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This study aims to enlist the red flag behaviors exhibited in financial services frauds.

A pluralistic mixed methodology was adopted in this study. Data collected via semi-structured interviews were coded, quantified and subjected to descriptive analysis to identify the most frequently exhibited red flag behaviors in financial services frauds. The relative risk of exhibition of the identified red flag behaviors was assessed by intuitively comparing the red flag behaviors identified in financial services frauds (experimental group, n = 24) with the red flag behaviors identified in a heterogeneous control sample of non-financial services frauds (control group, n = 28).

This study identifies six red flag behaviors likely to be more frequently exhibited in financial services frauds than in non-financial services frauds.

Results of this study can be used to develop a typical behavioral profile of a financial services fraud perpetrator. Active communication of this profile in fraud awareness training can help make fraud conspicuous in the financial services industry.

This study is unique because human behavior as a possible fraud indicator is an under-researched area. Further, this study examines first level of evidence and attempts an ex-post analysis of actual red flag behaviors exhibited in acknowledged fraud cases in which the perpetrator/perpetrators has/have been clearly identified.

The purpose of this paper is to highlight the intelligence and investigatory challenges experienced by law enforcement agencies in discovering the identity of illicit Bitcoin users and the transactions that they perform. This paper proposes solutions to assist law enforcement agencies in piecing together the disparate and complex technical, behavioural and criminological elements that make up cybercriminal offending.

A literature review was conducted to highlight the main law enforcement challenges and discussions and examine current discourse in the areas of anonymity and attribution. The paper also looked at other research and projects that aim to identify illicit transactions involving cryptocurrencies and the darknet.

An optimal solution would be one which has a predictive capability and a machine learning architecture which automatically collects and analyses data from the Bitcoin blockchain and other external data sources and applies search criteria matching, indexing and clustering to identify suspicious behaviours. The implementation of a machine learning architecture would help improve results over time and would be less manpower intensive. Cyber investigators would also receive intelligence in a format and language that they understand and it would allow for intelligence-led and predictive policing rather than reactive policing. The optimal solution would be one which allows for intelligence-led, predictive policing and enables and encourages information sharing between multiple stakeholders from the law enforcement, financial intelligence units, cyber security organisations and fintech industry. This would enable the creation of red flags and behaviour models and the provision of up-to-date intelligence on the threat landscape to form a viable intelligence product for law enforcement agencies so that they can more easily get to the who, what, when and where.

The development of a functional software architecture that, in theory, could be used to detected suspicious illicit transactions on the Bitcoin network.

The purpose of this study is to investigate the informal micro-level mechanisms through which caregivers maximize their health literacy and caregiving skill-set, particularly in cases of emergent, pervasive health disorders. Specifically, I investigate how important micro-level social factors, such as lay self-education and local community networks, mitigate extensive experiences of medical uncertainty that are associated with caring for a child with autism. This study theorizes a series of processes of becoming lay health care professionals (HCP), which serve as effective health care interventions and ways to secure vital resources for patients and their families.

This study uses qualitative research methods in the form of 50 individual intensive interviews with primary caregivers of at least one child under the age of 18 with an official autism diagnosis, as well as two years of participant-observation at two primary sites that are autism parent and caregiver resource meetings, both located in Northern California.

This study first demonstrates the major institutional limits and gaps involved in health-related caregiving for children with autism. Next, I define the processes through which caregivers challenge these institutional constraints and fight for life altering resources for their families, which include becoming a lay diagnostician and expert caregiver. Here, I demonstrate a sophisticated set of health literacy skills and key local community-based ties that caregivers develop and rely on, which affords families the tools to overcome diverse institutional obstacles in health-seeking and health care access.

The families in this study are predominantly white, middle-class, and reside in California. For future research, the scope of the study could be expanded by increasing the sample size and including greater geographic and demographic diversity.

