Preempting fraud: a financial distress prediction perspective on combating financial crime

Abstract

Purpose The prevention of fraudulent activities, particularly within a financial context, is of paramount significance in all spheres, as it not only impacts the sustainability of corporate entities but also has the potential to have a broader economy-wide impact. This paper aims to focus on dual implications associated with financial distress, the first being associated with the temptation to launder funds due to financial distress, and the second being the potential for illicit activities, such as fraud, money laundering or terror financing, to give rise to financial distress. Design/methodology/approach The paper examines the literature on financial distress and uses theories of financial crime to establish a link between financial distress and financial crime. Findings In recent years, there has been a surge in corporate financial distress, particularly in the aftermath of concurrent crises such as the COVID-19 pandemic and the Russia–Ukraine war. Through a comprehensive examination of literature pertaining to financial distress and financial crime, this study identifies a proclivity towards fraudulent conduct arising from instances of financial distress. Moreover, the engagement in such illicit activities subsequently exacerbates the financial distress. An analysis of the relationship between financial crime and financial distress reveals the existence of a vicious cycle between the two. Originality/value The results of this study have the potential to advance understanding of the relationship between financial distress and financial crime, which has been previously underexplored.

Employing Artificial Neural Networks and Multiple Discriminant Analysis to Evaluate the Impact of the COVID-19 Pandemic on the Financial Status of Jordanian Companies

“…He classified a company as healthy if its corresponding Z-score was more than 2.99, distressed if its Z-score was less than 1.8, and inconclusive if its Z-score was between 1.8 and 2.99. Altman's model was superior to that of Beaver's, as the short-term accuracy surpassed 90%; however, this accuracy declines for long-term predictions (Halteh, 2019). MDA has been extensively used in many FDP studies, and despite being dated, it is still used in research, including Altman et al (2017), Chung et al (2008), Grice and Ingram (2001), Halteh (2019), Lee and Choi (2013).…”

“…Altman's model was superior to that of Beaver's, as the short-term accuracy surpassed 90%; however, this accuracy declines for long-term predictions (Halteh, 2019). MDA has been extensively used in many FDP studies, and despite being dated, it is still used in research, including Altman et al (2017), Chung et al (2008), Grice and Ingram (2001), Halteh (2019), Lee and Choi (2013).…”

“…EVOLUTION OF FINANCIAL DISTRESS PREDICTORS Innumerable models have been developed over the years to deal with Financial Distress Prediction (FDP) using various techniques. They vary in methodologies; however, they generally aim to analyze variables and/or identify the most accurate predictions possible (Halteh, 2019).…”

“…Its accuracy, however, declined significantly for long-term predictions. Beaver's model was also criticized for the used ratios since they may result in contradictory predictions (Halteh, 2019). Altman (1968) later founded the first multivariate statistical approach concerning FDP using Multiple Discriminant Analysis (MDA).…”

“…Altman (1968) later founded the first multivariate statistical approach concerning FDP using Multiple Discriminant Analysis (MDA). Altman's model was developed to tackle the main challenge of Beaver's (1966) model, i.e., different ratios could result in contradictory predictions (Halteh, 2019). Altman formulated a single weighted score (Z) for each company based on five financial ratios.…”