Dr. Mark Lokanan is a highly trained and skilled certified fraud examiner with expertise in financial forensics, forensic accounting, fraud examination, securities fraud, forensic data analysis, fraudulent financial statements, Benford Law for accounting fraud detection, anti-money laundering legislation and investigation, and forensic and investigative interviewing. Lokanan is one of the few scholars and practitioners in Canada with the requisite education, experience, expertise and publications in financial criminology, law, accounting and finance that qualify him as an expert in financial crimes and investigations.
Lokanan has worked and taught in the United States., the United Kingdom and Canada at the University of Guelph, San Diego State University, Simon Fraser University and in the Newcastle Business School. He has published in the areas of corporate accounting and securities fraud and regulatory compliance. Before working in academia, he worked for the Ministry of the Attorney General in Ontario as a fraud investigator, managing numerous projects and investigations from inception to completion, and devising and presenting reports and feedback to keep clients informed.
Lokanan holds a PhD in Accounting for Financial Crimes from Simon Fraser University (2012), a Master of Science in Forensic Accounting (2017) from Portsmouth University, a Master of Science in Financial and Organized Crime from San Diego University (2004), a Master of Arts in Sociology and Data Analysis from University of Guelph (2001), an undergraduate degree in Sociology and Law from York University (1998), an Advance Specialty Certificate in Securities Fraud Analysis (with Forensic Accounting) from BCIT (2012) and a Certificate in Advanced Accounting from McMaster University (2017). He also is a CFE and is reading for his CPA.
(Journal of Money Laundering Control, 2020) Lokanan, Mark; Nasimi, Noor
The purpose of this paper is to identify the anti-money laundering (AML) policies and procedures applied by the banks operating in Bahrain and assess the effectiveness of these policies.
Data for the study came from semi-structured interviews with compliance officers in Bahrain’s banking sector. A total of 22 interviews were conducted with Bahraini money laundering reporting officers and bankers.
The findings indicate that the banks in Bahrain comply with international AML procedures in combating money laundering. Despite Bahrain being ranked as having strong compliance policies and AML procedures among the Gulf Cooperation Council region, there are still issues with regulatory technology that needs to be addressed.
While there has been a positive impact of AML procedures, there are always more procedures that can be taken into consideration by banks in Bahrain to have more robust mechanisms to mitigate against the threat of money laundering.
To the best of authors’ knowledge, this paper is among the first to conduct an informed study of the effectiveness of compliance in the Bahrain’s financial sector. It can be used as a foundation paper for more mix-research on money laundering threats facing Bahrain’s banks.
(Journal of Financial Crime, 2020) Lokanan, Mark; Aujla, Indy
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 elements of fraud risks and fraud.
The paper is base on a review of the literature on the explanation of financial fraud. Both micro and macro theoretical explanations of fraud were analyzed to allow for a broader picture of the types of individuals that were involved in fraud, the rules governing their conduct, the types of law the broke.
The main reason why people commit fraud is that their crime propensity interacts with elements present in criminogenic environments. Indeed, since 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 that they are caught up in. Future research must endeavour to address these concerns.
The manuscript is among a new stream of literature that address the structural elements of financial fraud.
(Journal of Money Laundering Control, 2019) Lokanan, Mark
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 finance literature is inundated with research that empirically examines financial institutions and the broader capital markets in which they operate. Yet, despite the amount of assets that the investment management industry manages, and the impact that it has on the financial ecosystem as a whole, there has been very little policy oriented research in this area. Chains of Finance: How Investment Management is Shaped comes at an opportune time and attempts to address this gap by exploring the nuances and the inner workings of the investment management industry. The central argument of the book is that investment management should be understood as a chain that links savers (individuals, companies, government) of capital with corporations and governments that sell financial instruments.
The vast amount of data and the increasing development in technology in recent years
have changed the way in which many industries operate and compete with each other.
Millions of bytes, commonly referred to as big data, provide valuable insights for companies
to make informed business decisions. Companies that conduct business in the financial
service sector employ big data to inform their investment practices and make strategic
decisions. The increased use and complexity of big data poses a challenge to users of financial
information when analyzing financial statements. This is especially applicable to users who
possess fewer financial resources and have inferior knowledge to conduct in-depth analysis of
financial statements (Lokanan, 2014). Companies that wants to present a rosy picture of their
financial position, may exploits these users’ deficiencies through deliberate misleading and
omission of financial data in their annual reports (Rezaee, 2002; Albrecht et al., 2006; 2014;
Robinson and Lokanan, 2017).
Vietnamese companies were selected because of the high incidence of financial
reports manipulation (Tran, 2013). The number of listed companies reported by Hanoi Stock
Exchange (HNX) and Ho Chi Minh City Stock Exchange (HOSE) from 2000, when
Vietnam’s security market was in its infancy stage, to 2016 has steadily increased. In 2016,
there were more than 1,000 listed companies on these exchanges. Growth and structural
development in Vietnam’s financial markets comes with intense competition in the
marketplace and the possibility of financial statement manipulation of listed companies on the
HNX and HOSE (Tran, 2013). Indeed, there has been an increasing number of failed
companies and fraudulent reporting in Vietnamese markets in the last few years. To be
specific, 6,608 companies in the first seven months of 2017, 12,478 companies in 2016 and
9,467 companies in 2015 (Agency of Business Registration, 2017). The volume and intensity
of fraudulent reporting have made it difficult for humans to process and analyze anomalous
transactions (Grace et al., 2017). Even some traditional statistic regression techniques cannot
be applied due to the complexity of data set (Fan and Li, 2006). Thus, we need embedded
analytical models with highly-automated operating structures to deal with the large volume,
variety of features, and velocity of the data that the human brain cannot handle.
This is where big data techniques come into play. Big data have brought with it novel
techniques, such as machine learning and algorithms, that allow users to conduct in-depth
analysis and gain deeper understanding of anomalies in financial statements. The analysis of
big data using machine learning techniques can assist users of financial statements to detect
unusual patterns and transactions in companies’ financials. Big data are massive and can be
used by both users and companies to provide data-centric and data-driven insights on financial
This study is an attempt to use machine learning algorithms to detect anomalies in
financial statements in Vietnamese listed firms. As mentioned, the only resources available to
ordinary investors are quarterly reports, which may contain misleading financial information.
It is not enough just to look at the original state of such financial reports. Much research has
proved efficiency by analyzing financial ratios calculated from the values in companies’
reports (see Altman, 1968; Kotsiantis et al., 2006; Pustylnick, 2011). Therefore, we
approached the problem by using financial ratios as a series of variables, also known as
features. An important point in this paper is that the values of financial ratios are assumed to
follow a multivariate distribution, which means each ratio varies around one specific mean
value. This assumption will allow us to point out anomalous data by measuring whether the
distance of each datum to the ‘centroid’ (which will be explained in Research Methodology)
exceeds a certain threshold. Additionally, we will take the concept of distance further by
regarding it as the degree or extent of the anomaly. This extension of understanding enables
us to rank the credit worthiness of each company in each quarter: the more anomalous a
datum, the less credit-worthy it is. Therefore, the central question of this paper is as follows:
is it possible to rate the creditworthiness of a firm’s financial quarter using an anomaly