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This paper aims to analyze the processing of complaints against investment advisors and Member firms through the Investment Industry Regulatory Organization of Canada (IIROC) enforcement system between 2009 and 2016. The paper used the misconduct funnel to show the number of complaints that are “funneled in,” and how these complaints are subsequently “funneled out” and “funneled away” at the investigation and prosecution stages of IIROC enforcement system.

The paper uses data from IIROC enforcement annual reports from 2009 to 2016. A combination of descriptive statistics and correlation matrices was used to analyze the data.

The findings indicate that while IIROC “funneled in” more complaints, a significant proportion of complaints were “funneled out” of its enforcement system and funneled “away” from the criminal justice system. Fines imposed were often not collected from individual offenders. IIROC, it seems, is ineffective in handling the more serious and systematic industry problems.

It is hard not to see the findings from this study being used by the provincial securities commissions and the federal government to support the call for a national securities regulator in Canada.

This is the first study of its kind to systematically analyze the enforcement performance of IIROC.

The purpose of this paper is to evaluate the possibility of rating the credit worthiness of a firm’s quarterly financial report using a dynamic anomaly detection method.

The study uses a data set containing financial statements from Quarter 1 – 2001 to Quarter 4 – 2016 of 937 Vietnamese listed firms. In sum, 24 fundamental financial indices are chosen as control variables. The study employs the Mahalanobis distance to measure the proximity of each data point from the centroid of the distribution to point out the extent of the anomaly.

The finding shows that the model is capable of ranking quarterly financial reports in terms of credit worthiness. The execution of the model on all observations also revealed that most financial statements of Vietnamese listed firms are trustworthy, while almost a quarter of them are highly anomalous and questionable.

The study faces several limitations, including the availability of genuine accounting data from stock exchanges, the strong assumptions of a simple statistical distribution, the restricted timeframe of financial data and the sensitivity of the thresholds for anomaly levels.

The study opens an avenue for ordinary users of financial information to process the data and question the validity of the numbers presented by listed firms. Furthermore, if fraud information is available, similar research can be conducted to examine the tendency for companies with anomalous financial reports to commit fraud.

This is the first paper of its kind that attempts to build an anomaly detection model for Vietnamese listed companies.

This paper aims to examine the enforcement practices of the Investment Dealers Association of Canada (IDA) and argue that self-regulation simply does not work in the financial sector, as the sanctions available are neither applied with sufficient severity nor are the responsibilities for enforcement adequately divided between self-regulation, provincial securities commissions and the police.

The core compliance data for the study came from the IDA’s tribunal cases that were heard between 1984 and June 2008. The theoretical approach involves the invocation of classic articles by the likes of Stigler, Posner and Becker, the essence of whose conclusions is that institutions will act in their own best interests and cannot be expected to act in the public interest.

The findings show that over the period from 1984 to 2008, the severity of the sanctions increased consistently over the period. When penalty ceilings were increased, penalties increased. When in the latter phase of the period, public members (i.e. non-members of the industry) chaired the tribunals, penalties also increased.

Researchers can use the data to write a paper which asks “Why did the IDA tribunal penalties increase so consistently with time?” Future research could canvass various possible explanations, including the one presented in this paper, to focus sustained attention on the issue of self-regulation.

This study is the first to systematically examine the enforcement performance of the IDA.

The purpose of this paper is to argue for an integrated explanation of financial fraud. Greater emphasis must be placed on the structural and situational factors that are the elements of fraud risks and fraud.

The paper is based on a review of the literature on the explanation of financial fraud. Both micro- and macro-theoretical explanations of fraud were analysed to allow for a broader picture of the types of individuals that were involved in fraud, the rules governing their conduct and the types of law they broke.

The main reason why people commit fraud is that their crime propensity interacts with the elements present in criminogenic environments. Indeed, because most of the research on structural theories of fraud focuses on general criminality, not much has been done in the area of financial fraud. More research needs to be carried out to excavate the subterranean cluster of narrative on fraud risks and fraud.

To address the future contingency of fraud risks, the paper adopted a similar position of prior accounting research on financial crimes. The structural explanation of fraudulent behaviour considers individuals’ actions to be less the result of individual deviance and more the cause of societal forces. Structural theories take into consideration the individual psychology of the offenders and position it to reflect the various realities – institutional, structural and cultural life – they are caught up in. Future research must endeavour to address these concerns.

The manuscript is among a new stream of literature that addresses the structural elements of financial fraud.

This study aims to examine the demographic factors of investors, contributing to financial victimization that occurs in Canada from June of 2008 to December of 2019.

In all 235 cases disclosing the details of financial crime victims are collected from the Industry Regulatory Organization of Canada (IIROC) enforcement platform between June of 2009 and December of 2019 for the analysis. The study used a descriptive analysis to showcase the demographic characteristics of investors who have been victims of financial crimes in Canada.

The findings indicate that these investors of age 60 and above were more likely to fall prey to various types of financial crime. The results also disclosed that retirees and investors with limited investment knowledge increase the probability of being vulnerable to the perpetrators than others.

Overall, the study helps regulators in the securities industry gain insights into demographic portraits of the more vulnerable investors. Hence, more precautionary measures could pitch into these concerns to protect specific subsets of investors from investment fraud.