This study contributes vital, yet missing, pieces to the autism puzzle, which currently focuses on prevention, the fight for a so-called “cure,” and the role of vaccines in disorder prevalence. In the meantime, families are living with autism each day and are struggling for understanding and knowledge, and to secure adequate support services. In doing so, this study sheds light on current institutional gaps and limits in health care and delivery for children with autism, and suggests specific effective health care interventions applicable to other cases of emergent illnesses and disorders.

The purpose of this paper is to examine whether threat assessment, an intelligence-led policing (ILP) tool, can prevent mass public shootings.

In order to gauge the potential effectiveness of this ILP tool, the authors conduct a retrospective analysis of 278 mass public shootings that occurred in the USA between 1966 and 2016. This retrospective analysis allows us to determine how successful threat assessment protocols could be in preventing mass public shootings by examining how successful this tool would have been in identifying the offenders in our data.

The results show that threat assessment has the potential to be an effective tool in the ILP arsenal to identify and prevent impending mass public shootings. However, our results also point to several obstacles for the effective implementation of this ILP tool. The underreporting of threats and using the content of threats and characteristics of threateners are problematic in correctly assigning risk. The authors make suggestions for how to overcome these obstacles.

This study makes several contributions to the intelligence-led policing and mass murder field. This is the first study to test the potential effectiveness of an intelligence-led policing tool to prevent mass public shootings. Additionally, this is one of the first studies to examine the leaks, types, context and follow-though of threats made by mass public shooters in the United States. Consequently, it provides unique information on the foreshowing behaviors of mass public shooters.

The purpose of this paper is to show how modelling can be used to provide an easy‐to‐follow, visual representation of the important characteristics and aspects of money laundering behaviours extracted from real‐world money laundering and terrorism financing typologies.

In total, 184 typologies were obtained from a number of anti‐money laundering and counter‐terrorism financing (AML/CTF) bodies to determine the common patterns and themes present in the cases involved. Financial flows, transactions and interactions between entities were extracted from each of the typologies and modelled using the Unified Modelling Language (UML) features within Microsoft Visio.

The paper demonstrates how complex transactional flows and interactions between the different entities involved in a money laundering and terrorism financing case can be shown in an easy‐to‐follow graphical representation, allowing practitioners to more easily and quickly extract the relevant information from the typology, as opposed to reading a full text‐based description. In addition, these models make it easier to discover trends and patterns present within and across Types and allow money laundering and terrorism financing typologies to be updated and published to the wider international AML/CTF community, as and when new trends and behaviours become apparent.

A set of models have been produced that can be extended every time a new scenario, typology or Type arises. These models can be held in a central repository that can be added to and updated by AML/CTF practitioners and can be referred to by practitioners to help them identify whether the case that they are dealing with fits already predefined money laundering and terrorism financing behaviours, or whether a new behaviour has been discovered. These models may also be useful for the development of money laundering and terrorism financing detection tools and the training of new analysts or practitioners. The authors believe that their work goes some way to addressing the current lack of formal methods and techniques for identifying and developing uniform procedures for describing, classifying and sharing new money laundering and terrorism financing with the international AML/CTF community, as and when they happen, in a simple but effective manner.

The phenomenon of corruption requires extra handling to achieve zero corruption. The purpose of this paper is to examine the integrated governance, risk management and compliance (GRC) implementation, the quality of internal audits and management's commitment to improving the ability to detect corruption and its impact on the company's financial performance.

This paper used primary and secondary data. Financial statement data and survey results from participants in 69 state-owned companies were analyzed using the Partial Least Square method.

There was a positive and significant effect of the integrated GRC implementation, quality of internal audit and management's commitment to increasing the organization's internal capability in detecting corruption. However, the failure to detect corruption mediates the effect of management commitment on financial performance. Besides, the organization's three internal factors could be better because their functions could be more optimal and require further improvement.

State-owned companies are continuing to be restructured, so these results can be helpful for now. However, they must update continuously with developments related to the composition and classification of state-owned companies.

