature.

Through the empirical analysis, this study helps practitioners in streamlining the design of cyber security strategies and actions that span across the supply chain for better alignment. This could mean more coordination of efforts and more targeted/accurate investments in CSCRM initiatives. The study invites practitioners to ponder the perceived relevance of the human factor as a source of risk and the perceived importance of countermeasures aimed at mitigating risk events stemming from that source.

By focusing on an entire supply chain, this is one of the first studies on CSCRM that goes beyond the dyad. Its originality also lies in its use of the investigations of perceptions along the supply chain as pillars for the alignment of CSCRM strategies and mitigation initiatives. This original perspective allows for discovering the role of Logistics Service Providers in driving the alignment of the efforts towards better outcomes of the CSCRM process.

This chapter examines the phenomenon of doxxing: the practice of publishing private, proprietary, or personally identifying information on the internet, usually with malicious intent. Undertaking a scoping review of research into doxxing, we develop a typology of this form of technology-facilitated violence (TFV) that expands understandings of doxxing, its forms and its harms, beyond a taciturn discussion of privacy and harassment online. Building on David M. Douglas's typology of doxxing, our typology considers two key dimensions of doxxing: the form of loss experienced by the victim and the perpetrator's motivation(s) for undertaking this form of TFV. Through examining the extant literature on doxxing, we identify seven mutually non-exclusive motivations for this form of TFV: extortion, silencing, retribution, controlling, reputation-building, unintentional, and doxxing in the public interest. We conclude by identifying future areas for interdisciplinary research into doxxing that brings criminology into conversation with the insights of media-focused disciplines.