Individuals who are more vulnerable to investment fraud might not be entirely comparable with the stereotypical victims that most studies portray. The research gap could cause individual investors who appear to be at lower risk to unconsciously fall prey to investment fraud. The IIROC study, detailing the demographic factors of victims, can fill the gap and improve understanding of the tendency of victims.

The purpose of this paper is to use statistical techniques to mine and analyze suspicious transactions. With the increase in money laundering activities across various sectors in some of the world’s leading democracies, the ability to detect such transactions is gaining grounds with more urgency. Regulators and practitioners have been calling for an approach that can mine the large volume of unstructured data form suspicious money laundering transactions to inform public policies.

By deducing from the results of empirical studies in the field of money laundering detection, this paper presented an overview of data mining technology for detecting suspicious transactions.

After chronicling the data mining process, the paper delves into an analysis of the statistical approaches that can be used to differentiate between legitimate and suspicious money laundering transactions. The different stages of the data mining process are carefully explained in relation to their application to anti-money laundering compliance. The results indicate that statistical data mining methodology is a very efficient and useful technique to detect suspicious transactions.

The paper is of relevance to regulators and the financial service sector. A discussion of how data can be mined to facilitate statistical analysis can be used to inform regulatory policies on the detection and prevention of money laundering activities in the financial service sector.

The paper discuss approaches that illustrate how analysts can use statistical techniques to analyze data for suspicious money laundering transactions

The purpose of this paper is to formulate and propose a fraud investigation plan that forensic accountants can use to investigate financial frauds. In particular, the paper sets out the structure and rationale of the fraud investigation plan that both forensic accountants and fraud examiners can use in their investigation of false accounting and theft charges.

The paper uses the material facts from the Polly Peck International fraud as a prototype case upon which to build an investigation plan and detail potential areas of investigation to establish evidence for a criminal trial.

The findings revealed that the case can be used to provide insights on evidence gathering techniques and test particular models of fraud detection. The concealment and conversion evidence gathering techniques provide fodder on how to gather and triangulate both direct and circumstantial evidence that can be used to avoid mistrials in courts.

The case is of interest to practitioners and forensic and fraud examination students who would like to build on their existing knowledge and obtain insights into the steps to follow to conduct an investigation and gather evidence to build a case. The paper makes specific recommendations to enhance the effectiveness and efficiency of investigations.

The paper is among one of the few to propose a fraud investigation plan designed to investigate cases involving false accounting and theft charges. More importantly, the paper uses a real case to illustrate how to examine documentation/data and how such documentation will be analysed in a trial.

This paper aims to examine the influence Jones’ Moral Intensity Model (1991) has on the decision-making process of anti-money laundering (AML) compliance officers charged with reporting suspicious money laundering transactions in Jersey.

Ten interviews were conducted to elicit participants’ views on the six dimensions of moral intensity and their influence on the compliance officers’ decision to submit a suspicious activity report (SAR) of potential money laundering.

The findings indicate that the officers’ moral intensity to submit a SAR seems to be heavily influenced by issue-specific contextual factors. Contexts (legal and legislative mandates) seem to have more of an effect on the moral intent and actions of the officers rather than directly affecting the decision to submit a report of a suspicious money laundering transaction.

The paper lays the groundwork for further work in this area and calls on researchers to develop instruments that can enhance the measurements of the dimensions of moral intensity.

The setting (AML in the financial sector) is both timely and extremely interesting to keep studying, particularly in Jersey because of its dubious sensitive particularities.

The study is the first to examine Jersey AML sector through the lens of moral intensity. In this sense, the paper poses interesting questions, namely, to explore the dynamic complexities experienced by compliance officers in Jersey to detect and report suspicious money laundering activities and the decision-making criteria of actually submitting a SAR.

This paper aims to highlight the relationship between money laundering and the patterns of behaviour evident throughout the larger structural environment of the Swiss banking sector. In particular, the paper used HSBC as a prototype case of structural ritualisation to show that the normalisation of corrupt, unethical behaviour in the banking environment has shaped and influenced the behaviour and actions of the embedded group actors.

The paper used a content analysis methodological approach of media sources to collect data. The content analysis was categorised into six core ritualised symbolic practices (RSP) categories – corruption, reputation, blame, ignorance, regret and criticism.

The findings reveal that the highly ranked RSPs involving corruption, reputation, blame, regret, ignorance and criticism influence the embedded group’s patterns of behaviour, and they formed part of the cognitive script that dictated their behaviour and actions in the Swiss banking sector.

The paper added to the calls by Swiss policymakers for amendments to Swiss bank secrecy laws to reflect the changing landscape of international banking and finance.

This is the first paper of its kind to study ritualised illegal practices related to money laundering in the Swiss banking sector.

This paper aims to reviews the literature on applying visualization techniques to detect credit card fraud (CCF) and suspicious money laundering transactions.

In surveying the literature on visual fraud detection in these two domains, this paper reviews: the current use of visualization techniques, the variations of visual analytics used and the challenges of these techniques.

The findings reveal how visual analytics is used to detect outliers in CCF detection and identify links to criminal networks in money laundering transactions. Graph methodology and unsupervised clustering analyses are the most dominant types of visual analytics used for CCF detection. In contrast, network and graph analytics are heavily used in identifying criminal relationships in money laundering transactions.

Some common challenges in using visualization techniques to identify fraudulent transactions in both domains relate to data complexity and fraudsters’ ability to evade monitoring mechanisms.