Contemporary Financial Crime

Jung, Jeyong; Lee, Julak

doi:10.5296/jpag.v7i2.11219

Cited by 13 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Financial crime erodes public trust and affects the stability of the markets [ 40 ] (p. 5). The behavior and action that constitutes financial crime has always been difficult to define [ 41 , 42 , 43 ]. The uncertainty in the definition of financial crime raises questions about the demographic profile of investors who are more vulnerable to financial exploitation.…”

Section: Theory and Literature Reviewmentioning

confidence: 99%

Predicting Fraud Victimization Using Classical Machine Learning

Lokanan

Liu

2021

Entropy

View full text Add to dashboard Cite

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

show abstract

Section: Theory and Literature Reviewmentioning

confidence: 99%

Predicting Fraud Victimization Using Classical Machine Learning

Lokanan

Liu

2021

Entropy

View full text Add to dashboard Cite

show abstract

“…The concept of financial crime is dynamic and takes different notion as social contents, contexts and technical circumstances surrounding financial transaction advances (Jung & Lee, 2017). Based on the utility concept, financial crime is a departure from what the society considers as right measures in production, distribution and consumption of goods and services (Moyosore, 2015) for the satisfaction of needs (Njoku, Nwosu, & Callistus, 2017).…”

Section: Concept Of Financial Crimementioning

confidence: 99%

“…Owolabi (2007) asserts that financial crime is traceable to several dimensions of corruption. Gottschalk (2010) in Jung and Lee (2017) categorized financial crime as a key component of corruption comprising fraud, theft and manipulation. Lipset and Lenz (2000) in Fagbadebo (2007) assert that corruption is an effort to secure treasure through illegitimate means for private gain at public expense whereas Nkom (1982) in Moyosore (2015) posits that corruption is the perversion of public affairs for private advantage.…”

Section: Concept Of Financial Crimementioning

confidence: 99%

Financial Crime and Local Government Productivity in Rivers State

Briggs¹,

Wege²,

Nkanbia-Davies³

2020

Journal of Contemporary Research in Business, Economics and Fin

View full text Add to dashboard Cite

The study investigated the effect of financial crime on local government productivity in Rivers State. The study specifically ascertained the effect of financial crime on effective service delivery and human resource administration in Rivers State local government councils. The predominant factors that induced financial crime in Rivers State local government councils were also identified. Descriptive survey design was employed with purposive sampling drawn from eight local government councils. A total of 220 questionnaires were distributed to the eight local government councils and of the 220, 208 representing 95% were returned and used. The data were analyzed using simple percentages for descriptive analysis and Chi-square (χ2) for hypotheses testing. The findings revealed that financial crime significantly and negatively influenced effective service delivery and human resource administration in Rivers State local government councils. It was further revealed that greed, opportunity and need induced financial crime in Rivers State local government councils. To stimulate employee commitment and effective service delivery in Rivers State local government councils, it was recommended that local government employees should be properly compensated and the institutional and juridical mechanisms (whether on exclusive, concurrent and residual lists) should be strengthened for accountability and deterrence of financial crime.

show abstract

“…Financial crime from this perspective comprised the elements of deception, intention, resulting pecuniary losses, possible concealment, breach of trust and the possible appearance of outward respectability. Enforcement agencies in USA and UK differ in their focus (Jung and Lee, 2017) mainly because of different priorities and resource capacity.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, the amount being seized and frozen by legal institutions was estimated to be less than 1 per cent of illicit financial flows (UNODC, 2011). Differences among countries in terms of their national jurisdictions, varying implementation of international conventions and varying levels of expertise of their investigative and prosecutorial authorities and other factors accounted for the difficulties and obstacles faced when tracing criminals and their ill-gotten gains (Jung and Lee, 2017;Ryder et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence: accelerator or panacea for financial crime?

Yeoh

2019

JFC

View full text Add to dashboard Cite

PurposeThis purpose of this viewpoint is to address the intended good and unintended bad impacts of artificial intelligence (AI) applications in financial crime.Design/methodology/approachThe paper relied primarily on secondary data resources, business cases and relevant laws and regulations, and it used a legal-economics perspective.FindingsCurrent AI systems could function as antidotes or accelerator of financial crime, in particular cybercrime. Research suggests criminal law could be applied via three approaches to curb these cybercrimes. However, others considered this to be an inappropriate mechanism to hold AI agents accountable, as present AI systems were not deemed capable of making ethically informed choices. Instead, administrative sanctions would be considered more appropriate for now. While keeping vigilance against AI malicious acts, regulatory authorities in the USA and the UK have opted largely for the innovation-friendly, market-oriented, permissionless approach over the state-interventionist stance so as to maintain their global competitive edge in this domain.Originality/valueThe paper reinforced the growing arguments that AI applications should be deployed more as panacea for financial crimes rather than being abused as crime accelerators. There equally though is the need for both public and private sectors to be mindful of the unintended negative, harmful consequences to society, especially those connected to cybercrime. This implied the further need to beef up attention and resources to help mitigate these risks.

show abstract

Contemporary Financial Crime

Cited by 13 publications

References 6 publications

Predicting Fraud Victimization Using Classical Machine Learning

Predicting Fraud Victimization Using Classical Machine Learning

Financial Crime and Local Government Productivity in Rivers State

Artificial intelligence: accelerator or panacea for financial crime?

Contact Info

Product

Resources

About