Organizations can improve their ability to detect corruption in the workplace by using an early warning system such as the integrated GRC, internal audit quality and a high commitment from management.

To the author's limited knowledge, empirical research on integrated GRC implementation, internal audit quality and management commitment are still rare if they improve the detection of corruption ability. It uses the factors that cause corruption in the fraud hexagon to analyze the financial performance.

The purpose of this paper is to better understand suicidality among adolescent Mississippians.

Mississippi-specific data were obtained from an existing national health data set and utilized for two hierarchal linear regressions.

Highest risk for adolescent suicidality is for females with poor body image and a history of traumatic experiences.

This study demonstrates the need for further research into unique suicide risk factors for adolescents in Mississippi. Causality cannot be inferred due to the correlational nature of this study, and direct comparison of the findings to adolescents from other states cannot be made.

This exploratory study employed a holistic, inclusive approach toward better identifying adolescent Mississippians most at-risk for suicidality; findings lead to future, targeted research efforts for better understanding specific suicide risk factors in this population.

The purpose of this paper is to measure the size of the money laundering and terrorism financing problem, identify threats and trends, the techniques employed and the amount of funds involved to determine whether the information obtained about money laundering and terrorism financing in real‐world environments can be transferred to virtual environments such as Second Life and World of Warcraft.

Analysis of 184 Typologies obtained from a number of anti‐money laundering and counter‐terrorism financing (AML/CTF) bodies to: determine whether trends and/or patterns can be identified in the different phases of money laundering or terrorism financing, namely, the placement, layering and integration phases; and to establish whether trends and/or behaviours are ubiquitous to a particular money laundering or terrorism financing Type.

Money launderers and terrorism financers appeared to have slightly different preferences for the placement, layering and integration techniques. The more techniques that are used, the more cash can be successfully laundered or concealed. Although terrorism financers use similar channels to money launderers, they do not utilise as many of the placement, layering and integration techniques. Rather, they prefer to use a few techniques which maintain high levels of anonymity and appear innocuous. The sums of monies involved in money laundering and terrorism financing vary significantly. For example, the average maximum sum involved in money laundering cases was AUD 68.5M, as compared to AUD 4.8 for terrorism financing cases.

This paper provides some insight into the relationship between predicate offence, the predominant techniques utilised in carrying out that offence and the sums of money involved.

This study aims to attempt a gender-based ex post examination of behavioral red flags of fraud exhibited by fraud perpetrators.

Qualitative data collected from semi-structured interviews were triangulated, quantified and subjected to statistical analysis to calculate the relative risk of exhibition of a behavioral red flag of fraud by a male/female fraud perpetrator.

This study reports the percentage of fraud cases in which male and female fraud perpetrators display particular behavioral red flags. The study also enlists the behavioral red flags likely to be more frequently exhibited by female fraud perpetrators relative to male fraud perpetrators and vice-versa.

Use of the results of this study in anti-fraud training is likely to make organizational fraud more susceptible to observation.

This study is unique because it is one of the very few studies that examine employee behavior as a potential fraud signal, establish gender distinction in behavioral red flags of fraud, and assess this phenomenon in a country other than a Western country.

Artificial‐life techniques – specifically, agent‐based models and evolutionary learning algorithms – provide a potentially powerful new approach to understanding some of the fundamental processes of war. This paper introduces a simple artificial‐like “toy model” of combat called Enhanced ISAAC Neural Simulation Tool (EINSTein). EINSTein is designed to illustrate how certain aspects of land combat can be viewed as self‐organized, emergent phenomena resulting from the dynamical web of interactions among notional combatants. EINSTein's bottom‐up, synthesist approach to the modeling of combat stands in stark contrast to the more traditional top‐down, or reductionist approach taken by conventional military models, and represents a step toward developing a complex systems theoretic toolbox for identifying, exploring, and possibly exploiting self‐organized emergent collective patterns of behavior on the real battlefield. A description of the model is provided, along with examples of emergent agent patterns and behaviors.