Investigating aggravating &amp; mitigating factors considered by IIROC in penalty imposition

Lokanan, Mark; Masannagari, Navya

doi:10.1080/12294659.2021.1966202

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…As can be seen in Table 5, the F1score for all of the models ranges between 98% and 99%, which signi es that the models did a very good job predicting settlement cases. These ndings corroborate previous studies of IIROC's tendency to settle cases through settlement hearings (Lokanan & Masannagari, 2021;Wiesenfeld & Dove, 2011). Figure 4 presents the confusion matrix results for the classi ers used in this paper.…”

Section: Classi Cation Resultssupporting

confidence: 91%

“…No speci c weight is given to any single factor, and in most cases, more than one mitigating and aggravating factor is involved. As can be seen in Table 2, an average of three mitigating and aggravating factors each were cited per case (see also Lokanan, 2014;Lokanan & Masannagari, 2021). Most importantly is that at least one aggravating factor was present in a case but was not considered to merit a harsher sentence.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

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Incorporating Machine Learning in Dispute Resolution and Settlement Process for Financial Fraud

Mark¹

2022

Preprint

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This paper aims to classify disciplinary hearings into two types (settlement and contested). The objective is to employ binary machine learning classifier algorithms to predict the hearing outcomes given a set of features representing the victims, offenders, and enforcement. Data for this project came from the Investment Industry Regulatory Industry of Canada’s (IIROC) tribunal hearing. The data comprises cases that made their way through the IIROC ethics enforcement system and were decided or negotiated by a hearing panel. The findings from the machine learning classifiers confirm that decisions in these cases are not proportionate to the harm committed and that the presence of aggravating factors does not result in harsher sentences.

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Section: Classi Cation Resultssupporting

confidence: 91%

Section: Descriptive Statisticsmentioning

confidence: 99%

Incorporating Machine Learning in Dispute Resolution and Settlement Process for Financial Fraud

Mark¹

2022

Preprint

View full text Add to dashboard Cite

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Incorporating machine learning in dispute resolution and settlement process for financial fraud

Lokanan

2023

J Comput Soc Sc

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Investment Strategies in the North American Market in the Post-Epidemic Era

Kong¹

2023

HBEM

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This paper delves into the intricacies of investing in the North American market, particularly in the aftermath of the Covid-19 pandemic. While the pandemic has caused significant disruption and uncertainty across the global economy, it has also created new opportunities for investors in North America. The paper highlights sectors such as healthcare biotechnology, digital services, and e-commerce as offering significant growth potential due to increased adoption of digital technologies and heightened awareness of ESG factors. However, it also acknowledges the risks posed by geopolitical tensions, trade disputes, and industry chain issues. To succeed in the North American market, investors must adopt successful investment strategies that take into account these risks and opportunities. This may involve staying informed about political influences and economic conditions, building partnerships with local businesses and industry associations, and incorporating an assessment of social responsibility into investment decisions. Ultimately, the key to achieving sustainable growth in the North American market is to have a thorough understanding of market trends and challenges and to implement effective investment strategies that mitigate risks and capitalize on opportunities. By doing so, investors can achieve their investment objectives and position themselves for long-term success in a rapidly evolving economic landscape.

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Investigating aggravating & mitigating factors considered by IIROC in penalty imposition

Cited by 3 publications

References 20 publications

Incorporating Machine Learning in Dispute Resolution and Settlement Process for Financial Fraud

Incorporating Machine Learning in Dispute Resolution and Settlement Process for Financial Fraud

Incorporating machine learning in dispute resolution and settlement process for financial fraud

Investment Strategies in the North American Market in the Post-Epidemic Era

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