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

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Abstract

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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Data availability

The dataset will be available from Royal Roads University’s repository once the paper is accepted.

Notes

  1. See also Rule 12 of the Ontario Securities Commission Rules of Procedure. Retrieved from https://www.osc.ca/sites/default/files/2020-11/Proceedings_20191210_rules-of-procedure.pdf.

  2. In 2012, Goldman Sachs was fined $22 million for short-term stock tipping. Known as the trading huddle, it was noted that Goldman Sachs research analysts and traders would identify stocks that are likely to rise or fall because of upcoming earnings announcements and directions of the market. More interesting is that the trading huddles grew from the Global Settlement to resolve accusation on stock research by big banks to win lucrative investment business [30].

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  1. Faculty of Management, Royal Roads University, 2005 Sooke Road, Victoria, BC, V9B 5Y2, Canada

    Mark E. Lokanan

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Mark Lokanan is an Associate Professor in the Faculty of Management at Royal Roads University. He is a graduate from Simon Fraser University, Canada and is an expert in fraud, forensic and investigative accounting.

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Lokanan, M.E. Incorporating machine learning in dispute resolution and settlement process for financial fraud. J Comput Soc Sc 6, 515–539 (2023). https://doi.org/10.1007/s42001-023-00202-1

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